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We propose a novel device-free localization technique for human-object tracking indoors using wireless radiofrequency fingerprint. The received signal-strength indicators (RSSIs) are measured by the receiving antennae as the features for machine learning subject to the Random Forest model. In our proposed approach, both transmitters and receivers are fixed within the room, thus making human(s) free from carrying any transceiver. The placement of receivers will impact on the localization accuracy, and therefore we investigate the effect of receiver placement in this work. We will introduce an optimal receiver placement strategy such that the average communication-link distance can be minimized. Our proposed method is verified through simulations supported by a popular channel-propagation software, Feko. The pertinent experimental results demonstrate that the localization accuracy of 77.50% can be attained by our proposed Random Forest learning system for a 20 m×10 m indoor area divided into eight equi-sized zones where sixteen receiving antennae are placed at their optimal locations along the perimeter of the room.

This paper proposes a novel indoor device-free localization and tracking approach using the received signal-strength indicators (RSSIs) of WiFi signals. The RSSI feature-vectors simulated by a channel-propagation emulator software are adopted as the training data for our proposed scheme. Prevalent discriminative machine-learning methods are used to predict the locations of a moving human-object. Hidden Markov models (HMMs) are also incorporated with such machine-learning techniques for robust and reliable indoor tracking. In this work, we partition the given indoor geometry into several equi-sized zones and then convert the underlying localization/tracking problem to the classical multi-classification problem. Simulation results demonstrate that the gradient boosting decision-tree (GBDT) classifier in conjunction with the Viterbi algorithm over hidden Markov models leads to the highest localization-accuracies of 83.9% for eight zones and 71.4% for sixteen zones. As a result, our proposed new indoor localization and tracking scheme can be very promising for many indoor device-free surveillance applications in the future.

In the past, distribution of signals within traction batteries has been carried out with wires. For example, lengthy voltage cell sense leads for lithium batteries ran from cell terminals to centralized controllers. The industry is moving to wireless data to replace wiring. A key enabler to wireless powertrain networking is the ability to exploit redundancies within multiple signals so as to reduce the required networking bandwidth. We provide an approach for bandwidth reduction which uses unsupervised learning in order to develop the training sets for long short term memory that reduces the number of signals transmitted.

3D LiDAR sensor and 2D color camera provide complementary information of a scene. It is essential to fuse both types of data to obtain a holistic representation of the real-world scene. In this paper, we present a method to fuse the data captured by rigidly mounted LiDAR and camera. We first cocalibrate the LiDAR and camera with a calibration target. We then establish 3D-2D correspondences and fuse the LiDAR and camera data through geometric transformations. Experimental results demonstrate that our method is able to fuse the 3D LiDAR point cloud with the 2D color image efficiently and accurately. Our method is also fast and can be used in autonomous driving applications.

This paper introduces a new indoor-localization approach using a deep-learning network for which the received signal-strength indicator (RSSI) is adopted as the radiofrequency fingerprint. In our proposed scheme, the RSSIs which are estimated by a channel-propagation emulator software, are adopted as the input features for deep learning. This approach is measurement-free and thus it is very cost-effective and convenient to users. A multilayer perceptron (MLP) is constructed to predict the location(s) of the mobile object(s). The time evolution of the predicted locations of an object will form the predicted trajectory thereby. Because deep-learning networks require tremendous training data to achieve good prediction accuracy, we propose to partition the indoor geometry of interest (ex., a room) into several zones. Preliminary simulation results demonstrate that the AUC (area under the receiver-operating characteristic curve) can reach up to 0.89 for a room partitioned into eight zones. Our proposed new indoor-localization scheme in this work can be a rare but promising localization technology, which is neither passive nor active as other existing prevalent localization methods.

GPS accuracy is affected by multiple factors, such as satellite clock error, atmospheric delay and multipath fading. Traditionally, develop an algorithm to increase GPS accuracy requires complicated analytical modeling on each error source. Instead, this paper aims to present an approach to improve the accuracy by utilizing deep learning technique. A Long Short-Term memory (LSTM) based prediction method is presented to predict the GPS errors. A well trained model can learn and detect the underlying patterns of the GPS errors in the past time series and thus predict the future errors. Data smoothing preprocess is performed by a digital filter, which is designed and integrated into our approach, this preprocess increases the accuracy and the stability of the system by reducing the impact of unpredictable random noise. The system is set for 10 future steps prediction, with the performances been evaluated by comparing with traditional prediction models. Based on our experimental results, the overall prediction accuracy is increased by 16%, and 37.7% at the furthest prediction step.

This paper presents a real-time long short-term memory (LSTM) recurrent neural network (RNN) to trace and predict the GPS positioning errors within next one to several seconds, offering an enhance GPS positioning. The proposed LSTM prediction model was further verified over extensive experimental data captured in cities and metropolitans, urbans and highways across several middle and eastern States of the United States. The prediction accuracy of the proposed real-time LSTM can be within less than 1-3% of its ground true values outperforms those results gained by conventional statistics and linear prediction models.

Low transmission latency and packet drop rate (PDR) are two of the most important characteristics of vehicular communications and the major indexes in evaluating the performance of their associated technologies. The two most competitive candidate technologies are Dedicated Short-Range Communications (DSRC) and Long-Term Evolution (LTE), it is essential to compare their performances, thus clarify which one fits better for active safety applications. Most of the related studies focused on the computer simulation using theoretical models and were in lack of real-world data to back up their results. It is thus necessary to build a measurement platform for practical data collection and analysis. In this study, a Vehicle-to-Vehicle (V2V) communication test platform was established, which integrated two DSRC On-Board Units (OBUs) and two LTE-4G cellular terminals. Associated software tools were developed for performance measurement. An accurate two-way time-of-flight measurement scheme was adopted to estimate packet latency in the resolution of microseconds for DSRC links and nanosecond for LTE links. The experiments were carried out under several typical weather and road conditions, including Mcity, campuses, highways, downtown, residential, rural areas and many others. The experiments focused on road safety applications with relatively close distances between two testing vehicles, ranging from 1 to 20 meters. According to experimental results, only DSRC meets all the latency requirements for safety applications such as pre-crash sensing, while LTE-4G communications with network outperform DSRC in terms of packet drop rate and coverage.

This paper presents blind modulation classification algorithm of circular linearly modulated signals using cyclic cumulant. The classification uses positions of non-zero cyclic frequencies (symbol rate frequency or carrier frequency) of the received signals. The method works over flat and frequency selective fading channels without having any knowledge of the signal parameters and channel statistics. The proposed method is used for classifying circular constellation modulation format which has not been addressed before. It requires lower complexity than the maximum likelihood approach. The performance of this novel algorithm is compared with the existing methods. In this paper, we have considered a four-class problem including quadrature phase-shift keying (QPSK), offset-QPSK, π/4-QPSK, minimum shift keying.

Fine-scale classification in form of object extraction or segmentation for high resolution SAR images is a challenging task due to the existing local noises, object deformation and part missing. A novel SAR classification method based on CRFs which combines low-level features, label context and object structure priors is presented in this paper. Local label pattern is proposed in this paper to model the object structures by measuring the local label configuration on the grid layer of SAR images. We build a new CRFs model with label context and object structure priors for image classification. Besides, we adopt Mean Field approximation for efficient inference of our CRFs model. This work intends to implement an efficient classification framework by integrating high-level label context and object priors and apply it to fine-scale object extraction of SAR images. The framework demonstrates good performance in both accuracy and efficiency for object extraction or segmentation of simulated images and high resolution SAR images.

The variational inversion for synthetic aperture radar (SAR) wind retrieval can take errors of all sources involved into account, but the complexity of ascertaining errors of wind vectors is high and the iteration process is very time-consuming. In this paper, we modify the decomposition of wind vectors into speed and direction, and adopt a damped Newton method (DNVAR) to solve the cost function, which is based on inexact line search condition. Experimental results show that DNVAR can effectively reduce background wind vector errors, and the average number of iterations for DNVAR descends greatly. For practical applications, when the background wind speed is higher than 10 m/s, the accuracy of DNVAR is higher than direct SAR wind retrieval (DIRECT), otherwise, DIRECT performs better.

In heterogeneous networks consisting of relay nodes and multiple antennas, beam-forming optimization is an enabling technology yielding high throughputs. Network coding not only transforms interferences into effective signals, but also provides more freedom of macro-relay coordination. This paper presents a network coding interference coordination scheme over lattice-forward multiple-input multiple-output channels. At first, we will derive the achievable rate with nested lattice codes, and then maximize the sum-rate of cooperating regions through iterative algorithm and Lagrange multiplier method. For comparisons, capacity of several conventional schemes are also provided. Numerical results validate the proposed scheme.

Cognitive radio (CR) implements dynamic spectrum access (DSA) mainly through performing spectrum sensing. In some circumstances, spectrum sensing is regarded as not sufficient for CR systems without generating unacceptable impact on primary users. For example, for wireless microphone (WM) systems when the transmitters either are turned off or stay idle while receivers are actively listening, CR systems decide to transmit message over the idle channel after spectrum sensing. At this moment, active WM receivers still can detect narrow-band BPSK/QPSK signals transmitted by customer premise equipments (CPEs) leading to unexpected audio signals. In this paper, we first address and study such a hidden busy channel (HBC) situation. The degree of audio interferences generated by narrow-band BPSK/QPSK signals on HBCs is evaluated through field testing. To detect HBCs, we propose a novel technique named iSens. Experiments show that detection the rate of iSens is higher than 90% while the false alarm rate is lower than 10%.

Blind symbol or baud rate estimation is one of the most important parts of the blind signal detection process. Most of the blind symbol rate estimation algorithms provided in the literature either estimate symbol rate from the baseband signal or require prior knowledge of transmitted signal parameters. Moreover, these algorithms had been mostly limited to theoretical studies. In this paper, a cyclic correlation based blind symbol rate estimation is considered for linearly modulated signals. The estimation is performed directly from an intermediate frequency (IF) signal and without having prior knowledge of transmitted signal parameters. The algorithm is implemented on a National Instruments (NI) hardware to measure its performance in a realistic environment. Theoretical and experimental results are provided and compared with existing methods.

In this paper, a novel combining weight estimation algorithm including a capability for inter-symbol interference (ISI) cancelation is proposed for monobit impulse radio (IR) ultra wideband (UWB) receivers. By applying the proposed algorithm to a monobit weighted transmitted-reference IR-UWB receiver as a case study, we have demonstrated through computer simulations that the algorithm can effectively mitigate the destructive effects of ISI and achieve considerable performance improvement over the algorithm without ISI cancellation capability. Furthermore, because most calculations required by the proposed algorithm can be performed offline and the obtained results can be saved into lookup tables for online operations, the system complexity to implement the algorithm is low. Therefore, this ISI-included combining weight estimation algorithm enables the employment of monobit IR-UWB receivers for high data rate applications under non-line-of-sight channels, and can be considered as a promising technology for IR-UWB applications demanding for low system complexity, low power consumption, and high data rates.

In this paper, the effects of the transceiver distance on the number of multipath components (MPCs), received power, maximum delay spread, and root mean squared delay spread of intra-vehicle ultra wideband (UWB) propagation channels are investigated through analyzing data obtained from field measurements. Moreover, statistical models are established to describe the behavior of those parameters as a function of distance. Analysis results show that there is a noticeable correlation between the parameters and transceiver distance. However, the average to standard deviation ratio of the measured data reveals that these parameters are very slowly decreasing with distance. This study demonstrates the robustness of intra-vehicle UWB channels against transceiver distance. Therefore, UWB technology is promising to be used for intra-vehicle wireless communication and control systems.

In this paper, a low complexity suboptimal monobit receiver, denoted as Q-function estimation (QFE) based monobit receiver, is proposed for transmitted-reference (TR) based impulse radio (IR) ultra wideband (UWB) systems to mitigate the performance degradation caused by severe quantization noise due to employing monobit ADCs. Through accumulating reference samples over multiple blocks, the QFE-based monobit receiver can considerably reduce the noise-on-noise effect and optimize the contribution of each sample to the decision statistic. Computer simulations demonstrate that in both line-of-sight (LOS) and non-LOS (NLOS) channels the QFE-based monobit weighted TR receiver can achieve a close-to-optimal bit-error rate (BER) performance without requiring full channel state information. Furthermore, because the optimal combining weights can be obtained offline and saved into a lookup table, the complexity of the proposed suboptimal monobit receiver is quite low. Therefore, it is considered as a promising technology for IR-UWB applications requiring low system complexity and low power consumption.

In this paper, two novel digital signal processing technologies, denoted as polarity-invariant square law (PISL) technology and forgetting factor based successive reference enhancement (SRE) technology, are proposed for transmitted reference (TR) based monobit receivers to compensate the quantization noise and enhance the quality of reference pulses. Since weighted TR (WTR) system can achieve a superior performance as well as a high data-rate under severe multipath environments, the PISL and SRE technologies are applied to the WTR receiver over intra-vehicle channels. The results show that compared to the SRE technology, the PISL technology enables a fixed summation time for all multipath channels, and significantly enhances the performance of the monobit ADC WTR receiver. Therefore, PISL technology is a promising technology to compensate the information loss caused by monobit quantization insuring high-performance, power efficient, and low complexity implementation for TR-based UWB receivers.

In this demonstration, we will reveal a full functional wireless access for vehicular environments (WAVE) prototype, which achieves the complete functions defined by the IEEE 802.11p standard through integrating effective and novel base-band algorithms including fast time-synchronization, multiple-stage multiple Doppler detection, accurate channel estimate, and joint QAM demodulation and convolution decoding, all upon the Cyclone III field programmable gate array (FPGA) development kit from the Terasic Company. The WAVE prototype has been extensively tested under various road conditions showing satisfied performances. The WAVE prototype has an Ethernet interface to connect with a computer, upon which media control access (MAC) protocols and a vehicular network simulator operate. Meanwhile, the prototype can also connect with an Acer Iconia Tablet running on the Android system through a universal series bus (USB) to support various intelligent transportation system (ITS) applications based on vehicular communications and networks. This activity is funded by several NSF grants and the CISCO Research Grant.

In this contribution, we study the cascaded multi-keyhole channels for cooperative wireless communications through presenting the corresponding channel model. The cascaded Wishart distribution is adopted to investigate the distribution of the eigenvalues of multi-keyhole MIMO channel matrix as well as the capacity of the wireless systems over such channels. An approximation method is presented when estimating the distribution of the largest eigenvalues of channel matrix. The fitness between analytical derivations and simulations validates the proposed models and analysis methods, which provide an important reference for cooperative diversity wireless communications.

Having adequate robustness to variations of both weight coefficient and integration interval, the weighted-transmitted reference (WTR) system can achieve a superior performance and support high data rate ultra-wideband (UWB) communications as well. To benefit from these excellent advantages without using ultra-wideband analog delay lines, in this paper, a finite-resolution digital receiver is proposed. The performance of the proposed receiver is evaluated at a data rate of 114Mbps averaged over 100 realizations of IEEE 802.15.3a channel model 1 (CM1) for different quantization levels of finite-resolution analog-to-digital converters (ADCs) operating at Nyquist rate, taking into account noise, inter-path/pulse interference (IPaI/IPI), and inter-symbol interference (ISI). The effects of sampling rates, number of reference pulses, and the ADC threshold(s), weight coefficient, and summation interval on the receiver performance are also investigated. The evaluation results show that the proposed receivers using finite-resolution ADCs only suffer a performance degradation of several dB as compared to the full-resolution ADC and analog receivers. Therefore, employing finite-resolution ADCs could be a promising approach to implement the WTR receivers without using ultra-wideband analog delay lines.

This paper analyzes the outage probability of opportunistic decode-and-forward relaying network over asymmetric fading channels where cooperative nodes are located at distributed locations and signals experience asymmetric and independent Rayleigh and Rician channels. The outage probability of such a wireless relaying network is theoretically derived in high SNR scenarios, which is thereafter verified through Monte-Carlo simulations. It is shown that the variation of the outage probability of a relaying network under different asymmetric channels is independent to the number of relay nodes.

Over the last few decades, artificial Neural Networks (NNs) has been studied well. NNs usually was used in prediction and decision making model. The key problem to design the NNs is to determine its structure. In this paper, we present an expert system based on NNs, and an efficient method is provided to determine the number of the hidden layer nodes. We take the NNs-based expert system for highway construction scheme as an example to demonstrate the feasibility of this method.

Inspired by the overwhelming popularity of nowadays wireless local area networks (wireless LAN, or Wi-Fi), researchers and developers have uncovered their wide application perspectives in a vehicular environment. Currently, several prototypes of dedicated short range communications (DSRC) for vehicular networks have been developed by some companies for both intelligent transportation systems (ITS) and vehicular infrastructure integration (VII). However, most of the designers simply apply the existing indoor wireless technologies, such as Wi-Fi, into a vehicular environment without redesigning them for a vehicular environment. The vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) channels feature with doubly selective fading due to the high mobility of vehicles. Both theoretical analysis and experiments have verified that the indoor wireless technologies are unable to achieve optimized and robust performance when applied to a vehicular environment. Industry is calling for new physical and MAC layer protocols for ITS and VII. The IEEE 802.11p workgroup is currently working on standardizing the wireless access for vehicular environment (WAVE), which is anticipated to be ratified in 2009. In this paper, we brief the design of a WAVE prototype based on the IEEE 802.11p protocol, which allows us to implement and evaluate the performance of the IEEE 802.11p standard in a practical approach.

The paper describes information theoretic capacity of orthogonal pulse based modulation schemes for time hopping ultra wideband (TH-UWB) systems over a multiple access and multipath fading channel. Orthogonal pulse based modulation schemes such as pulse shape modulation (PSM), biorthogonal PSM (BPSM) and a combination of orthogonal pulse position modulation (OPPM) and BPSM have been proposed for reducing inter symbol interference (ISI) and multiple access interference (MAI) as well as for improving system performance. System capacity of TH-UWB systems can be increased by employing these modulations for higher level modulation schemes. The capacity of OPPM-BPSM outperforms PSM and BPSM schemes over a multipath fading channel. The complete mathematical analysis and theoretical simulation results are provided in detail.

In this paper, an autoregressive channel prediction model is presented for cognitive radio(CR) systems to estimate spectrum holes. This model adopts a second-order autoregressive process and a Kalman filter. A Bayes risk criterion for spectrum hole detection is presented by considering interference temperature and channel idle probability. Theoretical analysis and simulations show that CR systems based on this scheme can greatly reduce the number of collisions between licensed users and rental users.

An endfire phased array, which is used in Wireless Access for Vehicular Environments (WAVE) Systems, is proposed in the paper. The array consists of four antenna elements, which are fed by a dual-port feeding network. These two ports are physically connected to but electrically isolated from each other. By switching between two ports, the antenna radiation pattern can be switched to the opposite endfire direction.

In this paper, we first discuss multicarrier modulation (MCM) based cognitive radio (CR) systems. A maximum likelihood ratio spectrum detection model is then presented to detect the occurrence and spectrum gap of licensed users (LUs) signals. Next, we theoretically study the proposed model by deducing an optimal decision region and the detection probability and false alarm probability. Simulation results validate the derived performances of the proposed model for MCM based CR systems.

Future wireless intra-vehicle communications (IVC) systems adopting ultra-wideband (UWB) radio are proposed in this paper, which provide onboard passengers with high-speed duplex data exchange services. In this paper, a vehicular UWB channel model is first developed by modifying the IEEE 802.15.3a indoor model to fit into the vehicular environments with dense and concentrated clusters and rays. The main channel statistical parameters, such as maximum excess delay, root mean square delay and the number of multipath components are then theoretically deduced. Next, the proposed vehicular model is verified by experimental data gained from a measurement campaign conducted within a pickup truck, which lays down a solid foundation for theoretical analysis and system design for future wireless IVC systems.

This paper presents a new phasing scheme to achieve the lowest peak-to-average power ratio (PAPR) of multi-carrier modulation (MCM) signals than that of some existing methods. The new scheme is based on a global optimization combination of the initial phases using simulated annealing. The flexibility in different modulation formats and subcarrier sizes make the proposed scheme more suitable for reduction PAPR of MCM signals. To validate the analytical results, some simulations have been conducted, showing that the proposed schemes can achieve the lowest PAPR among the any value yet reported schemes.

The applications of ultra wideband (UWB) systems in commercial vehicles are increasingly attracting attention recently. This paper aims to lay a solid foundation for the use of UWB radio in vehicular environments by measuring both intra-vehicle and inter-vehicle UWB channels. At first, a comprehensive measurement campaign was conducted to gather a set of channel impulse responses of UWB channels under several typical scenarios. The UWB vehicular channel is analyzed and compared with the IEEE 802.15.3a indoor model. Next, the bit error rate (BER) against signal to noise ratio (SNR) curves of UWB systems applied in commercial vehicles are presented, analyzed and compared to the performance of indoor UWB systems based on the IEEE 802.15.3a model.

At first, this paper presents an overview on the background and technical merits of wireless access for vehicular environments (WAVE) systems. Next, a WAVE testbed was setup and the preliminary experimental results are reported. The WAVE testbed operates at the center frequency of 5.9 GHz and adopts a signal format defined by a draft of the IEEE 802.11p standard. Based on the experimental data captured in a typical vehicle-to-roadside access point environment, related channel impulse responses and capacities are extracted and analyzed

This paper examines the effectiveness of a brake-by-wire system with the uses of differential brake torque for lateral control and yaw stability control in the context of Intelligent Vehicle Highway Systems. Differential braking easily realized by the brake-by-wire system can generate the vehicle yaw moment. The resulting yaw moment affects vehicle lateral position and yaw rate, thereby providing a limited steering function. The steering function achieved through BBW system can then be used to control lateral position in an unintended road departure system. A 7-DOF model is developed to capture the vehicle longitudinal, lateral and yaw motion, and the other four degrees of motion representing the wheel dynamics. Since the 7-DOF model is rather complicated for controller design, a linearized vehicle model is presented for the explanation of the idea of differential braking and for the control design. A fuzzy logic controller is developed to explore the feasibility and capability of BBW systems for lateral control and yaw stability control. Simulation results on the nonlinear vehicle model are used to illustrate the effectiveness of the fuzzy logic control approach for lateral control and yaw stability control with differential braking.

An experiment is conducted to compare the performances of path loss and packet loss ratio of the IEEE 802.11a and 802.11g wireless local area networks reflecting the coverage and signal quality. The experiment was performed in typical indoor and outdoor environments by adopting commercial WLAN products. The experimental data show that the IEEE 802.11g WLAN offers a wider coverage and has similar signal qualities in the locations in the proximity while the IEEE 802.11a WLAN demonstrate large variations in the signal qualities. Moreover, the significant impairments of the IEEE 802.11g WLAN are various RF interferences. The IEEE 802.11a WLAN has comparable clear electromagnetic environments.

The paper proposes a simple cascade estimation method to detect both carrier frequency and sampling clock offsets for orthogonal frequency-division multiplex (OFDM) systems. The new approach needs much less computation than the typical maximum likelihood (ML) method and generates a satisfactory estimate, which is validated by simulation results.

This paper studies the statistical properties of the conventional frequency offset estimation algorithm. Analytical expressions describing the mathematic expectation and the variance are derived for both the integer and the fractional frequency offset estimation algorithms. The theoretical equations presented in this paper provide improved agreement with simulation results in comparison to those previously reported in the literature.