Non-line-of-sight Environments Research Articles

Fingerprint-Based Localization Enabled by Low-Rank Matrix Reconstruction in Intelligent Reflective Surface-Assisted Networks

The intelligent reflective surface (IRS) is a novel network node that consists of a large-scale passive reflective array to obtain a customized reflected wave direction by modulating the amplitude phase, which can be easily deployed to change the wireless signal propagation environment and enhance the communication performance under a non-line-of-sight (NLOS) environment, where location services cannot perform accurately. In this study, a low-rank matrix reconstruction-enabled fingerprint-based localization algorithm for IRS-assisted networks is proposed. Firstly, a 5G positioning system based on IRSs is constructed using multiple IRSs deployed to reflect signals. This enables the base station to overcome the influence of NLOS and receive the positioning signal of the point to be positioned. Then, the angular domain power expectation matrix of the received signal is extracted as a fingerprint to form a partial fingerprint database. Next, the complete fingerprint database is reconstructed using the low-rank matrix fitting algorithm, thereby considerably reducing the workload of building the fingerprint database. Finally, maximal ratio combining is used to increase the gap between the fingerprint data, and the Weighted K-Nearest Neighbor (WKNN) algorithm is used to match the fingerprint data and estimate the location of the points to be located. The simulation results demonstrate the feasibility of the proposed method to achieve sub-meter accuracy in an NLOS environment.

An NLOS Ranging Error Mitigation Method for 5G Positioning in Indoor Environments

Positioning based on wireless signals such as mobile communication networks has become an important means to provide high-precision location services in environments where satellite signals are blocked. In complex environments such as indoors and underground, wireless signal propagation is obstructed and non-line-of-sight (NLOS) phenomena appear due to serious occlusion and reflection. The time delay caused by NLOS effects has little impact on communication system but can significantly increase positioning errors in positioning systems. Therefore, the effective suppression of NLOS errors is crucial to improving 5G positioning accuracy. To address the insufficient feature extraction of existing NLOS error suppression methods, the neglect of residual NLOS measurement errors, and poor stability of position estimation results, this paper innovatively proposes an NLOS mitigation and location estimation method for 5G positioning terminals. Simulation and experimental test results demonstrate that the proposed method outperforms the comparative methods both theoretically and practically, achieving an average positioning accuracy of 1.85 m in complex indoor NLOS environments. The method proposed in this paper provides a new strategy for NLOS error suppression in indoor 5G positioning, which can significantly contribute to high-precision location services based on commercial 5G networks.

LOS compensation and trusted NLOS recognition assisted WiFi RTT indoor positioning algorithm

In previous studies, ranging errors in the line of sight (LOS) environment were often overlooked, resulting in an increase in positioning errors. In non-line of sight (NLOS) conditions, the measuring distance hardly participates in the location calculation, leading to the loss of useful positioning information. Thus, this paper proposes a WiFi RTT positioning approach based on LOS compensation and trusted NLOS recognition, which improves the performance of RTT positioning by using the compensated LOS distances and trusted NLOS ranges to estimate location. In the proposed approach, NLOS and LOS identification need to be implemented first. A support vector machine algorithm is used to construct a real-time model that recognizes NLOS and LOS distances based on the proposed classification features. Then, the distances under NLOS and LOS environments are evaluated and compensated, respectively, to obtain reliable NLOS ranges and calibrated LOS measurements. A least squares algorithm is utilized to build the LOS distance compensation model, and Bayesian theorem is applied to recognize the trusted NLOS ranges. The compensated LOS distances and credible NLOS ranges are used for estimating the location. The experimental results demonstrate that the proposed algorithm achieves excellent positioning accuracy, with a mean absolute error of 1.082 m. This signifies a substantial enhancement of 53.34% when compared to the least squares algorithm, a remarkable 60.24% improvement over the weighted centroid positioning algorithm, a significant 36.11% progress in comparison to the RTNLIA algorithm, and a noteworthy 32.82% boost relative to the NLRNB approach.

User pairing impact on the performance of hybrid beamforming NOMA system

This paper proposes a new user pairing technique for a power domain nonorthogonal multiple access (NOMA) deploying fully connected and subconnected hybrid beamforming (HBF) structures for a typical urban microcell downlink of line-of-sight (LOS) and non-line-of-sight (NLOS) surroundings. NOMA system’s configurations are set up for two (multiple input single output) users per cluster down- linking base station equipped with 128 antennas. HBF processing adopts phased zero forcing (P-ZF) for both fully connected (HBFNOMA) structure (FCS) and sub-connected (HBF-NOMA) structure (SCS) precoders’ optimization, and successive interference cancellation zero forcing (SIC-ZF) schemes to optimize the SCS-HBF-NOMA precoder exploiting dynamic power allocation. The new users’ pairing exploits users’ distance to the base station, namely near and far clusters different from the benchmark angle of arrival- based users’ pairing. The proposed users’ pairing and the precoding schemes’ impact are investigated for finite-resolution HBF structures operating in LOS and NLOS surroundings. The execution under New York University (NYU) mmW channel model is explored for two users in a cluster with different angles of arrival. Results show that the users’ pairing based on AoA performs better than the newly proposed users’ pairing. However, the proposed users’ pairing scheme performs better than their corresponding OMA counterparts, which still make them beneficial for multiple access technique and a scenario, where one of the far cluster users need to access the base station for high data rate service and vice versa. Finally, the modified Liang processing scheme for quantizing SCS-HBF-NOMA precoder is capable of mitigating the quantization error arising from the NLOS environment and the nature of sub-connected (HBF) structure under a low SNR regime.

Joint Active-Passive Beamforming and User Association in IRS-Assisted mmWave Cellular Networks

Intelligent reflecting surfaces (IRSs) are a promising technology for future-generation wireless networks by extending coverage region to blind spots and increasing mmWave propagation paths in non-line of sight environments. User association (UA) in dense millimeter wave (mmWave) networks is vital to characterizing connections among base stations (BSs) and mobile users for load balancing, interference management, and maximizing network utility. However, it has yet to be examined thoroughly in a multi-IRS-aided network. This paper presents a new UA scheme that takes cell interference into account for a multi-cell mmWave cellular network aided with multiple IRSs. We formulate a network spectral efficiency maximization problem by jointly optimizing active beamforming (AB) at the BSs, passive beamforming (PB) at the IRSs, and user-BS association with consideration of the impact of IRSs. We then propose a computationally efficient iterative algorithm based on alternating optimization (AO) to resolve this intractable mixed-integer non-convex problem. A fractional programming technique is used to optimize active beamforming at the BSs and passive beamforming at the IRSs, and a penalization method combined with successive convex programming is applied for UA optimization, which is shown to reach the optimal solution. Simulation results show significant performance improvements obtained by the proposed algorithm, providing higher spectral efficiency compared to several benchmark algorithms, while having a low computational complexity.

Convex Relaxation Approaches to Robust RSS-TOA Based Source Localization in NLOS Environments

For handling unreliable datasets contaminated by the non-line-of-sight (NLOS) bias errors, this correspondence statistically robustifies the traditional least squares type hybrid received signal strength and time-of-arrival location estimator using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> and Huber loss functions. The two robust formulations are then tackled via different convex relaxation approaches. Numerical results are presented to make fair comparisons with the commonly adopted balancing parameter based scheme, demonstrating the positioning accuracy superiority of the proposed methods in various NLOS environments.

Hybrid Indoor Positioning System Based on Acoustic Ranging and Wi-Fi Fingerprinting under NLOS Environments

An accurate indoor positioning system (IPS) for the public has become an essential function with the fast development of smart city-related applications. The performance of the current IPS is limited by the complex indoor environments, the poor performance of smartphone built-in sensors, and time-varying measurement errors of different location sources. This paper introduces a hybrid indoor positioning system (H-IPS) that combines acoustic ranging, Wi-Fi fingerprinting, and low-cost sensors. This system is designed specifically for large-scale indoor environments with non-line-of-sight (NLOS) conditions. To improve the accuracy in estimating pedestrian motion trajectory, a data and model dual-driven (DMDD) model is proposed to integrate the inertial navigation system (INS) mechanization and the deep learning-based speed estimator. Additionally, a double-weighted K-nearest neighbor matching algorithm enhanced the accuracy of Wi-Fi fingerprinting and scene recognition. The detected scene results were then utilized for NLOS detection and estimation of acoustic ranging results. Finally, an adaptive unscented Kalman filter (AUKF) was developed to provide universal positioning performance, which further improved by the Wi-Fi accuracy indicator and acoustic drift estimator. The experimental results demonstrate that the presented H-IPS achieves precise positioning under NLOS scenes, with meter-level accuracy attainable within the coverage range of acoustic signals.

An Adaptive IMU/UWB Fusion Method for NLOS Indoor Positioning and Navigation

Indoor positioning system (IPS) plays an important role in the applications of Internet of Things (IoT), including intelligent hospital, logistics, and warehousing. Ultra-wideband (UWB) based IPS has shown superior performance due to its strong multipath resistance and high temporal resolution. However, the non-line-of-sight (NLOS) situations noticeably degrade both the positioning accuracy and the communication reliability. To address this issue, we first propose a support vector machine (SVM) based channel detection method to distinguish the line-of-sight (LOS) and NLOS conditions. Then, one base station (BS) based distance and angle positioning algorithm with extended Kalman filter (DAPA-EKF) in NLOS environment is proposed. For the LOS environment, least squares (LSs) with EKF processing of acceleration (LS-AEKF) and velocity (LS-VEKF) are developed. To further improve the performance, the combination of time difference of arrival (TDOA) and KF in LOS environment is proposed. Simulation results show that the positioning accuracy of the proposed algorithm is improved in various environments. Finally, validated using more than 1000 testing positions, the positioning accuracy of LS-AEKF is 73.8% to 74.1% higher than that of LS-VEKF among the two proposed algorithms in terms of 3 or 4 BSs metrics.

Spatial Multi-Source Information Fusion Localization Algorithm in Non-Line-of-Sight Environments

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.

A robust indoor localization algorithm based on polynomial fitting and Gaussian mixed model

Wireless sensor network (WSN) positioning has a good effect on indoor positioning, so it has received extensive attention in the field of positioning. Non-line-of sight (NLOS) is a primary challenge in indoor complex environment. In this paper, a robust localization algorithm based on Gaussian mixture model and fitting polynomial is proposed to solve the problem of NLOS error. Firstly, fitting polynomials are used to predict the measured values. The residuals of predicted and measured values are clustered by Gaussian mixture model (GMM). The LOS probability and NLOS probability are calculated according to the clustering centers. The measured values are filtered by Kalman filter (KF), variable parameter unscented Kalman filter (VPUKF) and variable parameter particle filter (VPPF) in turn. The distance value processed by KF and VPUKF and the distance value processed by KF, VPUKF and VPPF are combined according to probability. Finally, the maximum likelihood method is used to calculate the position coordinate estimation. Through simulation comparison, the proposed algorithm has better positioning accuracy than several comparison algorithms in this paper. And it shows strong robustness in strong NLOS environment.

Strong Tracking Particle Filter Based on the Chi-Square Test for Indoor燩ositioning

In recent years, a number of wireless indoor positioning (WIP), such as Bluetooth, Wi-Fi, and Ultra-Wideband (UWB) technologies, are emerging. However, the indoor environment is complex and changeable. Walls, pillars, and even pedestrians may block wireless signals and produce non-line-of-sight (NLOS) deviations, resulting in decreased positioning accuracy and the inability to provide people with real-time continuous indoor positioning. This work proposed a strong tracking particle filter based on the chi-square test (SPFC) for indoor positioning. SPFC can fuse indoor wireless signals and the information of the inertial sensing unit (IMU) in the smartphone and detect the NLOS deviation through the chi-square test to avoid the influence of the NLOS deviation on the final positioning result. Simulation experiment results show that the proposed SPFC can reduce the positioning error by 15.1% and 12.3% compared with existing fusion positioning systems in the LOS and NLOS environment.

Research on UWB Indoor Positioning Algorithm under the Influence of Human Occlusion and Spatial NLOS

Ultra-wideband (UWB) time-of-flight (TOF)-based ranging information in a non-line-of-sight (NLOS) environment can display significant forward errors, which directly affect positioning performance. NLOS has been a major factor limiting the improvement of UWB positioning accuracy and its application in complex scenarios. Therefore, in order to weaken the influence of the indoor complex environment on the NLOS environment of UWB and to further improve the performance of positioning, in this paper, we first analyze the factors and characteristics of NLOS formation in an indoor environment. The NLOS is divided into fixed NLOS influenced by spatial structure and dynamic random NLOS influenced by human occlusion. Then, the anchor LOS/NLOS information map is established by making full use of indoor spatial a priori information. On this basis, a robust adaptive extended Kalman filtering algorithm based on the anchor LOS/NLOS information map is designed, which is not only effectively able to exclude the influence of spatial NLOS, but can also optimize the random error. The proposed algorithm was validated in different experimental scenarios. The experimental results show that the positioning accuracy is better than 0.32 m in complex indoor NLOS environments.

Study on the UWB location algorithm in the NLOS environment

At present, people spend most of their time indoors, so it is necessary to study high-precision positioning. Ultra wide band (UWB) can obtain the ranging accuracy with centimeter-level error. However, since the indoor environment is more complex than the outdoor environment, positioning errors tend to be generated during the UWB positioning due to the influence of non-line of sight (NLOS). Therefore, this paper investigates how to identify the NLOS environment and reduce the NLOS error. This paper proposes a method to determine the line of sight (LOS) environment credibility and uses it to determine the NLOS environment. The data with NLOS error is determined according to the standard deviation of the residual between the square of the predicted ranging value and the square of the measured ranging value. Then the data with NLOS error is compensated by complementary filtering. Finally, the compensated measurement data and the optimal estimation of the state at the previous moment are imported into the Kalman filter (KF) to determine the optimal estimation at the current moment.

Hybrid TOA/AOA Virtual Station Localization Based on Scattering Signal Identification for GNSS-Denied Urban or Indoor NLOS Environments

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.

Robust TDOA localization based on maximum correntropy criterion with variable center

In this paper, we propose a new robust method for the time-difference-of-arrival (TDOA) localization of a source under non-line-of-sight (NLOS) conditions. According to the maximum correntropy criterion with variable center (MCC-VC), the method jointly estimates the source position together with the kernel center and bandwidth using the TDOA measurements. In particular, we formulate two optimization problems based on MCC-VC, one estimating the kernel center and bandwidth, and the other the source position. The first problem is solved by dividing it into two subproblems, which are further approximated by convex problems that can be easily solved by a convex optimization package. The second problem is also divided into two subproblems by invoking the property of the convex conjugate function, which is then solved iteratively by solving the two subproblems in an alternating manner for each iteration. The proposed method does not require any prior information on the NLOS errors, making it easy to apply. Both simulation and real experimental results show that the proposed method performs well in both sparse and dense NLOS environments.

Simultaneous Localization of Scatterers and Target User Based on Indoor Prior Information in NLOS Environments

In complex indoor environments, there is often no Line of Sight (LOS) path between the transmitter and the receiver due to walls, moving people, and many other obstacles. Traditional localization method based on LOS measurement is no longer available which provides lower localization accuracy in Non-Line of Sight (NLOS) environments. In response to this problem, this paper proposes a joint localization algorithm for scatterers and target user based on indoor prior information in NLOS scenarios. The algorithm makes use of the prior localization information of the access point (AP) and the wall to determine the position of the scatterers and target user simultaneously. Since the established constrained optimization problem is non-convex which is a difference of convex (DC) problem, it is transformed into a convex one based on a Taylor series expansion (TSE) method before it is solved. At the same time, as only a single AP signal can be received normally, the proposed method is then extended to the scenario with only one AP. Simulation and real-world testing results both show that not only the proposed method based on multiple APs, but also the method based on a single AP achieve high positioning accuracy, which are better than the existing positioning methods mentioned in this article as well.

A Succinct Method for Non-Line-of-Sight Mitigation for Ultra-Wideband Indoor Positioning System.

Ultra-wideband (UWB) is a promising indoor position technology with centimetre-level positioning accuracy in line-of-sight (LOS) situations. However, walls and other obstacles are common in an indoor environment, which can introduce non-line-of-sight (NLOS) and deteriorate UWB positioning accuracy to the meter level. This paper proposed a succinct method to identify NLOS induced by walls and mitigate the error for improved UWB positioning with NLOS. First, NLOS is detected by a sliding window method, which can identify approximately 90% of NLOS cases in a harsh indoor environment. Then, a delay model is designed to mitigate the error of the UWB signal propagating through a wall. Finally, all the distance measurements, including LOS and NLOS, are used to calculate the mobile UWB tag position with ordinary least squares (OLS) or weighted least squares (WLS). Experiment results show that with correct NLOS indentation and delay model, the proposed method can achieve positioning accuracy in NLOS environments close to the level of LOS. Compared with OLS, WLS can further optimise the positioning results. Correct NLOS indentation, accurate delay model and proper weights in the WLS are the keys to accurate UWB positioning in NLOS environments.

Optimization of ultraviolet communication links based on finite difference stochastic approximation.

The ultraviolet communication (UV) channel has been shown to have unique features that could be exploited for covert ground-to-ground communications in complex non-line-of-sight (NLOS) scenarios. A key challenge is the determination of optimal configuration of pointing directions of the UV nodes in unknown NLOS environments to maximize the link performance. In this paper, we proposed a novel steering optimization approach based on Finite Difference Stochastic Approximation (FDSA) to simultaneously optimize the transmitter (Tx) and receiver (Rx) pointing directions without any knowledge about the locations and relative orientations of the two nodes. We perform parametric analysis using Monte Carlo channel simulations to investigate and select appropriate key algorithmic parameters and analyze the performance of the proposed algorithm. We also carry out experimentation using our custom designed UV Tx and Rx gimbal systems and demonstrate the utility and efficiency of the proposed steering optimization approach and show that the received photon count can be increased significantly.

Wireless Sensor Network-Based Rigid Body Localization for NLOS Parameter Estimation.

In wireless sensor network (WSN)-based rigid body localization (RBL) systems, the non-line-of-sight (NLOS) propagation of the wireless signals leads to severe performance deterioration. This paper focuses on the RBL problem under the NLOS environment based on the time of arrival (TOA) measurement between the sensors fixed on the rigid body and the anchors, where the NLOS parameters are estimated to improve the RBL performance. Without any prior information about the NLOS environment, the highly non-linear and non-convex RBL problem is transformed into a difference of convex (DC) programming, which can be solved by using the concave–convex procedure (CCCP) to determine the position of the rigid body sensors and the NLOS parameters. To avoid error accumulation, the obtained NLOS parameters are utilized to refine the localization performance of the rigid body sensors. Then, the accurate position and the orientation of the rigid body in two-Dimensional space are obtained according to the relative deflection angle method. To reduce the computational complexity, the singular value decomposition (SVD) method is employed to solve the problem in three-Dimensional space. Simulation results show that the proposed method can effectively improve the performance of the rigid body localization based on the wireless sensor network in NLOS environment.

Simultaneous WiFi Ranging Compensation and Localization for Indoor NLoS Environments

Smartphone-based WiFi ranging using fine time measurement (FTM) is severely impacted by Non-line-of-sight (NLoS) environments, which causes significant positioning errors. To address this problem, we propose a novel WiFi FTM positioning (WFP) approach based on the geomagnetism and enhanced genetic algorithm (EGA), which can simultaneously execute WiFi localization and ranging compensation. Based on the distribution of the ranging error in NLoS environments, a semiparametric error model-based ranging compensation method is proposed. To construct the EGA searching model, geomagnetism-based positioning is adopted and fed to the EGA together with the measured WiFi ranging data and the ranging compensation method. During online localization, the EGA model dynamically compensates for the erroneous ranging data until it finds the optimal position. Experimental results show that the ranging and localization accuracy of this EGA-based WFP are 1.33 m and 1.64 m, being an improvement of 30.7% and 56.5% compared to the uncompensated ranging data and the trilateration algorithm using the weighted least square (WLS) method, respectively.