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Use of the object proposal method as a preprocessing step for object detection of vision sensors has improved computational efficiency in recent years. Good object proposal methods require high object detection recall, low computational cost, good localization accuracy, and repeatability. However, existing methods cannot always achieve a good balance of performance. To solve this problem, we propose a fast and high-performance object proposal algorithm. First, we propose a construction method to enhance frequency features that are combined with a linear classifier to learn and generate a set of proposal boxes. Second, we propose a strategy of binarizing frequency features and classifiers to accelerate the calculation. Last, we propose a merging strategy to improve the localization quality of the proposal boxes. Empirically, for the VOC2007 and MSCOCO2017 datasets using the intersection over union (IOU) threshold of 0.5 and 104 proposals, our method achieves 99.3% object detection recall, 81.1% mean average best overlap, and 80% mean repeatability with an average time of 0.0014 seconds per image. The average time is three times faster than the current fastest method, and the mean repeatability is 11% higher than that of the region proposal network (RPN) method. We applied our method to the target detection of autonomous vehicles, and in the experiment with the Oxford RobotCar dataset, we achieved 95.6% detection precision and 91.2% detection recall. This work could provide a new way to improve real-time performance and detection accuracy in the object detection of visual sensors. [ABSTRACT FROM AUTHOR]

Vision-based detection of fingertips is useful for freehand Human-Computer Interaction (HCI)—especially in virtual, augmented, and mixed reality—to have a seamless experience. The estimation of fingertips position in an RGB image involves overcoming various challenges like occlusion, appearance ambiguities, etc. The general approach relies on a two-stage pipeline involving hand location and detection of fingertips for a single hand. This paper presents an effective single-stage Convolutional Neural Network (CNN) for the detection of fingertips of both hands. We use a set of reference points, referred to as pose particles, and train a CNN model end-to-end to find the N-nearest particles in the proximity of each fingertip. Moreover, the same CNN model is used to compute the position vector’s components with reference to these N-nearest neighbors. Finally, a fingertip position is estimated by computing the centroid of all the points given by these position vectors. With the proposed approach, it is possible to estimate the fingertips position for single or double hands. Moreover, there is no requirement for prior hand localization. We demonstrated the feasibility and effectiveness of the proposed methodology by performing experiments on three different datasets. [ABSTRACT FROM AUTHOR]

Accurate and effective localization of reliability-critical gates (RCGs) is one of the important prerequisites for low-cost circuit fault tolerance in the early stages of circuit design. This article introduces an accurate and effective approach for localizing RCGs in combinational logic blocks through a benchmarking technique. In the proposed approach, uniform non-Bernoulli sequences are used to produce a set of input vectors for driving circuits. A full-period linear congruential algorithm is employed to generate a sequence that provides the sampled order for the RCGs to be analyzed. This ensures that each gate in the circuit is treated as fairly as possible. To accelerate the localization process, an input-vector-based pruning technique combined with a counting method is also introduced to identify the specified number of RCGs. Then, the criticality of gate reliability for each RCG is measured through benchmarking. A clustering algorithm carries out the convergence checking for the proposed approach. The performance of the proposed approach was evaluated in terms of accuracy, stability, and time-space overhead by various simulations on 74-series circuits and ISCAS-85 benchmark circuits. The results show that its accuracy is close to that of the Monte Carlo model and its stability is better than that of other approximate methods. Moreover, compared with approximate methods, the time overhead of our approach is advantageous in the presence of similar memory overheads. [ABSTRACT FROM AUTHOR]

We propose a deep learning-based approach to localizing a small moving object with a single millimeter-wave frequency-modulated continuous-wave (FMCW) radar. The main challenge that foils conventional localization techniques, such as 3-D fast Fourier transform (3-D-FFT), Pisarenko method, multiple signal classification (MUSIC), estimation of signal parameters via rotational invariance technique (ESPRIT), Capon’s method, and Burg’s method, is the low signal-to-noise ratio of the reflected signal from millimeter-sized objects. Our key idea is to combine useful but noisy features from classical transforms [e.g., fast Fourier transform (FFT)] with neural networks that can refine and interpret those features into range and angle estimates by training on a large dataset of examples. Importantly, our networks were designed to be translation-equivariant, which enables accurate predictions of unseen object locations and improves the range and azimuth root mean square error (RMSE) scores by 34%–46% and 41%–60%, respectively, over state-of-the-art approaches. This pilot study establishes a new baseline for small-object tracking using FMCW and can enable tracking of small animals, such as ants inside the colony for behavior studies. Our first FMCW-small-object dataset and the source code are publicly available on https://github.com/shikuzen/RA-CNN. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) has become an increasing and challenging problem in microwave interferometric radiometry (MIR). Accurate localization of RFI sources is helpful to provide location information for switching off unauthorized transmitters causing RFI and mitigating the impact of these RFI sources. In this article, we propose a new RFI localization method based on reweighted nuclear norm minimization (RNNM). This method exploits the low-rank property of augmented covariance matrix (ACM) collecting visibility samples in MIR and introduces a singular value weighting strategy to consider different contributions of ACM components. First, ACM is constructed from the original covariance matrix of sparse array, which increases the degree of freedom (DOF) for array processing and hence improves the angular resolution performance. Second, we present a fixed point iteration (FPI)-based RNNM Algorithm, named FRA, to achieve low-rank approximation of ACM involving contribution degrees of ACM components. In this way, the ACM components corresponding to RFI signals are retained well and ones corresponding to background noises are suppressed. Third, we use a subspace-based direction-of-arrival (DOA) estimation approach, i.e., MUSIC algorithm, on the weighted completed ACM (WCACM) (obtained by FRA in the second stage) to locate the potential RFI sources. Retrieved results using synthetic data and real soil moisture and ocean salinity (SMOS) satellite data demonstrate that the proposed RNNM-based method not only has the superiority on improved detection performance, especially for identifying weak sources, but also shows better or competitive localization accuracy and angular resolution, compared with the existing commonly used RFI localization methods in MIR. [ABSTRACT FROM AUTHOR]

Goal: To develop a low-cost magnetic resonance imaging (MRI)-free transcranial focused ultrasound (FUS) system for microbubble-mediated therapy. Methods: A 128-element 11 MHz array for skull localization was integrated within a 256-module multi-frequency (306/612/1224 kHz) dual-mode phased array. The system's transcranial transmit and receive performance was evaluated with ex-vivo human skullcaps using phase aberration corrections calculated from computed tomography (CT)-based simulations via ultrasound-based (USCT) and landmark-based (LMCT) registrations, and a gold-standard fixed source emitter (FSE)-based method. Results: Displacement and rotation registration errors of 1.4 ± 0.4 mm and 2.1 ± 0.2 $^\circ $ were obtained using USCT, resulting in sub-millimeter transmit targeting errors driven at 306 kHz (0.9 ± 0.2 mm) and 612 kHz (0.9 ± 0.3 mm), and source localization errors of 1.0 ± 0.3 mm and 0.6 ± 0.2 mm at receive frequencies of 306 kHz and 612 kHz, respectively (mean ± SD). Similar errors were obtained using LMCT and no significant differences between these two approaches were found on either transmit (p = 0.64/0.99) or receive (p = 0.45/0.36) at 306 kHz/612kHz. During volumetric multi-point exposures, approximately 70% and 60% of the transmit frames in which microbubble activity was detected via FSE were recovered using USCT when imaging at the second-harmonic and half-harmonic, respectively, compared to 60% and 69% using LMCT. Conclusion: This low-cost ultrasound-guided transcranial FUS system affords USCT skull registration with accuracy comparable to LMCT methods. Significance: Such systems have great potential to advance the adoption of microbubble-mediated FUS brain therapy by improving access to the technology. [ABSTRACT FROM AUTHOR]

Objective: The ocular vascular system plays an important role in preserving the visual function. Alterations in either anatomy or hemodynamics of the eye may have adverse effects on vision. Thus, an imaging approach that can monitor alterations of ocular blood flow of the deep eye vasculature ranging from capillary-level vessels to large supporting vessels would be advantageous for detection of early stage retinal and optic nerve diseases. Methods: We propose a super-resolution ultrasound localization microscopy (ULM) technique that can assess both the microvessel and flow velocity of the deep eye with high resolution. Ultrafast plane wave imaging was acquired using an L22-14v linear array on a high frequency Verasonics Vantage system. A robust microbubble localization and tracking technique was applied to reconstruct ULM images. The experiment was first performed on pre-designed flow phantoms in vitro and then tested on a New Zealand white rabbit eye in vivo calibrated to various intraocular pressures (IOP) – 10 mmHg, 30 mmHg and 50 mmHg. Results: We demonstrated that retinal/choroidal vessels, central retinal artery, posterior ciliary artery, and vortex vein were all visible at high resolution. In addition, reduction of vascular density and flow velocity were observed with elevated IOPs. Conclusion: These results indicate that super-resolution ULM is able to image the deep ocular tissue while maintaining high resolution that is comparable with optical coherence tomography angiography. Significance: Capability to detect subtle changes of blood flow may be clinically important in detecting and monitoring eye diseases such as glaucoma. [ABSTRACT FROM AUTHOR]

Accurate state estimation is essential for correct supervision of power grids. With the existence of cyber-attacks, state estimation may become inaccurate, which can eventually lead to wrong supervisory decision making. To detect cyber-attacks in power grids equipped with phasor measurement units (PMUs), a new intrusion detection system based on clustering approach, called PMU Intrusion Detection System (PMUIDS), is proposed in this article. After solving the optimal PMU placement in N-1 contingency, several static state estimations are obtained by removing the measurements of one PMU in each time. The resulting state vectors are clustered in two steps: First, subtractive clustering is employed to obtain the number of clusters, which determines the number of integrity attacks; and second, fuzzy C-means clustering assigns the state vectors to the corresponding clusters, which determines the attacked PMUs. In addition, two theorems are proved, which indicate that the attacker cannot coordinate successful stealth attacks in cases that by removing attacked PMUs from state estimation, the power system still remains full observable. Furthermore, in the case of possible stealth attacks, the attacker cannot falsify the estimation of any arbitrary state variable. The hardware-in-the-loop results on a sample power system show that the proposed approach can detect integrity attacks, determine the number of attacks, obtain the correct state vector, and localize the attacks, even in case of multiple simultaneous attacks. [ABSTRACT FROM AUTHOR]

To more effectively exploit the omnipresent public non-cooperative illuminators of opportunity (IOs) to improve the low probability of intercept (LPI) performance, a power allocation strategy is developed for target localization in distributed hybrid non-coherent active-passive radar networks. The core of the strategy is to optimize each active radar power allocation to minimize the total intercepted power for the hostile intercept receiver in view of power constraints, while achieving the predetermined collaborative target localization accuracy with the assistance of public non-cooperative IOs. Considering the timing uncertainty of public non-cooperative IOs, the localization performance is determined by joint Cráme-Rao lower bound (CRLB) of the target position and the time-of-arrival (TOA) of the direct-path in a distributed passive radar network. Then, the Bayesian CRLB (BCRLB) is derived to evaluate collaborative target localization accuracy, adopted as the optimization criterion for the power allocation strategy. Furthermore, the optimization problem of the power allocation strategy is strictly proved to be a convex optimization, which implies that the global optimal lower bound can be easily obtained. By introducing a semi-definite matrix, the optimization problem is subsequently converted to semi-definite programming (SDP), resolved by the exiting classical algorithm directly. Finally, the simulation results verify the theoretical analysis and the LPI performance of the proposed strategy which outperforms existing strategies. [ABSTRACT FROM AUTHOR]

Other than the relative position-based and bearing-based localization with aligned coordinate frames on all sensor nodes, the existing 3D network localization algorithms using distance/bearing/angle measurements without aligned coordinate frames usually require each node to have at least four neighbors, which poses large communication burden for the network system. To reduce the communication burden in each iteration, this paper studies the 3D network localization problem where each node is allowed to have angle measurements with respect to only three neighbors. Firstly, we use a tetrahedral angularity to describe the network consisting of a set of nodes and tetrahedra among the nodes. Given that at least one node in each tetrahedron has the knowledge of the direction of the global $Z$ -axis, the geometric constraint of each tetrahedron is described by an angle-induced linear constraint whose coefficient matrices are only related to the interior angles among the four nodes in the tetrahedron and the angles between the global Z-axis and the inter-node edges. Secondly, both algebraic and topological localizability conditions are derived based on the coefficient matrices of the tetrahedra’s angle-induced linear constraints. Moreover, a distributed method is also presented to check the network localizability. Lastly, distributed localization laws are designed under the cases of continuous communication, aperiodic communication, and aperiodic communication on jointly localizable angularities, respectively. Simulation examples are provided to validate the effectiveness and advantages of the proposed localization laws. [ABSTRACT FROM AUTHOR]

Ultra Wide Band (UWB) has been emerged as a technology to provide reliable, accurate, and energy-efficient indoor navigation/localization systems. There are, however, several key challenges ahead for its efficient implementation including complexity of the identification/mitigation of Non Line of Sight (NLoS) links, and the limited battery life of UWB beacons, which is especially problematic in practical circumstances with certain beacons located in strategic positions. To address these challenges, we introduce an efficient node selection framework to enhance the location accuracy, without using complex NLoS mitigation methods, while maintaining a balance between the remaining battery life of UWB beacons. Referred to as the Deep Q-Learning Energy-optimized LoS/NLoS (DQLEL) UWB node selection framework, the mobile user is autonomously trained to determine the optimal set of UWB beacons to be localized based on the 2-D Time Difference of Arrival (TDoA) framework. The effectiveness of the proposed DQLEL framework is evaluated in terms of the link condition, the deviation of the remaining battery life of UWB beacons, location error, and cumulative rewards. Based on the simulation results, the proposed DQLEL framework significantly outperformed its counterparts across the aforementioned aspects. [ABSTRACT FROM AUTHOR]

Nowadays, Wi-Fi or Bluetooth based indoor localization algorithms are designed to improve the effects from variation of receive signal strength (RSS) at different locations. However, the positioning accuracy of targeting device is highly related to the indoor topological layout, e.g., the blocking effects from partitioning walls. In this paper we propose a novel skeleton-based positioning and reference point deployment for hybrid wireless localization (S-PDH) that utilizes the spatial skeleton of indoor layout to improve location estimation accuracy. Firstly, we adopt an automatic map analysis algorithm to generate appropriate reference points (RPs) from the spatial skeleton of indoor layout. Secondly, we propose a novel clustering algorithm to consider both spatial layout and RSS information in order to enhance the accuracy of RP matching. Those outlier RPs resulting poor performance will be replaced by appropriate deployment of BLE APs. Finally, the cluster-based positioning algorithm is presented to estimate the target’s location. Compared to existing methods, both the simulation and experimental results show that the proposed S-PDH system can achieve better positioning accuracy along with the benefits of automatically generating the spatial layout information for wireless indoor localization. [ABSTRACT FROM AUTHOR]

The ability to determine the location of a sensor node in a wireless sensor network is essential in many applications. We propose a received signal strength (RSS) based cooperative localization technique. The target nodes, whose locations are unknown during the deployment phase, communicate with the anchor nodes (whose locations are known) and the neighboring target nodes to localize themselves. In the proposed technique the target nodes behave as pseudo anchor nodes after they have localized themselves. It is observed that in such cooperative techniques the localization error propagates from one iteration to the next. We mitigate this error propagation by eliminating the network edge nodes from updating their location information after the first iteration. We propose two distributed edge node detection techniques: quadrant method (Quad) and geometric dilution of precision (GDOP) based method. Extensive simulations have been carried out, on uniform anchor and non-uniform network topologies, to analyze the performance of the proposed edge detection techniques and the iterative localization algorithm. The proposed localization algorithm is compared with state of the art localization techniques using simulations as well as experiments, and is shown to achieve improved localization accuracy. The Cramer-Rao lower bound (CRLB) is also derived and used as a performance benchmark. [ABSTRACT FROM AUTHOR]

We consider the problem of spatial signal design for multipath-assisted mmWave positioning under limited prior knowledge on the user’s location and clock bias. We propose an optimal robust design and, based on the low-dimensional precoder structure under perfect prior knowledge, a codebook-based heuristic design with optimized beam power allocation. Through numerical results, we characterize different position-error-bound (PEB) regimes with respect to clock bias uncertainty and show that the proposed low-complexity codebook-based designs outperform the conventional directional beam codebook and achieve near-optimal PEB performance for both analog and digital architectures. [ABSTRACT FROM AUTHOR]

With the increasing demand for location-based services in indoor environments, research on indoor localization technology has become an urgent need. In complex indoor environments, strong multipath effects can greatly interfere with the propagation of wireless signals, which may seriously degrade localization accuracy. To solve the problem, this paper proposes an indoor localization system DuLoc based on fine-grained subcarriers and a dual-channel convolutional neural network (CNN). In this system, abnormal sampling points are removed by the Isolation Forest method to deal with environmental noise. In order to select more stable and effective signals, the number of clusters of each subcarrier is analyzed by the Mean Shift clustering method, and the fingerprint database is divided into two sub-databases according to the threshold. The dual-channel CNN model is constructed to optimize the classification results with the data characteristics. Those two types of subcarrier data construct the input feature images to train the dual-channel CNN model. Then the final location coordinate is calculated by the probability weighted centroid method. Experimental results are presented to demonstrate that DuLoc has higher localization accuracy in complex indoor environments compared with the other existing methods. [ABSTRACT FROM AUTHOR]

Localization is a critical task in wireless sensor networks (WSNs). However, accurate localization is difficult when there is noise from external sources and faulty nodes, especially when the transmit power is unknown. Signal strength-based localization methods are popular due to their low complexity and simple implementation. In this paper, robust localization based on the received signal strength difference (RSSD) is considered with unknown transmit power and Gaussian mixture noise in the presence of faulty nodes. A robust fault-tolerant localization (RFLT) technique is proposed by reformulating the original localization problem using a generalized trust-region subproblem (GTRS) framework. The resulting problem is non-convex so it is divided into two subproblems using a regularization item (RI) with a block-update surrogate function (BUSF). The active set method (ASM) is used to obtain an initial solution. The corresponding Cramer–Rao lower bound (CRLB) for Gaussian mixture noise is derived as a benchmark. Simulation results are presented which show that the performance of the proposed method is superior to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This letter deals with the problem of simultaneously locating the single source and refining erroneous sensor positions in a distributed network with the use of ground receivers whose positions are known a priori. Besides the external time delay (TD) measurements from sensors to ground receivers, we also exploit the inter-sensor TD measurements with the aim of improving the positioning accuracy. The devised scheme introduces the coupling terms of source and sensor locations as nuisance parameters to enable the formulation of pseudolinear equations, and then the problem could resort to a two-stage weighted least squares estimator. The scheme is shown in both theory and simulation to be capable of reaching the Cramer-Rao lower bound under the small measurement noise and small sensor position errors. [ABSTRACT FROM AUTHOR]

Generalized Cross Correlation (GCC) is the most popular localization technique over the past decades and can be extended with the beamforming method e.g. Steered Response Power (SRP) when multiple microphone pairs exist. Considering the promising results of Deep Learning (DL) strategies over classical approaches, in this work, instead of directly using Generalized Cross Correlation (GCC), SRP is derived with the DL-learnt ideal correlation functions for each pair of a microphone array. To deploy visual information, we explore the Conditional Variational Auto-Encoder (CVAE) framework in which the audio generative process is conditioned on the visual features encoded by face detections. The vision-derived auxiliary correlation function eventually contributes to the back-end beamformer for improved localization performance. To the best of our knowledge, this is the first deep-generative audiovisual method for speaker localization. Experimental results demonstrate our superior performance over other competitive methods, especially when the speech signal is corrupted by noise. [ABSTRACT FROM AUTHOR]

With the wide application of vehicular location-based services, precise estimation of the travel time plays a crucial role in intelligent transportation systems, such as driving navigation, traffic monitoring, and route planning. Recent methods have made significant progress on public datasets, but are not satisfied for current ride-hailing platforms with complex road network topology and dynamic traffic fluctuation. In this article, we propose an end-to-end Deep Fusion framework for Travel Time Estimation, which exploits multisource heterogeneous traffic information within an encoder–decoder architecture. Specifically, we explore a relational fusion network to learn the relationship of road link segments, and employ an attention mechanism to capture efficient correlations among spatial and temporal features. Extensive experiments have been conducted on two large-scale real-world traffic datasets collected by DiDi Corporation (DiDi) platform, and the results have demonstrated our effectiveness compared with the state of the art. [ABSTRACT FROM AUTHOR]

Development of a cyber security strategy for the active distribution systems is challenging due to the inclusion of distributed renewable energy generations. This paper proposes an adaptive hierarchical cyber attack detection and localization framework for distributed active distribution systems via analyzing electrical waveforms. Cyber attack detection is based on a sequential deep learning model, via which even minor cyber attacks can be identified. The two-stage cyber attack localization algorithm first estimates the cyber attack sub-region, and then localize the specified cyber attack within the estimated sub-region. We propose a modified spectral clustering-based network partitioning method for the hierarchical cyber attack ‘coarse’ localization. Next, to further narrow down the cyber attack location, a normalized impact score based on waveform statistical metrics is proposed to obtain a ‘fine’ cyber attack location by characterizing different waveform properties. Finally, compared with classical and state-of-art methods, a comprehensive quantitative evaluation with two case studies shows promising estimation results of the proposed framework. [ABSTRACT FROM AUTHOR]

We propose a marker-based geometric framework for the high-frequency absolute 3D pose estimation of a binocular camera system by using the data captured during the exposure of a single rolling shutter scanline. In contrast to existing approaches enforcing temporal or motion models among scanlines (e.g. linear motion, constant velocity or small motion assumptions), we strive to determine the pose from instantaneous binocular capture (i.e. without using data from previous scanlines) and achieve drift-free pose estimation. We leverage the projective invariants of a novel rigid planar pattern, to both define a geometric reference as well as to determine 2D-3D correspondences from raw edge detection measurements from individual scanlines. Moreover, to tackle the ensuing multi-view estimation problem, achieve real-time operation, and minimize latency, we develop a pair of custom solvers leveraging our geometric setup. To mitigate sensitivity to noise, we propose a geometrically consistent measurement refinement mechanism. We verify the quality of our solvers by comparing with state of the art general solvers for absolute pose estimation of generalized cameras. Finally, we demonstrate the effectiveness of our proposed approach with an FPGA-based implementation which achieves a localization throughput of 129.6 KHz with a $1.5\ \mu \mathsf{s}$ latency. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel monocular visual-inertial odometry system with pre-built maps deployed on the remote server, which can robustly run in real-time on a mobile device even in high latency situations. By tightly coupling VIO with geometric priors from pre-built maps, our system can tolerate the high latency and low frequency of global localization service, which is especially suitable for practical applications when the localization service is deployed on the remote server. Firstly, sparse point clouds are obtained from the dense mesh by the ray casting method according to the localization results. The dense mesh can be reconstructed from the point clouds generated by Structure-from-Motion. We directly use the sparse point clouds in feature tracking and state update to suppress drift. In the process of feature tracking, the high local accuracy of VIO is fully utilized to effectively remove outliers and make our system robust. The experiments on EurocMav datasets and simulation datasets show that compared with state-of-the-art methods, our method can achieve better results in terms of both precision and robustness. The effectiveness of the proposed method is further demonstrated through a real-time AR demo on a mobile phone with the aid of visual localization on the remote server. [ABSTRACT FROM AUTHOR]

This letter investigates the problem of accurate localization of a target node in wireless sensor networks using time of arrival (TOA) and received signal strength (RSS) measurements in an adverse non-line of sight (NLOS) environment. The proposed methodology works without any requirement of the NLOS path identification or the prior knowledge of NLOS bias distribution. A non-linear weighted least squares (NLWLS) problem is formulated through general approximations on the hybrid data model. The formulated NLWLS problem is solved using a computationally efficient majorization-minimization (MM) algorithm in which the NLWLS objective is iteratively minimized via simple update steps. The proposed MM algorithm is guaranteed to converge to a stationary point of the NLWLS objective. Simulation results and computational complexity analysis validate that the proposed MM algorithm attains fast convergence with lower latency. Moreover, the proposed hybrid localization algorithm outperforms the state-of-art methods in terms of estimation accuracy and computational complexity. [ABSTRACT FROM AUTHOR]

WiFi-based indoor localization using the fine time measurement (FTM) protocol has become a popular technique. However, in harsh Non-Line-of-Sight (NLoS) environments, WiFi FTM positioning (WFP) suffers from poor performance. In this letter, a novel WiFi FTM localization method with the assistance of geomagnetic positioning (GP) is proposed. To ensure the accuracy of GP, an enhanced mind evolutionary algorithm (EMEA) is designed. A fine-grained WiFi position estimation method using the overlapping searching area (OSA) and the coincident points selection strategy is proposed. Experimental results show that the EMEA-based GP improves the localization performance of WFP in NLOS environments, the mean localization error (ME) and root-mean-square error (RMSE) of the GP-aided WFP are 1.82 m and 2.08 m, respectively. Compared with the classic WFP using the weighted least square (WLS) method, the ME and RMSE are reduced by 51.7% and 52.4%, respectively. [ABSTRACT FROM AUTHOR]

From sirens to car horns, the urban environment is full of sounds that are designed to direct drivers’ attention to conditions that require special care. Microphone-equipped autonomous vehicles can also use these acoustic cues to increase safety and performance. This article explores auditory perception in the context of autonomous driving and smart vehicles, in general, examining the potential of exploiting acoustic cues in driverless vehicle technology. With a journey through the literature, we discuss various applications of auditory perception in driverless vehicles, ranging from the identification and localization of external acoustic objects to leveraging ego noise for motion estimation and engine fault detection. In addition to solutions already proposed in the literature, we point out directions for further investigations, focusing, in particular, on parallel studies in the areas of acoustics and audio signal processing that demonstrate potential for improving the performance of driverless cars. [ABSTRACT FROM AUTHOR]

Most modern organizations today support network infrastructure to provide ubiquitous network coverage at their premises. Such a network infrastructure consisting of a set of access points deployed at different locations in buildings can be used to support coarse-level localization of individuals, who connect to the infrastructure using their mobile devices. This paper describes a system, entitled Quest that supports a variety of applications (e.g., identifying hotspot regions, finding people who are potentially exposed to a condition such as COVID-19, occupancy count of a region/floor/building) based on network data to empower organizations to maintain safety at their workplace/premises. Quest builds the above functionalities while fully protecting the privacy of individuals. Quest incorporates computationally- and information-theoretically-secure protocols that prevent adversaries from gaining knowledge of an individual's location history (based on WiFi data). We describe the architecture, design choices, and implementation of the proposed security/privacy techniques in Quest. We, also, validate the practicality of Quest and evaluate it thoroughly via an actual campus-scale deployment at our organization over a very large dataset of over 50M rows. [ABSTRACT FROM AUTHOR]

We designed a distributed fiber optic sensor based on a hybrid Michelson-Sagnac interferometer to overcome the defect that the conventional hybrid structure cannot locate when the disturbance position is close to the midpoint of the Sagnac ring. The sensor has excellent and flat frequency response for no-blind zone location. With the demodulation algorithm based on 3 × 3 optical coupler and simple mathematical operations, we can get two phase signals with time difference for location and pattern recognition. The phase signals were converted to two-dimensional images by Gramian Angular Field. Tagging images to create a dataset and feeding dataset into a convolutional neural network to achieve pattern recognition. After experiments, the frequency response capability of the structure was verified, and it was able to achieve location within 0-20 km with location errors of $ \pm $ 42 m. In a six-category pattern recognition, the testing set accuracy was 97.83%. [ABSTRACT FROM AUTHOR]

Time delay estimation (TDE) is an important step in identifying and locating vibration sources. The TDE result can be obtained by the cross-correlation method through seeking the maximum correlation peak of two signals. However, the cross-correlation method will induce random error when dealing with the nonstationary signal. We propose a novel time shifting deviation (TSDEV) method to solve this problem, which has been proven to achieve ultrahigh-precision localization results in the fiber vibration monitoring system. This paper compares the TSDEV method with cross-correlation in detail by simulating the TDE process in different conditions, such as signals with arbitrary intercepted length, nonstationary drift and correlated noise. In addition, an experimental demonstration was carried out on a 60 km fiber to localize a wide band vibration signal. The typical localization error is 3.0 m with a standard deviation of 23.0 m using the TSDEV method. It stands in clear contrast to the result of cross-correlation method, whose localization error is 76.3 m and the standard deviation is 216.5 m. Compared with cross-correlation method, TSDEV has the same resistance to white noise, but has fewer boundary conditions and better suppression effect on linear drift or common noise, which leads to more precise TDE results. [ABSTRACT FROM AUTHOR]

A reconfigurable intelligent surface (RIS) is a nearly-passive flat layer made of inexpensive elements that can add a tunable phase shift to the impinging electromagnetic wave and are controlled by a low-power electronic circuit. This paper considers the fundamental problem of target detection in a RIS-aided multiple-input multiple-output (MIMO) radar. At first, a general signal model is introduced, which includes the possibility of using up to two RISs (one close to the radar transmitter and one close to the radar receiver) and subsumes both a monostatic and a bistatic radar configuration with or without a line-of-sight view of the prospective target. Upon resorting to a generalized likelihood ratio test (GLRT), the design of the phase shifts introduced by the RIS elements is formulated as the maximization of the probability of detection in the location under inspection for a fixed probability of false alarm, and suitable optimization algorithms are proposed. The performance analysis shows the benefits granted by the presence of the RISs and shed light on the interplay among the key system parameters, such as the radar-RIS distance, the RIS size, and the location of the prospective target. A major finding is that the RISs should be better deployed in the near-field of the radar arrays at both the transmit and the receive side. The paper is concluded by discussing some open problems and foreseen applications. [ABSTRACT FROM AUTHOR]

Ultra-wide band (UWB) localization system suffers from deteriorating performance in complex scenes, especially in non-line-of-sight (NLOS) conditions. In order to improve the accuracy and robustness of the localization system in NLOS environments, we propose an end-to-end deep neural network with both distance and received signal strength (RSS) measurements. On one hand, high-level spatial-temporal features can be learned through the proposed network from both RSS and distance data, which benefits the localization performance. On the other hand, the proposed network is robust to the variance in the number of available anchors, leading to high adaptability to different scenes. Specifically, three modules are designed in the deep network: 1) a module based on convolutional neural network (CNN) is presented to extract the local spatial features from the input data, and the structure of this module lends itself to varying-dimension input. 2) To manage the correlations between consecutive frames, we develop a deep long short-term memory (LSTM) model to extract temporal features and provide a high-level representation for a series of input data. 3) Finally, the fully-connected layers are utilized to estimate the 3D positions of the UWB tag. We conduct extensive experiments in three real-world scenarios to evaluate the proposed deep network. The experimental results indicate that the proposed network can significantly improve the accuracy and robustness of the UWB localization results, especially in NLOS situations. [ABSTRACT FROM AUTHOR]

Human target localization has a plethora of ubiquitous applications, including healthcare and security. Reliable location estimation is challenging in outdoor settings because most targets do not carry, wear, or attach a sensor to their bodies to provide motion information. Seismic and audio sensors can be used extensively to monitor targets in an unobtrusive manner. This paper proposes regression-based methods to localize a human target in an outdoor environment using seismic and audio signatures. The approaches fall into two groups. The first group of approaches applies a late fusion on the audio and seismic modality for target localization. First, a regression algorithm on the seismic signals is applied to estimate the distance of the target from each sensor; then, the target distance information is combined with the audio direction to infer the target location. In contrast to the first group of approaches, the second group of approaches applies an early fusion of audio and seismic modality to directly predict the target location. We compare the proposed methods with multiple state-of-the-arts on different evaluation measures. On 5-fold cross-validation, we achieve a root-mean-localization error of 0.735 and 0.907 meters in an area of 324 meter2, for dataset-1 and dataset-2, respectively. The best results with proposed approaches show an improvement of 4.68 and 4.57 meters over the best performing state-of-the-art approach for dataset-1 and dataset-2, respectively. We also analyzed the generalization capabilities of the proposed methods. Further, a detailed ablation study on the number of sensors required for localization, feature selection, training data requirement, etc., has been carried out. [ABSTRACT FROM AUTHOR]

Permanent magnet tracking technology is a reliable solution used to localize the wireless capsule endoscope (WCE) with an embedded permanent magnet. However, in wearable permanent magnet tracking systems, compensation for the geomagnetic field plays a key role in the localization accuracy. The geomagnetic field superimposed on the sensor measurement will change in accordance with human body posture changes, thus causing a reduction in localization accuracy. In this study, a wearable permanent magnet tracking system and a corresponding calibration method are proposed to compensate for the geomagnetic field interference dynamically. The proposed system overcomes the shortcomings of existing systems that prevent patients from freely changing posture. In addition, a nine-axis inertial measurement unit is adopted to obtain a robust posture estimation that contributes to the compensation of the geomagnetic field. This innovation allows the localization accuracy to be maintained over a longer period of time than with current technology. The experiments show the effectiveness and stability of the wearable permanent magnet tracking system for realizing the geomagnetic field compensation. Within a tracking region of 46 cm $\times34$ cm $\times28$ cm, the proposed method achieves a 15-Hz update rate. Furthermore, the average position error caused by postural changes is reduced from 9.9 mm to 1.8 mm, and the average orientation error is reduced from 25.4° to 5.1°. [ABSTRACT FROM AUTHOR]

This article proposes two algorithms for the localization or motion analysis of a moving target, by using a number of stationed sensors observing the frequency of the emitted target signal. This is a challenging problem due to the complicated non-linear relation between the target location parameters and the observations. The first algorithm is closed-form through algebraic evaluation that is computationally efficient. The second is derived from semidefinite programming by relaxationthat is robust against noise. The situations of having target frequency known but erroneous and completely unknown are investigated, as well as the presence of sensor position errors. Sequential algorithms for motion analysis are proposed in addition to the two for reducing latency and complexity. The algorithms are analyzed and their performance is able to achieve the CRLB accuracy under Gaussian noise. [ABSTRACT FROM AUTHOR]

Target localization with unambiguous range and angle estimation of the signal-of-interest is a key task of a radar system, especially for wide region surveillance. In this article, we develop a novel dual-mode array radar scheme, namely phased array and frequency diverse array cooperated radar, to provide unambiguous range and angle estimation. Our approach combines the advantages of the PA in high transmit gain and that of the FDA in resolving range ambiguity. In general, two kinds of cooperative parameter estimation algorithms are derived to determine the range and angle parameters jointly. The proposed algorithms can be interpreted as different combination methods of the PA and FDA, including the uniform combination and weighted combination based on weighted least squares, where the weight is designed with user tunable angle parameters. Particularly, the design criterion for the tunable parameter is given. At the analysis stage, the Cramér–Rao bounds are studied to verify the performance of parameter estimation. Extensive simulation results demonstrate the effectiveness of parameter estimation with the dual-mode array radar. [ABSTRACT FROM AUTHOR]

Least squares (LS) estimation is simple yet effective for parameter estimation. Most real-world problems are nonlinear. In practice, nonlinear LS problems are linearized first and then solved by a linear LS estimator. However, since these methods solve a nonlinear LS problem by a linear way, there is room for improvement only if estimators being nonlinear in the original measurement. In this article, an LS approach is proposed for nonlinear LS problems, along with theoretical analysis and justification. First, for a general nonlinear measurement model with any form of noise, a unified linearization (UL) approach is proposed under certain assumptions and a linearized least squares (LLS) estimator is defined based on the UL model. Second, an augmented nonlinear least squares (ANLS) estimator is proposed based on LLS estimation. In the ANLS estimator, the original measurement is augmented by its nonlinear conversion intended to further utilize the nonlinear measurement. Moreover, we prove that the ANLS estimator outperforms LLS estimation-based estimators. Finally, the UL and the ANLS estimator are applied to source localization with time difference of arrival and/or frequency difference of arrival measurement, and a two-step ANLS localization algorithm is developed. The performance of the proposed algorithm is evaluated via simulation of some typical localization scenarios. [ABSTRACT FROM AUTHOR]

Modeling cross-video relationship is an important issue for the weakly supervised temporal action localization task. To this end, traditional methods operate at the action level and rely on complicated strategies to prepare triplet samples, which only mines the cross-video relationships among three videos from two categories. In this work, we observe that action instances from different categories could exhibit similar motion patterns, i.e. subaction, and propose to operate at the sub-action granularity to elaborately explore cross-video relationships. However, only given video-level category labels, the sub-actions are undefined and not annotated. To tackle this challenge, we represent video features via a group of sub-actions, i.e. the sub-action family. Specifically, the sub-action family contains multiple feature vectors, where each vector is in charge of representing a specific sub-action. The sub-action family is shared among all videos in the dataset, while all videos contribute to the learning of the sub-action family. Consequently, we can not only get rid of the complicated sampling strategy but also thoroughly mine cross-video relationships from all available videos in the dataset. To learn feature vectors within the sub-action family, we employ a bottom-up temporal action localization paradigm and introduce an extra top-down branch. The sub-action family is introduced into the top-down branch, and it learns feature vectors via representing raw video features. Moreover, we propose a consistency loss to guide the learning process and a diversity loss to mine distinct sub-actions. Extensive experiments are carried out on three benchmark datasets, i.e. THUMOS14, ActivityNet v1.2 and ActivityNet v1.3, and the proposed method builds new high performance. [ABSTRACT FROM AUTHOR]

The use of radio frequency identification (RFID) technology for the traceability of products throughout the production chain, warehouse management, and the retail network is spreading in the last years, especially in those industries in line with the concept of Industry 4.0. The last decade has seen the development of increasingly precise and high-performance methods for the localization of goods. This work proposes a reliable 2-D localization methodology that is faster and provides a competitive accuracy, concerning the state-of-the-art techniques. The proposed method leverages a phase-distance model and exploits the synthetic aperture approach and unwrapping techniques for facing phase ambiguity and multipath phenomena. Trilateration applied on consecutive phase readings allows finding hyperbolae as the localization solution space. Analytic calculus is used to compute intersections among the conics that estimate the tag position. An algorithm computes intersections quality to select the best estimation. Experimental tests are conducted to assess the quality of the proposed strategy. A mobile robot equipped with a reader antenna localizes in 2-D the tags placed in an indoor scenario and reconstructs the map of the environment through a simultaneous localization and mapping (SLAM) algorithm. Note to Practitioners—A localization technology based on passive ultrahigh-frequency (UHF) radio frequency identification (RFID) is an enabling technology for intelligent warehouses, logistics, and retails. For this reason, this work presents a novel method to estimate the tag location with high accuracy. A reader antenna is mounted on an autonomous mobile robot that can move in an indoor or outdoor environment due to a simultaneous localization and mapping (SLAM) algorithm. The motion of the antenna generates a synthetic aperture. The system receives the phase measurements from the RFID tags and generates a distance model through phase unwrapping. In such a way, the possible locations of the tags in the environment are generated, creating conics. The trilateration step is performed analytically, intersecting the obtained conics. The resulting estimations are very accurate and not computation expensive. Therefore, the proposed approach can be employed in any application where localizing objects is fundamental even when reduced computational power is available, e.g., in warehouses where the products are at known heights, or where the items are placed on a fixed infrastructure, such as high-shelves logistics, to produce the inventory of the tagged objects within each shelf. [ABSTRACT FROM AUTHOR]

Localization in the complex urban environment is an open problem for current methods. The occlusion from dynamic objects, such as vehicles and pedestrians, degenerates the precision of the localization result. This article proposes a pole-like feature-based localization framework to solve this problem. Pole-like objects, such as posts of lamps or traffic sign and tree trunks, widely exist in the urban environment and are robust to occlusion, as they are usually higher than the objects on the road. First, this type of feature is extracted from the point cloud by a robust clustering algorithm. Then, the features from different frames of data are stitched to generate a feature map. For online localization, a Monte Carlo localization (MCL) framework is used to fuse the vehicle motion data and the map-matching result. An improved version of iterative closest point (ICP) that is specifically designed for the pole-like feature association is used for map matching based on the state of every particle. With the MCL scheme, localization is robust to the local minimum or robot kidnapping problem. Experimental results in the real urban environment demonstrate the precision and robustness of the proposed method, with mean absolute errors less than 0.20 m and 0.5°. The results also show that the proposed method outperforms some state-of-the-art localization methods in the complex urban environment. Note to Practitioners—There are some works using features from the 3-D point cloud, e.g., corners, planes, and reflectance, for robot localization. Instead of the abstract features, this article presents an object-feature-based localization scheme. We propose a novel pole-like object extraction algorithm based on the spatial distribution of the 3-D points. This algorithm can extract most types of pole-like objects in the urban environment. As these objects are highly distinct from other types of objects in their surroundings, localization is achieved by associating the pole-like features in the map and the features detected in real time through maximizing the likelihood. The whole system is verified with data collected in the real world, which indicates that its accuracy can fulfill the requirements of autonomous driving. The limitation of the proposed method is that it highly depends on one specific type of feature, which may not work well in the rural environment. In future research, we will address this problem by incorporating more types of semantic features for localization. [ABSTRACT FROM AUTHOR]

In recent years, onboard self-localization (OSL) methods based on cameras or lidar have achieved many significant progresses. However, some issues such as estimation drift and robustness in low-texture environment still remain inherent challenges for OSL methods. On the other hand, infrastructure-based methods can generally overcome these issues, but at the expense of some installation cost. This poses an interesting problem of how to effectively combine these methods, so as to achieve localization with long-term consistency as well as flexibility compared to any single method. To this end, we propose a comprehensive optimization-based estimator for the 15-D state of an unmanned aerial vehicle (UAV), fusing data from an extensive set of sensors: inertial measurement unit (IMU), ultrawideband (UWB) ranging sensors, and multiple onboard visual-inertial and lidar odometry subsystems. In essence, a sliding window is used to formulate a sequence of robot poses, where relative rotational and translational constraints between these poses are observed in the IMU preintegration and OSL observations, while orientation and position are coupled in the body-offset UWB range observations. An optimization-based approach is developed to estimate the trajectory of the robot in this sliding window. We evaluate the performance of the proposed scheme in multiple scenarios, including experiments on public datasets, high-fidelity graphical-physical simulation, and field-collected data from UAV flight tests. The result demonstrates that our integrated localization method can effectively resolve the drift issue, while incurring minimal installation requirements. [ABSTRACT FROM AUTHOR]

This article formalizes, under a single common multisensor-based predictive control framework, five different types of parking maneuvers: perpendicular, diagonal for both forward and backward motions, and parallel for backward motions. Since, from a practical point of view, forward parallel parking is usually not advisable, it is not addressed in this work. By moving the effort from motion planning to control, the parking tasks can be completely defined solely from the detected empty parking spots. Additionally, the classical compromise between completeness and computational efficiency when compared to exploration-based path planning techniques is eliminated. The results of a few individual cases are presented and compared against a state-of-the-art path planning approach to illustrate the behavior and performance of the proposed framework as well as results from exhaustive simulations to assess its convergence. As shown in the convergence analyses, the presented approach allows us to park from virtually any sensible initial pose. Finally, real experimentation using a robotized Renault ZOE shows the validity and robustness in the convergence domain of the presented approach. [ABSTRACT FROM AUTHOR]

Ultrasound localization microscopy (ULM) is an emerging vascular imaging technique that overcomes the resolution-penetration compromise of ultrasound imaging. Accurate and robust microbubble (MB) localization is essential for successful ULM. In this study, we present a deep learning (DL)-based localization technique that uses both Field-II simulation and in vivo chicken embryo chorioallantoic membrane (CAM) data for training. Both radio frequency (RF) and in-phase and quadrature (IQ) data were tested in this study. The simulation experiment shows that the proposed DL-based localization was able to reduce both missing MB localization rate and MB localization error. In general, RF data showed better performance than IQ. For the in vivo CAM study with high MB concentration, DL-based localization was able to reduce the vessel MB saturation time by more than 50% compared to conventional localization. In addition, we propose a DL-based framework for real-time visualization of the high-resolution microvasculature. The findings of this article support the use of DL for more robust and faster MB localization, especially under high MB concentrations. The results indicate that further improvement could be achieved by incorporating temporal information of the MB data. [ABSTRACT FROM AUTHOR]

The development of Internet of Things (IoT) requires demanding accurate and low-power indoor localization. In this article, a high-precision 3-D ultrasonic indoor localization system with ultralow power is proposed. A new piezoelectric micromachined ultrasonic transducer (PMUT) chip with a slotted membrane is designed and fabricated as a receiver, breaking the dilemma of low resonant frequency and wide field of view required for indoor localization. The system works based on the time difference of arrival (TDoA), and an improved quantum genetic algorithm (QGA) is used to estimate the location. The results show that the system achieves centimeter-level positioning precision, which is among the best solutions nowadays. Due to the high performance and small size endowed by the PMUT, the receiver footprint reaches as small as 0.25 cm2 and power consumption could reach as low as 0.1 mW, which are far better than that of current indoor localization systems. [ABSTRACT FROM AUTHOR]

Near-field mapping along the surface of an integrated circuit (IC) could reveal the radiated emission source inside by collecting the radiated electromagnetic field. In this article, the radiated emission source localization is first illustrated in a microstrip line model by analyzing the relationship between the physical position of the line and the hot spots of the electromagnetic field distribution along the line surface. The hot spot in each magnetic field distribution does not necessarily correspond to the physical position of the emission source due to the ground plane below. Furthermore, by analogy with the microstrip line model, the localization analysis is applied to a large-scale IC [field-programmable gate array (FPGA)] in a ball grid array package. For the demonstration, four operation states of the FPGA are designed with four general-purpose input–output (GPIO) pins outputting waveforms of different frequencies. By activating the sources in different operation states, it is found that the activated GPIO pins (as emission sources) can be located well by comprehensively analyzing the emission patterns of different components. The experimental results would be instructive to improve the understanding of IC near-field electromagnetic patterns and reasonable to localize an emission source. [ABSTRACT FROM AUTHOR]

Finding out and turning off the radio-frequency interference (RFI) sources as many as possible is an important work for synthetic aperture interferometric radiometer (SAIR). While the impact of strong RFIs on reconstructed brightness temperature (BT) image is clearly observable, the presence of low- or moderate-level RFIs is often hard to be aware of, leaving the RFI detection and localization a difficult problem. In this article, an improved RFI detection method is proposed by making use of the point-source ripple to give prominence to the RFIs presented in the SAIR imagery. The method tests based on Soil Moisture and Ocean Salinity satellite (SMOS) visibility samples and the performance evaluations by numerical simulations validate the correctness and effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

This paper focuses on optimal time-of-arrival (TOA) sensor placement for multiple target localization simultaneously. In previous work, different solutions only using non-shared sensors to localize multiple targets have been developed. Those methods localize different targets one-by-one or use a large number of mobile sensors with many limitations, such as low effectiveness and high network complexity. In this paper, firstly, a novel optimization model for multi-target localization incorporating shared sensors is formulated. Secondly, the systematic theoretical results of the optimal sensor placement are derived and concluded using the A-optimality criterion, i.e., minimizing the trace of the inverse Fisher information matrix (FIM), based on rigorous geometrical derivations. The reachable optimal trace of Cramér-Rao lower bound (CRLB) is also derived. It can provide optimal conditions for many cases and even closed form solutions for some special cases. Thirdly, a novel numerical optimization algorithm to quickly find and calculate the (sub-)optimal placement and achievable lower bound is explored, when the model becomes complicated with more practical constraints. Then, a hybrid method for solving the most general situation, integrating both the analytical and numerical solutions, is proposed. Finally, the correctness and effectiveness of the proposed theoretical and mathematical methods are demonstrated by several simulation examples. [ABSTRACT FROM AUTHOR]

Multi-autonomous underwater vehicles (AUVs) cooperative localization has become a research hotspot in the marine navigation field. In this paper, a filtering algorithm for slave AUV with compass failure is proposed based on ‘two-master-one slave’ cooperative localization model. In this algorithm, the course angle is unknown, and non-Gaussian measurement noise caused by measurement outliers is considered. The cooperative localization model is reconstructed, and a filtering algorithm is derived whose performance will not be affected by the unknown course angle. Moreover, the maximum information potential (MIP) criterion is introduced into a recursive model to deal with the non-Gaussian measurement noise in a complicated underwater environment. Simultaneously, the kernel bandwidth is adaptively adjusted by the innovation matrix and the measurement error covariance matrix. The proposed algorithm can accurately estimate the variation of the unknown course angle without expanding the dimension of state under the condition of non-Gaussian measurement noise. Finally, the proposed algorithm is verified through field experiments. The results show that the proposed algorithm has higher accuracy and robustness than other existing algorithms. [ABSTRACT FROM AUTHOR]

Transmissibility is a mathematical model that relates a subset of a system’s outputs to another subset of outputs of the same system without knowledge of the external excitation or the dynamics of the system. This study investigates fault detection, localization, and mitigation of connected autonomous vehicles (CAV) platoons using transmissibility operators. A CAV platoon is a network of connected autonomous vehicles that communicate together to move in a specific path with the desired velocity. Failure in a physical component of a vehicle, or failure in the form of an internal delay, a cyber-attack, or a communication time-delay affects the safety and security of the CAV platoons. In this paper, we use measurements from sensors available in CAV platoons to identify transmissibility operators, which are used for health monitoring, fault localization, and fault mitigation in the platoon. We first consider the case of vehicle-to-cloud communication (V2C) to monitor the platoon’s health. Then, we assume that the platoon loses communication with the cloud, and we monitor the health of the platoon based on vehicle-to-vehicle (V2V) communication. We apply the proposed technique to a model of the platoon obtained using the bond graph approach, and an experimental setup consisting of three connected autonomous robots. [ABSTRACT FROM AUTHOR]

Geo-localization services are an important functionality in cellular networks. Besides, the use of Ultra Dense Networks and small cells, in current and future cellular networks, greatly increases the complexity of centralized localization approaches. Consequently, we propose a Self-Synchronization Positioning Estimation (SSPE) algorithm that estimates the transmitter position in a distributed fashion. The proposed SSPE algorithm reaches consensus for the posterior distribution of the transmitter position rather than on the final estimates. Such consensus ensures that the proposed SSPE algorithm converges to the centralized Direct Positioning Estimation (DPE) approach, which has the best performance of all localization approaches. We show that the proposed algorithm is related to the Iterative Positioning Estimation (IPE) algorithm, since both exploit the self-synchronization mechanism. As a result, the improvements and extensions for IPE, previously studied in other works, can be directly applied to the proposed SSPE algorithm. In addition, the proposed algorithm is able to localize the transmitter even when it is not time synchronized with the network as it is usually the case. The performance of the algorithms is numerically assessed through Monte-Carlo simulations by the mean distance error and mean range offset error. Finally, we not only show that our approach gets close to the DPE performance after a few iterations, but also that it converges for different logical network configurations. [ABSTRACT FROM AUTHOR]

The present work addresses localization problem in arbitrarily-deployed wireless networks in the presence of malicious attackers that can manipulate (spoof) their distance measurements. This problem is very important for the forthcoming applications that depend on accurate location information, since malicious attacks can potentially lead to disastrous consequences without the consideration of the security aspect. Therefore, this work proposes a new scheme for attacker detection based on generalized likelihood ratio test (GLRT) applied after a preliminary location estimate is obtained based on a simple geometric approach. The detected attacker is then removed from the localization process, which is converted into a generalized trust region sub-problem (GTRS) framework by applying the law of cosines (LC) and solved efficiently via bisectioning. The work also introduces theoretical bounds on the probability of detection in closed-form and studies the performance of the proposed algorithm from computational complexity, success in attacker detection, and localization accuracy perspectives. Our simulations corroborate the effectiveness of the proposed scheme, offering good localization accuracy and detection performance even in noisy environments [ABSTRACT FROM AUTHOR]

Source localization plays a key role in many applications including radar, wireless and underwater communications. Among various localization methods, the most popular ones are Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), Angle-Of-Arrival (AOA) and Received Signal Strength (RSS) based. Since the Cramér-Rao lower bounds (CRLB) of these methods depend on the sensor geometry explicitly, sensor placement becomes a crucial issue in source localization applications. In this paper, we consider finding the optimal sensor placements for the TOA, TDOA, AOA and RSS based localization scenarios. We first unify the three localization models by a generalized problem formulation based on the CRLB-related metric. Then a unified optimization framework for optimal sensor placement (UTMOST) is developed through the combination of the alternating direction method of multipliers (ADMM) and majorization-minimization (MM) techniques. Unlike the majority of the state-of-the-art works, the proposed UTMOST neither approximates the design criterion nor considers only uncorrelated noise in the measurements. It can readily adapt to to different design criteria (i.e. A, D and E-optimality) with slight modifications within the framework and yield the optimal sensor placements correspondingly. Extensive numerical experiments are performed to exhibit the efficacy and flexibility of the proposed framework. [ABSTRACT FROM AUTHOR]