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Global Satellite Navigation System (GNSS) has been widely used in various high-precision positioning services and has become an indispensable part of daily production and life. However, in urban environments, GNSS is highly susceptible to interference from non-line-of-sight (NLOS) signals, resulting in a sharp deterioration in positioning accuracy. The use of machine learning methods for NLOS detection has become a research hotspot due to its advantages of no need to change hardware devices and high accuracy. However, in existing machine learning NLOS detection work, the machine learning models used to detect NLOS are usually simple versions. In practice, these existing methods using a single model may fall into the local optimal solution. Therefore, we propose a GNSS NLOS detection method based on the stacking ensemble learning (SEL) model, which reduces the bias of each base model and reduces generalization error by fusing different and heterogeneous base models. The experiment used collected multi-scene smartphone GNSS data to conduct a detailed evaluation of the characteristics of GNSS observations, the accuracy of the models for NLOS detection, and the impact of the SEL NLOS detection model on smartphone GNSS positioning performance. The results show that the GNSS NLOS detection method based on the SEL model has higher detection accuracy and generalization ability than base models. Under static conditions, smartphones can achieve more than 90% recognition accuracy, and dynamic scenes can achieve 75% to 85% recognition accuracy. After NLOS detection based on the SEL model, the horizontal and vertical CEP95 positioning accuracy of the Vivo smartphone has been improved by 21.9% and 24.5%, respectively, and the positioning accuracy of other smartphones has also been improved. This method does not require additional improvements to the receiver hardware and has the potential to be widely applied to various GNSS terminals. [ABSTRACT FROM AUTHOR]

The advancement in the miniaturization technology of Synthetic Aperture Radar (SAR) systems and SAR three-dimensional (3D) imaging has enabled the 3D imaging of urban areas through Unmanned Aerial Vehicle (UAV)-borne array Interferometric SAR (array-InSAR), offering significant utility in urban cartography, complex environment reconstruction, and related domains. Despite the challenges posed by multipath signals in urban scene imaging, these signals serve as a crucial asset for imaging hidden targets in Non-Line-of-Sight (NLOS) areas. Hence, this paper studies NLOS targets in UAV-borne array-InSAR 3D imaging at low altitudes and establishes a multipath model for 3D imaging at low altitudes. Then, a calculation method is proposed for obtaining the multipath reachable range in urban canyon areas based on building plane fitting. Finally, a relocation method for NLOS targets is presented. The simulation and real data experiments of UAV-borne array-InSAR show that the proposed method can effectively obtain 3D images and relocate NLOS targets in urban canyon areas, with errors typically below 0.5 m, which realizes the acquisition of hidden NLOS region information.

To address the problem of low indoor positioning accuracy in time-of-arrival systems in the non-line-of-sight (NLOS) environments, we proposed an optimized positioning algorithm based on semidefinite programming (SDP). This algorithm reduces the NLOS error through a novelty method. Compared with the original SDP algorithm, we optimized the algorithm's objective function by avoiding its dependence on the prior information, thereby decreasing infeasibility problems. The experiment showed that the proposed algorithm's accuracy is superior to that of the traditional SDP algorithm in the same indoor environment. [ABSTRACT FROM AUTHOR]

The Multipath Exploitation Radar (MER) target detection technology is primarily based on the Non-Line-Of-Sight (NLOS) multipath propagation characteristics of electromagnetic waves, such as reflection and diffraction on the surface of the medium, enabling the effective detection of targets hidden in the “visually” blind area, such as urban street corners and vehicle occlusion. Thus, the technology can be feasible for various applications, including urban combat and intelligent driving. Further, it has significant practical and research implications. This paper summarizes the domestic and foreign literature in this field since the beginning of the 21st century to keep abreast of developments in this field and predict future development trends. The literature review revealed that according to the different types of detection platforms, MER target detection technology primarily consists of multipath detection technologies based on air and ground platforms. Both these technologies have achieved certain produced research results of practical significance. For air platforms, the following aspects are discussed: feasibility verification, analysis of influencing factors, architectural environment perception, and NLOS target detection. Further, for ground platforms, these four aspects are covered: target detection and recognition, two-dimensional target positioning, three-dimensional target information acquisition, and new detection methods. Finally, the prospects of MER target detection technology are summarized, and the potential issues and challenges in the current practical application of this technology are highlighted. These results show that MER target detection technology is evolving toward diversification and intelligence.

In this paper, we provide a solution based on fast Fourier Transform (FFT) for frequency detection and interference analysis in a non-line-of-sight (NLOS) multiple-input multiple-output (MIMO) optical camera communications (OCC) link. The proposed NLOS MIMO-OCC uses two organic light-emitting diodes (OLEDs) and a camera as transmitters (Txs) and the receiver, respectively, and reflections from various surfaces such as plain wall, paper and wooden surfaces. In this paper, for the first time, we analyze the performance of NLOS MIMO-OCC for two indoor scenarios utilizing two OLED Txs that are in phase and modulated at the same modulation frequency $f_{s}$ and especially when they are out of phase and under different $f_{s}$ . We carry out FFT for $f_{s}$ detection and interference analysis for OLEDs operating at different phases and $f_{s}$ . To analyze the performance of NLOS MIMO-OCC, first FFT-based $f_{s}$ detection is performed to identify Txs followed by evaluation of the reception success rates $R_{\mathrm {rs}}$ for a range of camera exposure times $t_{\exp }$ and the gain $G_{v}$ for a given $f_{s}$ . The results show that for two OLEDs in phase and different phases at $t_{\exp }$ of 1500, $G_{v}$ of 45 dB and a link span of 2 m, $R_{\mathrm {rs}}$ of 94% is achieved at $f_{s}$ of 600 Hz for the plain wall and paper reflectors. For interference analysis, the proposed FFT analysis gives accurate, i.e., 100 % $f_{s}$ detection in all the above cases.

In the field of unmanned aerial vehicle (UAV) control, high-precision navigation algorithms are a research hotspot. To address the problem of poor localization caused by non-line-of-sight (NLOS) errors in ultra-wideband (UWB) systems, an UWB/MIMU integrated navigation method was developed, and a particle filter (PF) algorithm for data fusion was improved upon. The extended Kalman filter (EKF) was used to improve the method of constructing the importance density function (IDF) in the traditional PF, so that the particle sampling process fully considers the real-time measurement information, increases the sampling efficiency, weakens the particle degradation phenomenon, and reduces the UAV positioning error. We compared the positioning accuracy of the proposed extended Kalman particle filter (EKPF) algorithm with that of the EKF and unscented Kalman filter (UKF) algorithm used in traditional UWB/MIMU data fusion through simulation, and the results proved the effectiveness of the proposed algorithm through outdoor experiments. We found that, in NLOS environments, compared with pure UWB positioning, the accuracy of the EKPF algorithm in the X- and Y-directions was increased by 35% and 39%, respectively, and the positioning error in the Z-direction was considerably reduced, which proved the practicability of the proposed algorithm.

Underwater acoustic (UWA) communication is an essential technology for networking underwater sensors and drones to support maritime activities. However, real-time data transfer among mobile nodes faces technical difficulties due to the severe nature of the UWA channel, which has large delay and Doppler spreads. Furthermore, the performance of UWA communication may be severely impaired by marked channel changes between line-of-sight (LoS) and non-line-of-sight (NLoS) environments. The goal of this work is to explore the UWA channel under LoS and NLoS conditions and evaluate their effect on UWA communication in terms of communication quality. Specifically, we measured the delay and Doppler spreads of the UWA channel by switching LoS/NLoS conditions. We also investigated the relationship between the signal-to-noise ratio (SNR) and bit error rate (BER) for UWA communication using Dopplerresilient orthogonal signal division multiplexing (D-OSDM). Experimental results showed that the delay spread of the UWA channel and received signal power markedly changes depending on LoS/NLoS conditions. Specifically, the delay spread in the LoS environment was about 10 ms, while that in the NLoS environment was much larger or difficult to observe owing to signal power attenuation. Furthermore, SNR in the NLoS environment was approximately 15 dB less than that in the LoS environment, resulting in increased BER. The obtained results suggest that adaptive signal power control is necessary to realize stable UWA communication in shallow water areas where there is a mixture of LoS and NLoS condition. [ABSTRACT FROM AUTHOR]

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Abstract Traditionally, radar‐based localisation systems are designed to deal with targets in line‐of‐sight (LOS) scenarios. However, the performance of those radar systems is limited by multipath echoes reflected from non‐line‐of‐sight (NLOS) targets and walls in the urban areas. In recent years, there has been an increasing interest in multipath exploitation methods aided by urban environmental knowledge. Based on the exploitation of space diversity provided by the multipath effect, this article proposes a novel two‐stage localisation method using a single monostatic radar via association of multipath time‐of‐arrivals (TOAs). In the offline stage, reference TOA data at different locations is generated by a ray‐tracing method to avoid the heterogeneity of multipath propagation at different locations, assuming that the environment information is completely known prior. In the online stage, the TOA set of multipath echoes from the target is estimated by a sparse reconstruction method to achieve super‐resolution rather than the matched filter. Then based on the finite‐set statistics (FISST) theory, a grid‐based multi‐observation data association (GMODA) algorithm is present as the likelihood function of the multipath measurement given a candidate location, which is followed by a maximum likelihood estimation to derive the location of the target. Furthermore, the global nearest neighbour approximation of GMODA is introduced to avoid combinatorial explosion in the association algorithm. Simulation and experiment results on a single antenna millimetre‐wave radar in various scenarios show that the proposed method achieves high localisation accuracy in both LOS and NLOS conditions while maintaining low computational cost at the same time. The presented results suggest that our method can be applied to target localisation applications in the complex urban environment.

In this paper, a high-accuracy localization problem under a complex non-line-of-sight (NLOS) condition is addressed by a new method that utilizes multiple NLOS paths to improve localization accuracy, as opposed to the traditional method of suppressing them. The spatial multi-path array fusion localization model is presented and analyzed, followed by an angle-of-arrival (AOA) and time-of-arrival (TOA) algorithm based on spatial multi-information fusion that seeks to improve localization accuracy. Multi-path of spatial signals, measurement of the multi-element antenna, and geographic environment information are integrated into the proposed method for localization optimization. Simulation experiments were carried out, and the results revealed that the proposed algorithm is capable of making full use of spatial multi-location information for localization, thus improving the accuracy of localization in a the NLOS environment effectively and increasing the locatable probability of complex environment localization applications. [ABSTRACT FROM AUTHOR]

The performance of classic radar geometry based on the line-of-sight (LOS) signal transmitted from radar to the target in the free space is affected by multipath echoes in urban areas, where non-line-of-sight (NLOS) signals reflected by obstacles are received by the radar. Based on prior information of the urban situation, this article proposes a novel two-stage localization algorithm with multipath exploitation in a dense clutter environment. In the offline stage, multipath propagation parameters of uniformly distributed samples in the radar field of view are predicted by the ray-tracing technique. In the online stage, a rough location of the target is estimated by the maximum similarity between measurements and the predicted parameters of reference samples at different locations. The similarity is described by the likelihood between measurements and the predicted multipath parameters with respect to all possible associated hypotheses. A gating threshold is derived to exclude less likely hypotheses and reduce the computational burden. The accurate target location is acquired by a non-linear least squares (NLS) optimization of the associated multipath components. Simulation results in various noise conditions show that the proposed method provides robust and accurate target localization results under dense clutter conditions, and the offline pre-calculation of ray-tracing ensures the real-time performance of the proposed localization algorithm. The root mean square error (RMSE) of simulation results shows the advantage of the proposed method over the existing method. The presented results suggest that the proposed method can be applied to NLOS target localization applications in complex environments. [ABSTRACT FROM AUTHOR]

The real-time information of the unknown ionospheric environments is difficult to obtain, plaguing the timely and accurate geolocation of high frequency (HF) sources. In this paper, we propose an improved HF skywave source geolocation method based on the time-difference-of-arrival (TDOA) with the semidefinite programming (SDP), and model HF signal propagation paths as paths with significant non-line-of-sight (NLOS) biases. With this method, no priori information about the ionosphere, especially the priori ionospheric virtual heights of reflection, is necessary while timely and accurately geolocating the HF sources. Furthermore, we use the ray tracing technique and build a 3D ionospheric electron density gridded matrix model to simulate realistic HF signal propagation paths. In the simulations, the proposed method is compared with existing methods, and detailed geolocation error distribution maps are given. In the experiments, HF I/Q data captured from different types of HF transmitters are located by six receivers with time synchronization. Simulated and experimental results show that the proposed method improves the positioning accuracy by about 50% compared with existing methods under the same conditions, and the average relative positioning error is less than 2.7%. [ABSTRACT FROM AUTHOR]

Traditionally, radar‐based localisation systems are designed to deal with targets in line‐of‐sight (LOS) scenarios. However, the performance of those radar systems is limited by multipath echoes reflected from non‐line‐of‐sight (NLOS) targets and walls in the urban areas. In recent years, there has been an increasing interest in multipath exploitation methods aided by urban environmental knowledge. Based on the exploitation of space diversity provided by the multipath effect, this article proposes a novel two‐stage localisation method using a single monostatic radar via association of multipath time‐of‐arrivals (TOAs). In the offline stage, reference TOA data at different locations is generated by a ray‐tracing method to avoid the heterogeneity of multipath propagation at different locations, assuming that the environment information is completely known prior. In the online stage, the TOA set of multipath echoes from the target is estimated by a sparse reconstruction method to achieve super‐resolution rather than the matched filter. Then based on the finite‐set statistics (FISST) theory, a grid‐based multi‐observation data association (GMODA) algorithm is present as the likelihood function of the multipath measurement given a candidate location, which is followed by a maximum likelihood estimation to derive the location of the target. Furthermore, the global nearest neighbour approximation of GMODA is introduced to avoid combinatorial explosion in the association algorithm. Simulation and experiment results on a single antenna millimetre‐wave radar in various scenarios show that the proposed method achieves high localisation accuracy in both LOS and NLOS conditions while maintaining low computational cost at the same time. The presented results suggest that our method can be applied to target localisation applications in the complex urban environment. [ABSTRACT FROM AUTHOR]

Accurate localization is the premise of many technologies and applications, such as navigation, emergency assistance and wireless sensor network. For Global Navigation Satellite System (GNSS)-denied urban or indoor environments, various localization technologies based on mobile communication networks or other wireless technologies have been designed and developed. The main challenge for these localization technologies is the presence of a non-line-of-sight (NLOS) propagation environment due to dense obstacles or buildings. The virtual station method is a promising high-accuracy target localization technique in NLOS environments, and the localization of the scatterer is key to the virtual station method. Once one-bounce scattering signals from the same scatterer are identified, the localization of the scatterer can be achieved easily with the existing localization algorithm of line-of-sight (LOS) scenario, and then the localization of NLOS scenarios is converted into a problem of LOS easily. In this paper, a hybrid time of arrival (TOA)/angle of arrival (AOA) virtual station localization algorithm based on scattering signal identification is proposed. Firstly, one-bounce scattering signals from the same scatterer are identified based on TOA/AOA measurements. Next, scatterers are located based on one-bounce scattering signals with the LOS localization algorithm, and then scatterers are regarded as virtual stations and used for mobile station (MS) localization. Compared with the existing research on the virtual station method, the proposed algorithm relies only on TOA/AOA measurements and does not require any assumption or prior knowledge about the scatterer, base station (BS) or MS, which provides a solid foundation for feasible target localization. Simulation results demonstrate, as far as we know, the proposed algorithm outperforms the state-of-the-art hybrid TOA/AOA algorithm in localization accuracy. [ABSTRACT FROM AUTHOR]

Indoor localization using WiFi signals, though getting studied for many years, is not adopted extensively in real indoor environments yet. One possible impediment is that many prior systems perform worse in non-line-of-sight (NLoS) indoor scenarios than in line-of-sight (LoS) scenarios or even fail to work. Nevertheless, realizing localization in both LoS and NLoS scenarios is indispensable for a viable system working in indoor environments. Since existing state-of-the-art solutions mainly aim to localize a target in LoS indoor scenarios, we present a system that uses multipath channel parameters to achieve localization with a single AP in indoor NLoS scenarios. The proposed introduces deployable and simple custom-designed radio relays to assist localization, which can receive signals from the target and forward them to the AP in NLoS scenarios. Field trials conducted in a real NLoS scenario show that our system achieves a median 3-D localization accuracy of 86 cm with a uniform circular array (UCA) in most NLoS conditions, i.e., the direct path from a target to AP is not completely blocked. More importantly, even if in a challenging NLoS condition, i.e., the direct path does not exist, our system can still achieve a median 2-D localization accuracy of 85 cm. [ABSTRACT FROM AUTHOR]

The UHF radio-frequency identification (RFID) has gained growing attention for tagged object localization in smart storage systems. Due to Non-Line-Of-Sight (NLOS) condition, it is challenging to accurately locate the position of tags inside closed spaces. In this paper, we propose a precise and cost-effective solution for tagged object localization in closed spaces, using only received signal strength (RSS) information. We establish a RSS profile for each tag and discover some important features of RSS profiles including uniqueness, time-variation, column-dependence and waveform-similarity. Based on these features, we propose a reference-free RSS-profile (RFRP) localization scheme. The advantage of our propose scheme is to accurately localize multiple tags in closed spaces by overcoming the challenges including the lack of pre-deployed reference tags, NLOS propagation, multi-path propagation and coupling effect. The RFRP scheme first roughly estimates tags’ coordinates based on Peak Asymmetry Factor, then acquires reference-tag substitutes through the similarity of RSS sequences. Subsequently, our scheme refines the relative positions of all tags by these substitutes. Finally all tags’ absolute positions are estimated through a RSS-ranging model. Extensive experiment results demonstrate that our approach can achieve high ordering accuracy and localization accuracy for the tags inside closed spaces. [ABSTRACT FROM AUTHOR]

Since millimeter wave (mmWave) and sub-terahertz bands are highly vulnerable to blockage and penetration loss effects, wireless coverage enhancement is one of the critical challenges in indoor mmWave deployments. In particular, when the line-of-sight (LoS) link is blocked, a strong non-LoS (NLoS) path can provide a stable link quality. One of the efficient ways to improve the NLoS link is the use of strategically placed passive reflectors. In this letter, we study the indoor coverage improvement by using a transparent passive reflector attached on a wall. We consider an indoor open-door scenario, where the LoS link is blocked by the walls for receivers inside the room, and the coverage can only be achieved via an NLoS link through a passive reflector. We analytically derive closed-form equations of the reflection visibility probability and the coverage probability. By simulation and analytical results, we show the coverage dependency on the location and size of the reflector. [ABSTRACT FROM AUTHOR]

• A UWB-ranging offset calibration model is provided to improve the positioning accuracy of these UWB-based positioning modes. • An IGG-III robust model-enhanced CKF to constrain the influences of NLOS errors and linearization errors on these UWB-based positioning modes. • A novel UWB/INS tight integration model based on UWB ranging offset calibration and robust CKF is provided. Ultra-wideband (UWB) is treated as a competitive positioning technology for indoor location-based applications. However, the non-line-of-sight (NLOS) condition would lead to a UWB solution with low accuracy and poor stability. Such an influence of NLOS errors on UWB positioning accuracy can partially be degraded after integrating UWB range measurements with measurements from an inertial navigation system (INS). Compared with the loose integration based on UWB positioning solutions and the INS-predicted solutions, such a tight integration of UWB/INS has significant advantages in operating with the number of available UWB measurements less than three and detecting and reducing the influence of NLOS errors. However, the accuracy of such a UWB/INS tight integration in the vertical direction is still poor due to the distribution of UWB base stations and UWB ranging offsets. To solve these problems in UWB positioning, this paper proposes a robust and reliable UWB/INS tight integration method based on a ranging offset calibration model and an improved robust cubature Kalman filter (IRCKF), in which the impacts of UWB ranging offsets, NLOS errors, and linearization errors are constrained. According to the results based on a set of experiment data, about 4.3 % and 54.2 % position improvements in horizontal and vertical directions can be obtained by applying ranging offset calibration in UWB positioning mode. Such positioning improvements obtained by applying the IGG-III robust model are about 51.0 % and 2.8 % in horizontal and vertical directions. Furthermore, the CKF offers about 16.0 % and 6.1 % positioning improvements in horizontal and vertical directions, compared to that of EKF. In general, the IRCKF-based UWB/INS tight integration has an accuracy of 0.082 m, 0.841 m, 0.493°, 0.468°, and 5.785° in horizontal position, vertical position, roll angle, pitch angle, and heading angle, respectively, which offers about 65.8 %, 58.9 %, 27.3 %, 26.4 %, and 46.5 % improvements percentages compared to the solutions from the EKF-based UWB/INS tight integration. [ABSTRACT FROM AUTHOR]

At the present time, most of existing security systems only detect and track targets in line-of-sight (LOS). However, in practice, the locations of targets are often out of the line of sight. This article focuses on the non-line-of-sight (NLOS) moving target detection with low-power transmission signals by reflection. There are two key problems, the weak target echo signal and the multipath effect. In terms of the issues, this paper constructs the echo signal model of the NLOS target. On the basis of the echo model, the detection method of NLOS moving target based on millimeter-wave radar comes up, which is of great theoretical value and important practical significance for indoor security. This paper innovatively applies the polynomial fitting method to suppress static noise and range gating method to suppress noise from other range gates. Then, the location and velocity of the target are estimated by two-dimensional fast Fourier transform (FFT) and the multiple signal classification (MUSIC) method. Furthermore, in order to verify the accuracy of the NLOS target echo signal model proposed in this paper, we respectively simulated two important parts of the signal in the model, the target echo signal and the direct backscattered signal of the intermediate interface, both of which are multipath signals. We counted the echo path length distribution in these two parts, and applied the NLOS target detection method to process them respectively. In addition, we also simulated the NLOS target echo signal and obtained actual data in the actual scene, and processed both the simulated data and the actual data. Comparing the results of target detection with and without denoising methods, the effectiveness of the two denoising methods proposed in this paper is verified. [ABSTRACT FROM AUTHOR]

A significant challenge of visible-light communication systems (VLC) is to overcome their limited converge area in non-line-of-sight (NLOS) transmission. To tackle this problem, for the first time, a real-time high-speed dual-hop VLC system based on blue micro-light-emitting diodes (micro-LED) is proposed and experimentally demonstrated. Benefiting from the advantage of high electrical-to-optical (E-O) bandwidth of the micro-LED, the frequency-response measurements show that the 3-dB modulation bandwidth of 2 m free-space single-hop link is 880 MHz, and the dual-hop system can reach to 715 MHz over a 4 m communication distance. We then investigated the communication performance of our proposed single-hop and dual-hop systems. The real-time waveforms are analyzed at different positions of the dual-hop link and eye diagrams at the receiving terminal are captured for evaluation. Furthermore, the bit error rate (BER) at the target node is measured. The results demonstrate that a 1.1 Gbps on-off keying (OOK) signal with a BER less than the forward-error-correction (FEC) limit could be achieved over a 4 m NLOS free-space link. This work shows that the proposed dual-hop system based on a micro-LED can meet the requirements for most indoor NLOS-transmission scenarios. [ABSTRACT FROM AUTHOR]

The need for accurate localization and robust reference tracking control of mobile robot using wireless sensor networks (WSNs) under the non-line-of-sight (NLOS) situations have been widely needed in diverse areas of industry. In order to overcome NLOS situations, a smoothing signal model is employed to embed bias signals due to NLOS in the dynamic system of the mobile robot to avoid the effect of NLOS on the positioning and remote control of the mobile robot using WSNs. In the study, a robust $\text{H}\infty $ fuzzy localization filter is developed to efficiently estimate the mobile robot pose (position and orientation) and bias signals due to NLOS using the measurement of WSNs under NLOS situation, external disturbance and measurement noise. Further, a robust $\text{H}\infty $ fuzzy NLOS-tolerant localization filter-based remote control design is also proposed for mobile robot system to track the desired trajectory for some purpose in the cluttered and noisy indoor environment in WSN. The robust $\text{H}\infty $ NLOS-tolerant fuzzy localization filter-based tracking control design problems of mobile robot in WSN can be transformed to a corresponding linear matrix inequalities (LMIs)-constrained optimization problem, which could be easily solved with the LMI toolbox in Matlab. Finally, a simulation design example is provided to illustrate the design procedure and confirm the performance of the proposed methods for the localization estimation and reference tracking control of the mobile robot in WSN in comparison with the other method in an intelligent building.

In this paper, a high-accuracy localization problem under a complex non-line-of-sight (NLOS) condition is addressed by a new method that utilizes multiple NLOS paths to improve localization accuracy, as opposed to the traditional method of suppressing them. The spatial multi-path array fusion localization model is presented and analyzed, followed by an angle-of-arrival (AOA) and time-of-arrival (TOA) algorithm based on spatial multi-information fusion that seeks to improve localization accuracy. Multi-path of spatial signals, measurement of the multi-element antenna, and geographic environment information are integrated into the proposed method for localization optimization. Simulation experiments were carried out, and the results revealed that the proposed algorithm is capable of making full use of spatial multi-location information for localization, thus improving the accuracy of localization in a the NLOS environment effectively and increasing the locatable probability of complex environment localization applications.

The performance of classic radar geometry based on the line-of-sight (LOS) signal transmitted from radar to the target in the free space is affected by multipath echoes in urban areas, where non-line-of-sight (NLOS) signals reflected by obstacles are received by the radar. Based on prior information of the urban situation, this article proposes a novel two-stage localization algorithm with multipath exploitation in a dense clutter environment. In the offline stage, multipath propagation parameters of uniformly distributed samples in the radar field of view are predicted by the ray-tracing technique. In the online stage, a rough location of the target is estimated by the maximum similarity between measurements and the predicted parameters of reference samples at different locations. The similarity is described by the likelihood between measurements and the predicted multipath parameters with respect to all possible associated hypotheses. A gating threshold is derived to exclude less likely hypotheses and reduce the computational burden. The accurate target location is acquired by a non-linear least squares (NLS) optimization of the associated multipath components. Simulation results in various noise conditions show that the proposed method provides robust and accurate target localization results under dense clutter conditions, and the offline pre-calculation of ray-tracing ensures the real-time performance of the proposed localization algorithm. The root mean square error (RMSE) of simulation results shows the advantage of the proposed method over the existing method. The presented results suggest that the proposed method can be applied to NLOS target localization applications in complex environments.

The real-time information of the unknown ionospheric environments is difficult to obtain, plaguing the timely and accurate geolocation of high frequency (HF) sources. In this paper, we propose an improved HF skywave source geolocation method based on the time-difference-of-arrival (TDOA) with the semidefinite programming (SDP), and model HF signal propagation paths as paths with significant non-line-of-sight (NLOS) biases. With this method, no priori information about the ionosphere, especially the priori ionospheric virtual heights of reflection, is necessary while timely and accurately geolocating the HF sources. Furthermore, we use the ray tracing technique and build a 3D ionospheric electron density gridded matrix model to simulate realistic HF signal propagation paths. In the simulations, the proposed method is compared with existing methods, and detailed geolocation error distribution maps are given. In the experiments, HF I/Q data captured from different types of HF transmitters are located by six receivers with time synchronization. Simulated and experimental results show that the proposed method improves the positioning accuracy by about 50% compared with existing methods under the same conditions, and the average relative positioning error is less than 2.7%.

This paper addresses the target localization problem using time-of-arrival (TOA)-based technique under the non-line-of-sight (NLOS) environment. To alleviate the adverse effect of the NLOS error on localization, a total least square framework integrated with a regularization term (RTLS) is utilized, and with which the localization problem can get rid of the ill-posed issue. However, it is challenging to figure out the exact solution for the considered localization problem. In this case, we convert the RTLS problem into a semidefinite program (SDP), and then obtain the solution of the original problem by solving a generalized trust region subproblem (GTRS). The proposed method has a relatively good robustness in localization even under the circumstance that the prior knowledge of the NLOS links or its distribution does not know. The outperformance of the proposed method is demonstrated in the simulations compared with other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Recently, vehicle-to-vehicle (V2V) based localization has attracted much attention due to its potential to achieve high accuracy. However, the error caused by non-line-of-sight (NLOS) propagation significantly affects the localization performance. In this paper, a novel method combining V2V communication and NLOS identification is proposed to improve accuracy of vehicle localization in NLOS scenarios. First, the algorithm identifies NLOS links with statistical methods. In this step, identification is conducted by decision theory or z-test, which depends on whether priori NLOS information is known or not. After that, NLOS links are discarded. Using the information including length of remaining links and GPS positions of surrounding vehicles, the estimated positions of target vehicles can be obtained by multilateration. It is shown by simulations that the proposed algorithm outperforms several classical methods in accuracy. Specifically, when priori NLOS information is available, 80% of vehicles have a localization error less than 5 m. For the case where NLOS information cannot be obtained, the algorithm still have good performance, and the ratio of vehicles having error within 5 m reaches 70%. [ABSTRACT FROM AUTHOR]

Worst-case robust approximation was proven to be efficient in alleviating the non-line-of-sight (NLOS) influence for source positioning. However, the existing time-difference-of-arrival (TDOA)-based worst-case solutions still have two issues: 1) Inaccurate objective transformations are introduced in some algorithms, which reduce the accuracy; 2) A method with higher accuracy is computationally intensive. This study proposes an accurate and simplified worst-case approximation method to tackle the troubles. Precisely, we first prove that the nonconvex worst-case objective is piecewise monotone to the NLOS bias. We further use monotonicity to derive an accurate and convex expression of the worst-case objective. Then, we propose simplified transformations to redefine the worst-case approximation problem with fewer constraints. Besides, we prove the effectiveness of the simplified transformations. Simulations and experiments demonstrate that the proposed method with moderate computation exhibits better performance than the state-of-the-art worst-case approximation algorithms. [ABSTRACT FROM AUTHOR]

As an emerging technology that has attracted huge attention, non-line-of-sight (NLOS) imaging can reconstruct hidden objects by analyzing the diffuse reflection on a relay surface, with broad application prospects in the fields of autonomous driving, medical imaging, and defense. Despite the challenges of low signal-to-noise ratio (SNR) and high ill-posedness, NLOS imaging has been developed rapidly in recent years. Most current NLOS imaging technologies use conventional physical models, constructing imaging models through active or passive illumination and using reconstruction algorithms to restore hidden scenes. Moreover, deep learning algorithms for NLOS imaging have also received much attention recently. This paper presents a comprehensive overview of both conventional and deep learning-based NLOS imaging techniques. Besides, we also survey new proposed NLOS scenes, and discuss the challenges and prospects of existing technologies. Such a survey can help readers have an overview of different types of NLOS imaging, thus expediting the development of seeing around corners. [ABSTRACT FROM AUTHOR]

We study robust network localization for realistic mixed line-of-sight and non-line-of-sight (LOS/NLOS) scenarios, where (i) NLOS identification is not performed, (ii) no statistical knowledge of the LOS/NLOS measurement error is available, and (iii) no experimental campaign is affordable. We treat the bias term of each range measurement, both for LOS and NLOS, as an unknown parameter. Based on this, we indicate that the ranging biases possess a sparsity property in LOS-heavy scenarios. To exploit this sparsity, we propose the inclusion of a sparsity-promoting term into the conventional cost functions, giving rise to a generic sparsity-promoting regularized formulation. By bounding the cost function, we further develop an alternative generic bound-constrained regularized formulation. To ensure global optimality, we specify the residual error function in these formulations so that they are conveniently solved via relaxation as two semidefinite programs (SDPs). It is also shown that the two SDPs can be equivalent in the sense that they share the same optimal solution. Compared with the sparsity-promoting regularized SDP, the bound-constrained regularized SDP has the advantage that it allows us to develop one data-driven strategy for selecting an appropriate regularization parameter. Numerical results, based on both synthetic- and experimental data, demonstrate the overall enhanced performance of the devised approach, both in terms of localization accuracy and computational efficiency. The remarkable ability of the proposed data-driven method for parameter selection, at the cost of a slight increase in computational complexity, is also shown. [ABSTRACT FROM AUTHOR]

The future 5G networks are expected to use millimeter wave (mmWave) frequency bands to take advantage of the large unused spectrum. However, due to the high path loss at mmWave frequencies, coverage of mmWave signals can get severely reduced, especially for non-line-of-sight (NLOS) scenarios as mmWave signals are severely attenuated when going through obstructions. In this work, we study the use of passive metallic reflectors of different shapes/sizes to improve 28 GHz mmWave signal coverage for both indoor and outdoor NLOS scenarios. We quantify the gains that can be achieved in the link quality with metallic reflectors using measurements, analytical expressions, and ray tracing simulations. In particular, we provide an analytical model for the end-to-end received power in an NLOS scenario using reflectors of different shapes and sizes. For a given size of the flat metallic sheet reflector approaching to the size of the incident beam, we show that the reflected received power for the NLOS link is the same as line-of-sight (LOS) free space received power of the same link distance. Extensive results are provided to study the impact of environmental features and reflector characteristics on NLOS link quality.

This paper proposes a new robust localization of mobile robot (MR) in the complex environment with non-line-of-sight (NLOS) situation. Two novel measurement processing strategies are proposed to achieve accurate recognition of NLOS measurements. In addition, an improved particle filter (PF) based on genetic algorithm (GA) is presented, where GA is introduced to improve the resampling process so PF can effectively overcome sample degradation while reducing computational complexity. The effectiveness of the algorithm is evaluated through a series of experiments and simulations. The proposed method demonstrates better accuracy than traditional methods, and can realize real-time, accurate and stable positioning of MRs in different types of NLOS environments.

This work addresses target localization problem in precarious surroundings where possibly no links are line of sight. It exploits the known architecture of available reference points to act as an irregular antenna array in order to estimate the azimuth angle between a reference point and a target, based on distance estimates withdrawn from integrated received signal strength (RSS) and time of arrival (TOA) observations. These fictitious azimuth angle observations are then used to linearize the measurement models, which triggers effortless derivation of a new estimator in a closed-form. It is shown here that, by using fixed network geometry in which target orientation with respect to a line formed by a pair of anchors can be correctly estimated, the localization performance can be significantly enhanced. The new approach is validated through computer simulations, which corroborate our intuition of profiting from inherent information within a network.

Widespread proliferation of wireless coverage has enabled culmination of number of advanced location-based services (LBS). Continuous tracking of accurate physical location is the foundation of these services, which is a challenging task especially indoors. Multitude of techniques and algorithms have been proposed for indoor positioning systems (IPS's). However, accuracy, reliability, scalability and, adaptability to the environment still remain as challenges for widespread deployment. Especially, unpredictable radio propagation characteristics in vastly varying indoor environments plus access technology limitations contribute to these challenges. Machine learning (ML) approaches have been widely attempted recently to overcome these challenges with reasonable success. In this paper, we aim to provide a comprehensive survey of ML enabled localization techniques using most common wireless technologies. First, we provide a brief background on indoor localization techniques. Afterwards, we discuss various ML techniques (supervised and unsupervised) that could alleviate different challenges in indoor localization including Non-line-of-sight (NLOS) issue, device heterogeneity and environmental variations with reasonable complexity. The trade-offs among multitude of issues are discussed using numerous published results. We also discuss how the ML algorithms can be effectively used for fusing different technologies and algorithms to achieve a comprehensive IPS. In essence, this survey will serve as a reference material to acquire a detailed knowledge on recent development of machine learning for accurate indoor positioning.

Accurate localization is the premise of many technologies and applications, such as navigation, emergency assistance and wireless sensor network. For Global Navigation Satellite System (GNSS)-denied urban or indoor environments, various localization technologies based on mobile communication networks or other wireless technologies have been designed and developed. The main challenge for these localization technologies is the presence of a non-line-of-sight (NLOS) propagation environment due to dense obstacles or buildings. The virtual station method is a promising high-accuracy target localization technique in NLOS environments, and the localization of the scatterer is key to the virtual station method. Once one-bounce scattering signals from the same scatterer are identified, the localization of the scatterer can be achieved easily with the existing localization algorithm of line-of-sight (LOS) scenario, and then the localization of NLOS scenarios is converted into a problem of LOS easily. In this paper, a hybrid time of arrival (TOA)/angle of arrival (AOA) virtual station localization algorithm based on scattering signal identification is proposed. Firstly, one-bounce scattering signals from the same scatterer are identified based on TOA/AOA measurements. Next, scatterers are located based on one-bounce scattering signals with the LOS localization algorithm, and then scatterers are regarded as virtual stations and used for mobile station (MS) localization. Compared with the existing research on the virtual station method, the proposed algorithm relies only on TOA/AOA measurements and does not require any assumption or prior knowledge about the scatterer, base station (BS) or MS, which provides a solid foundation for feasible target localization. Simulation results demonstrate, as far as we know, the proposed algorithm outperforms the state-of-the-art hybrid TOA/AOA algorithm in localization accuracy.

A key area of wireless communications is visible light communication which uses solid-state light-emitting-diode. This paper shows, a comparative study to optimize the received signal strength indication (RSSI) in the optical wireless link between two optimization algorithms. The optimization algorithm we used in the paper is a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm. To, compute the maximum RSSI over the non-line-of-sight (NLOS) link, within an indoor environment, and to find the optimum NLOS route through distinct angles to achieve better communication quality, we have used those algorithms. Simulations were performed at matlab to check the feasibility of the scheme proposed. Also, we have compared the efficiency of the both system. In addition, GA and PSO detect the maximum RSSI of the receiver (Rx) in a specific direction instead of calculating RSSI at any random angle. The simulation result shows that the proposed system precisely detects the direction that provides an optimal RSSI for the Rx. This paper concludes that the GA is computationally effective while compared with PSO. After several iterations, the result estimates that the average elapsed time of the GA is 0.21 ms and the PSO is 30 ms, respectively. In the case of user positions 1 and 2, the percentage enhancement for GA as compared to PSO is almost 4.2% and 12.5% respectively. [ABSTRACT FROM AUTHOR]

In the indoor environment, where global positioning system (GPS) fails, the presence of multipath, non-line-of-sight (NLOS), and dense scatterer yields unreliable position estimation. In the massive multiple-input multiple-output (massive MIMO) system, the sparse channel property is exploited here for user localization as an integral part of the network to reduce the power of user equipment (UE). In this study, a novel spatial spectrum fusion estimation and localization (SSFEAL) algorithm using the uniform circular array (UCA) structure is proposed, which is based on the 2-D direction of arrival (DOA) estimation. The spatial spectrum of the incoherent narrowband uplink pilot signal on each frequency bin received at the distributed massive MIMO base stations (BSs) is used for spectrum fusion. The main contributions of this work are as follows: 1) the channel’s sparse characteristics at individual BSs are used to synthesize the covariance matrix of the received signal at the central fusion center and 2) the proposed method has 2-D spatial information measurement fusion to generate informative spatial spectrum followed by grid refinement processing to reduce the localization error to submeter level. It is demonstrated that the proposed method using UCA gives higher resolution estimation performance compared with the rectangular array in the localization accuracy. Comprehensive Monte Carlo simulation and real application test results show the validity and submeter precision in the indoor environment. [ABSTRACT FROM AUTHOR]

A non-line-of-sight (NLOS) visible light communication (VLC) system, utilizing a multi-pixel photon counter (MPPC) as the detector and interleaved single-carrier frequency division multiplexing (I-SC-FDM) scheme, was first proposed and experimentally investigated in this work. Specifically, the I-SC-FDM scheme exhibits a significantly lower peak-to-average power ratio (PAPR) than the original orthogonal frequency division multiplexing (OFDM) and widely used discrete Fourier transform spread (DFTs)-OFDM schemes, thereby necessitating a much lower power gain, which is greatly beneficial for indoor power-sensitive VLC systems. Meanwhile, the implementation of an MPPC with high sensitivity enables the detection of faintly scattered light and thus facilitates the possibility of omnidirectional optical transmission based on indoor NLOS links. Following this, an MPPC-based omnidirectional VLC system was developed and successfully achieved a net data rate up to 18 Mbps in a 3.6 m × 3 m × 3 m room, with a joint deployment of the I-SC-FDM scheme and hybrid time-frequency domain equalization (TFD-EQ) strategy. Such results can be viewed as an expansion of indoor VLC systems beyond line-of-sight (LOS) links and serve as a reference for the practical application of Light-Fidelity (LiFi) in future optical wireless access. • A non-line-of-sight (NLOS) visible light communication (VLC) system, utilizing a multi-pixel photon counter (MPPC) as the detector and interleaved single-carrier frequency division multiplexing (I-SC-FDM) scheme, was first proposed and experimentally investigated in this work. This system was developed and successfully achieved a net data rate up to 18 Mbps in a 3.6 m × 3 m × 3 m room. Such results can be viewed as an expansion of indoor VLC systems beyond line-of-sight (LOS) links and serve as a reference for the practical application of Light-Fidelity (LiFi) in future optical wireless access. [ABSTRACT FROM AUTHOR]

In the last few years, optical camera communication (OCC) has benefited from built-in CMOS image sensors in smart devices. OCC is now considered a candidate for incorporation into IEEE 802.15.7r1. A major study on OCC was conducted for line-of-sight (LOS) environments. To enable users to receive OCC signals reflected from any surface easily, we considered non-LOS (NLOS) OCC in a heterogeneous reflective background in this study. A modified background subtraction method was proposed to identify the transmitted modulated signal that was recorded in an image. Then, the majority vote principle was used to restore the damaged pixels that were primarily caused by nonuniform reflection from the heterogeneous reflective background. The experimental results obtained after using a self-built NLOS OCC system demonstrated a significant improvement in the bit error rate when the proposed OCC signal recovery method was used for different distances between a smartphone and a heterogeneous reflective background.

Device-to-device (D2D) communications have been proposed as a promising technology to improve network capacity and user experiences in the future mobile networks such as heterogeneous networks with densely deployed small cells, but it has not yet been fully incorporated into the existing cellular networks. Interference management is one of the critical issues when D2D communications using uplink resources and coexisting with conventional cellular communications, especially in the ultra-dense networks (UNDs). In this paper, we address the critical issue of interference management by a mode selection method, which is based on the maximum received signal strength (MRSS) for each D2D transmitter (TU). To analyze the capacity of a more practical D2D-enhanced network, we consider that the typical user is no longer a random user, i.e., random user selection by a round-robin (RR) scheduler, as assumed in most studies in the literature. Instead, a cellular user with the maximum proportional fair (PF) metric is chosen by its serving base station as the typical user, which is referred to as the PF scheduler in the cellular tier. Furthermore, we theoretically study the performance in terms of the coverage probability and the area spectral efficiency (ASE) for both the cellular network and the D2D one with the consideration of the PF scheduler in UDNs. Analytical results are obtained, and the accuracy of the proposed analytical framework is validated through Monte Carlo simulations. Through our theoretical and numerical analyses, we quantify the performance gains brought by D2D communications and the PF scheduler in cellular networks, and we find an optimum mode selection threshold β to maximize the total ASE in the network.

Localization is one of the most critical tasks in wireless sensor networks, but achieving a relatively accurate location estimation is challenging when there have Byzantine fault and non-line-of-sight (NLOS) bias simultaneously. In this context, a localization method, based on received signal strength (RSS), is proposed in this letter to mitigate the impact of Byzantine fault and NLOS bias on the localization accuracy of wireless sensor networks. The proposed method relies on a Byzantine fault-tolerant localization algorithm (BFLA), which converts the localization problem into a generalized trust-region subproblem (GTRS) by applying certain approximations. In order to obtain a feasible solution to the GTRS, a block-coordinate update (BCU) function with a regularization term is used to divide the localization problem into two subproblems. An iterative method, whose start-point is obtained by an unconstrained squared-range (USR) algorithm, is then used to obtain a solution. Numerical simulations are carried out to show the effectiveness of the proposed method, compared with the state-of-the-art approaches in different scenarios. [ABSTRACT FROM AUTHOR]

A significant challenge of visible-light communication systems (VLC) is to overcome their limited converge area in non-line-of-sight (NLOS) transmission. To tackle this problem, for the first time, a real-time high-speed dual-hop VLC system based on blue micro-light-emitting diodes (micro-LED) is proposed and experimentally demonstrated. Benefiting from the advantage of high electrical-to-optical (E-O) bandwidth of the micro-LED, the frequency-response measurements show that the 3-dB modulation bandwidth of 2 m free-space single-hop link is 880 MHz, and the dual-hop system can reach to 715 MHz over a 4 m communication distance. We then investigated the communication performance of our proposed single-hop and dual-hop systems. The real-time waveforms are analyzed at different positions of the dual-hop link and eye diagrams at the receiving terminal are captured for evaluation. Furthermore, the bit error rate (BER) at the target node is measured. The results demonstrate that a 1.1 Gbps on-off keying (OOK) signal with a BER less than the forward-error-correction (FEC) limit could be achieved over a 4 m NLOS free-space link. This work shows that the proposed dual-hop system based on a micro-LED can meet the requirements for most indoor NLOS-transmission scenarios.

At the present time, most of existing security systems only detect and track targets in line-of-sight (LOS). However, in practice, the locations of targets are often out of the line of sight. This article focuses on the non-line-of-sight (NLOS) moving target detection with low-power transmission signals by reflection. There are two key problems, the weak target echo signal and the multipath effect. In terms of the issues, this paper constructs the echo signal model of the NLOS target. On the basis of the echo model, the detection method of NLOS moving target based on millimeter-wave radar comes up, which is of great theoretical value and important practical significance for indoor security. This paper innovatively applies the polynomial fitting method to suppress static noise and range gating method to suppress noise from other range gates. Then, the location and velocity of the target are estimated by two-dimensional fast Fourier transform (FFT) and the multiple signal classification (MUSIC) method. Furthermore, in order to verify the accuracy of the NLOS target echo signal model proposed in this paper, we respectively simulated two important parts of the signal in the model, the target echo signal and the direct backscattered signal of the intermediate interface, both of which are multipath signals. We counted the echo path length distribution in these two parts, and applied the NLOS target detection method to process them respectively. In addition, we also simulated the NLOS target echo signal and obtained actual data in the actual scene, and processed both the simulated data and the actual data. Comparing the results of target detection with and without denoising methods, the effectiveness of the two denoising methods proposed in this paper is verified.

This paper studies the problem of detecting human beings in non-line-of-sight (NLOS) conditions using an ultra-wideband radar. We perform an extensive measurement campaign in realistic environments, considering different body orientations, the obstacles’ materials, and radar–obstacle distances. We examine two main scenarios according to the radar position: (i) placed on top of a mobile cart; (ii) handheld at different heights. We empirically analyze and compare several input representations and machine learning (ML) methods—supervised and unsupervised, symbolic and non-symbolic—according to both their accuracy in detecting NLOS human beings and their adaptability to unseen cases. Our study proves the effectiveness and flexibility of modern ML techniques, avoiding environment-specific configurations and benefiting from knowledge transference. Unlike traditional TLC approaches, ML allows for generalization, overcoming limits due to unknown or only partially known observation models and insufficient labeled data, which usually occur in emergencies or in the presence of time/cost constraints.

Accurate ranging and localization in non-line-of-sight (NLOS) conditions is an open research problem. In indoor environments, NLOS causes ranging and location errors due to reflections, refraction, and other propagation phenomena. This article proposes a method to mitigate ranging errors caused by through-the-wall (TTW) NLOS conditions. We develop a TTW ranging model that has less parameters than the conventional ranging model found in the literature and evaluate it using measurements from ultrawideband radios. The developed ranging model expresses the difference between NLOS and line-of-sight (LOS) ranges, i.e., bias, in terms of walls’ relative permittivity and thickness. NLOS ranges expressed with the proposed TTW ranging model are used as input to a trilateration algorithm. The proposed method is evaluated via simulations, and it is shown that localization using the simplified ranging model as input to the trilateration algorithm effectively mitigates NLOS ranging errors, resulting in location estimates that are close to LOS location estimates. [ABSTRACT FROM AUTHOR]

In this paper, we examine the combined impacts of distance-dependent Rician fading channel model and the absolute difference between the heights of base station (BS) and user equipment (UE) antennas on the coverage probability and the area spectral efficiency in an interference-limited ultra-dense (UD) small cell network (SCN). Exploiting distance dependent models for both path loss and multi-path fading, we show that in interference-limited UD-SCNs, Rician fading with variant Rician K factor aggravates the performance loss caused by the difference between the heights of the BS and UE antennas in comparison to Rayleigh fading. In particular, we demonstrate that due to presence of the specular line-of-sight component in the Rician fading, both the coverage probability and the area spectral efficiency experience a steeper decline towards zero as the BS density increases. Our performance analysis has a prominent impact on the deployment of UD-SCNs in the 5th-generation of mobile networks, as it indicates that the right modeling of multi-path fading makes a significant difference when assessing the performance of UD-SCNs with non-identical UE-BS antenna heights.

The time-sensitive network paradigm envisions the integration of operation technology and information technology in the same network. One of the requirements for building time-sensitive networks is sharing a global time along the network. This requirement is especially critical in wireless systems, where there are few robust methods to perform accurate time transfer. In this article, the problem of time transfer over realistic wireless channels is studied, and a time distribution scheme is proposed. The time distribution scheme has three components: Precision time protocol, a novel timestamping method (enhanced timestamps) and an algorithm to implement the enhanced timestamps. The performance of the proposed scheme has been evaluated in MATLAB using the IEEE 802.11n Standard under several standard wireless local area network channel models. The results show that the system can reach subnanosecond time transfer accuracy under non-line-of-sight and time-variant conditions, but its performance greatly depends on the signal-to-noise ratio and on the channel variation rate. [ABSTRACT FROM AUTHOR]