Non-line-of-sight Environments Research Articles

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.

Multipath Cluster-Assisted Single Station Localization Based on SSA-GA in Outdoor NLOS Environment

In this paper, we propose a novel multipath cluster-assisted single station localization method based on a genetic algorithm-based improved salp swarm algorithm (SSA-GA) to improve localization accuracy in an outdoor non-line-of-sight (NLOS) propagation environment. The scattering area model is presented which scatterers are considered Gaussian distribution for outdoor NLOS environments. The geometrical properties of propagation paths, such as angle of arrival and time of arrival, are jointly utilized to construct pseudoscatterer distribution. In order to filter the interference scatterers distributed outside the scattering region, the Gaussian kernel-based algorithm is developed. Furthermore, SSA-GA is proposed to solve the positioning objective functions constructed by pseudoscatterers clustering accurately. Results confirm the practicability of our newly proposed method, and the positioning error is less than 5% in outdoor NLOS propagation environment.

An accurate and reliable positioning methodology for land vehicles in tunnels based on UWB/INS integration

At present, the global navigation satellite system (GNSS) is the most widely used technology in the field of vehicle navigation and positioning. However, due to satellite signal blocking, it can be challenging to obtain precise and reliable vehicle positions in completely GNSS-denied environments. In this paper, a positioning strategy utilizing ultra-wide band (UWB) and a low-cost MEMS inertial navigation system (INS) is proposed, aimed at tracking vehicles in typical GNSS-denied scenarios. UWB tech has potential for high-precision ranging due to its strong penetration ability. However, non-line-of-sight (NLOS) propagation still has a high occurrence in typical traffic scenarios due to the fact that the direct path between the radio transmitter and the radio receiver is obstructed. Therefore, a two-step NLOS error mitigation method is proposed to deal with the abnormal measurements of UWB in tunnels. First, a state-of-the-art probabilistic factor graph model is proposed to estimate the vehicle’s position information based on UWB range measurements instead of excluding the NLOS reception. Then, a residual weighting algorithm is employed during UWB/INS loosely coupled integration to further mitigate the NLOS error. Finally, the performance of the proposed methodology is evaluated through experiments. Experiments conducted in the typical NLOS environment of Nanjing show that the proposed method can achieve higher positioning accuracy when compared with the conventional UWB/INS integration.

Moving Target Tracking Algorithm Based on Improved Resampling Particle Filter in UWB Environment

In this paper, a moving target tracking (MTT) algorithm based on the improved resampling particle filter (IRPF) was put forward for the reduced accuracy of particle filter (PF) due to the lack of particle diversity resulting from traditional resampling methods. In this algorithm, the influences of the likelihood probability distribution of particles on the PF accuracy were firstly analyzed to stratify the adaptive regions of particles, and a particle diversity measurement index based on stratification was proposed. After that, a threshold was set for the particle diversity after resampling. If the particle diversity failed to reach the set threshold, all new particles would be subjected to a Gaussian random walk in a preset variance matrix to improve the particle diversity. Finally, the performance of related algorithms was tested in both simulation environment and actual indoor ultrawideband (UWB) nonline-of-sight (NLOS) environment. The experimental results revealed that the nonlinear target state estimation accuracy was maximally and minimally improved by 12.83% and 1.97%, respectively, in the simulation environment, and the root mean square error (RMSE) of MTT was reduced from 17.131 cm to 10.471 cm in actual UWB NLOS environment, indicating that the IRPF algorithm can enhance the target estimation accuracy and state tracking capability, manifesting better filter performance.

Improving the Positioning Accuracy of UWB System for Complicated Underground NLOS Environments

The achievement of accurate and reliable localization is invariably a predominant bottleneck to the realization of shearer automation. Unfortunately, the positioning accuracy may significantly degrade and the yield may be unsatisfactory due to the existence of the line-of-sight (LOS), non-line-of-sight (NLOS) along with mixed LOS-NLOS situations in complicated under-ground environments. To address this issue, this article proposes a novel localization algorithm called GMM-NNIMM-CLMFO, which is a combination of the Gaussian mixture model (GMM), neural network (NN)-based interacting multiple model (NNIMM), Caffery localization (CL), and moth-flame optimization (MFO). The GMM is first used to re-estimate the range, and the variational Bayesian cubature Kalman filter with the assistance of NN under the IMM framework is then respectively employed to diminish the LOS and NLOS errors. Then, based on the result of GMM-NNIMM, the CL method is used to compute the position coordinates of the target node. Finally, the MFO algorithm is implemented to optimize the positioning outcome sourced from the GMM-NNIMM-CL method. The proposed method is evaluated by using P440 sensors, and the experimental results demonstrate that the proposed technique is able to remarkably enhance the overall positioning accuracy, and outperforms other approaches. It can therefore efficiently alleviate the influence of NLOS errors and achieve more accurate position estimation, thereby manifesting better positioning performance and higher robustness in mixed LOS/NLOS situations.

ALNN‐based LOS/NLOS identification in 3D millimetre wave channel

Line-of-sight (LOS)/non-line-of-sight (NLOS) identification is crucial for millimetre-wave (mmW) since it is vulnerable to blocking in the NLOS environment. In this paper, an LOS/NLOS environment identification method is proposed based on angle information learning. Specifically, a neural network named ALNN (angle information learning neural network) is employed, which can learn the difference between the various angle paraments to identify the LOS and NLOS environments for mmW wireless communication systems. First, the 3D mmW channel model is applied to extract the desired angle information. Moreover, an mmW wireless transmission system is constructed to introduce actual measurement data to test the proposed method. Furthermore, various hyperparameters of the ALNN are adjusted to improve identification accuracy. Simulation results show that the proposed method has outstanding identification performance, and the identification accuracy is up to 99.41%.

Acoustic Signal NLOS Identification Method Based on Swarm Intelligence Optimization SVM for Indoor Acoustic Localization

The demand for an indoor localization system is increasing, and related research is also becoming more universal. Previous works on indoor localization systems mainly focus on the acoustic signals in Line of Sight (LOS) scenario to obtain accurate localization information, but their effectiveness in Nonline of Sight (NLOS) scenario remains comparatively untouched. These works are usually less efficient as the acoustic signals often bring diffraction, refraction, scattering, energy decays, and so on in NLOS environments. So the system needs adjusting accordingly in a complex NLOS scenario based on NLOS identification results. Therefore, the identification of NLOS acoustic signal turns out to be significant in the indoor localization system. If the system only uses original support vector machine (SVM) to complete NLOS identification, the result turns out to be poor by our test. To address this challenge, we propose a novel indoor localization system, named ZKLocPro, which utilizes an advanced swarm intelligence method to optimize the traditional SVM classification model to deal with NLOS acoustic signal identification. Its results can help the system adjust the localization process if necessary in a complex NLOS scenario. Obviously, it is also significant to build our own NLOS data set, which is suitable for an indoor localization system’s situation. Specifically, four methods are added: (1) new LOS and NLOS acoustic localization signal sample production, rearrangement, and reselecting process; (2) advanced parameter optimization process; (3) elitist strategy; and (4) inertia weight nonlinear decrement. The experimental result shows that our system is efficient and performs better than state-of-the-art congeneric works even in a complex NLOS scenario.

A Majorization-Minimization Algorithm for Hybrid TOA-RSS Based Localization in NLOS Environment

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

A cascaded algorithm based on GMM, GKF, and data fusion for mobile tracking in wireless sensor network

SummaryWireless sensor network (WSN) is formed with numerous communication nodes, which plays an important role in the Internet of Things (IoT). Location‐based service is critically important for WSN; however, the nonline of sight (NLOS) condition can deteriorate the positioning precision significantly. In this paper, a robust localization algorithm based on the range measurements to deal with the mixed line of sight (LOS)/NLOS environment is proposed. Firstly, a hypothesis testing based on the data deviation is used to detect the transmission condition. For the identified LOS propagation, the measured data are processed by Gaussian mixture model (GMM) to calculate the mean and standard deviation, which can update the likelihood probability and residual by data fusion. Moreover, for the identified NLOS propagation data, the mean of every Gaussian component is processed by gray Kalman filter (GKF) to discard large outliers. Finally, maximum likelihood (ML) is adopted to derive final coordinates. The simulation results have shown that the proposed algorithm has great advantages in dealing with severe NLOS interference and has higher accuracy compared with existing classic algorithms.

Robust UWB Indoor Localization for NLOS Scenes via Learning Spatial-Temporal Features

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

Identification and mitigation of non-line-of-sight path effect using repeater forhybrid ultra-wideband positioning and networking system

At least two decades ago, various applications have been proposed for the implementation of ultra-wideband (UWB) technology, but only a few of them are being realised such as radar detection, home networking, and indoor positioning. Although UWB positioning offers precise locality tracking, the accuracy of the estimation is greatly affected by the non-line-of-sight (NLOS) path effect. In this paper, we propose a hybrid indoor UWB positioning and networking system that utilises the existing repeater of the data network to eliminate the NLOS paths. A switching algorithm is written to identify the existence of NLOS paths based on received signal strength (RSS) and unique channel characteristics such as mean excess delay (MED) and root mean square (RMS). From the simulation results, the NLOS paths have been successfully identified under the NLOS environment. Hence, a higher probability of accuracy across the entire tested area can be achieved for the UWB positioning system by mitigating the NLOS path effect using the proposed algorithm. Besides that, the transmission of the line-of-sight (LOS) signal attains a data rate of around 15 times higher than the NLOS signal at a bit-error-rate (BER) of 10$^{-5}$ in the indoor networking system. In this case, the minimum-bit-error-rate (MBER) receiver is again shown to outperform the minimum-mean-square error (MMSE) and the performance gain drops around 5 dB to 6 dB at a BER of 10$^{-5}$ when the repeaters increase to a maximum number of 10 units. In conclusion, the proposed method is capable of mitigating the NLOS path effect on both indoor positioning and data networking systems.

A Novel Strategy for Monitoring a PV Junction Box Based on LoRa in a 3 kW Residential PV System

Recently, 3 kW residential PV (Photovoltaic) junction boxes have mainly been installed on the roof or outer wall of building. Wired and wireless monitoring systems are being implemented by RS-485 and WIFI/IoT. However, conventional monitoring systems have a communication limitation according to the distance and environment. It cannot receive any information when a failure of the PV junction box occurs. Therefore, there is a need for a strategy to determine whether the fuse and diode in the PV junction box are faulty through voltage and current sensors. In this paper, we propose a novel strategy for monitoring PV junction boxes, based on LoRa (Long Range). The TTGO LoRa32 V2.0 module with LoRa and various input and output ports is utilized. The wireless TX module transmits various data collected from the PV junction box to the RX module in real time. In addition, the RX module displays the received data on an LCD (Liquid Crystal Display) screen so that the user can intuitively identify it, and the data is recorded on the internal storage device or database in the web server. The manufactured PV junction box monitoring system was tested under a 3 kW PV system. Additionally, communication reception has stable signal intensity overall, both indoors and outdoors. In particular, it shows excellent characteristics in maintaining RSSI (Received Signal Strength Indicator) > −99 dBm and PER (Packet Error Rate) < 2.7%, up to a radius of 200 m, even in NLOS (Non Line-Of-Sight) environments. Although some packet loss occurred, it was confirmed that invisible communication was possible up to 300 m.

An Adaptive Weighted Wi-Fi FTM-Based Positioning Method in an NLOS Environment

In the field of indoor positioning, Wi-Fi FTM is a new technology for realizing high-precision positioning. However, errors caused by clock drift and non-line-of-sight (NLOS) signals affect its positioning accuracy. When receiving NLOS signals, most existing positioning algorithms only delete these signals, which decreases the number of nodes and may decrease accuracy. To address this issue, this paper proposed an adapted weighted positioning method under the NLOS environment. First, this method includes a compensation model to decrease the error caused by clock drift and multipath. Additionally, it can evaluate ranging results and improving the positioning accuracy by assigning greater weight to better ranging results. To verify the effectiveness and feasibility of the proposed method, a positioning experiment is performed under an NLOS environment. The results show that our proposed method is suitable for positioning in a completely NLOS environment and effectively improves the positioning accuracy. Compared with the traditional least squares-based method and the inverse distance weighting-based positioning method, the mean error of the proposed method outperformed by approximately 30% and 20% respectively.

A Robust Indoor Localization Method for NLOS Environments Utilizing Sensor Subsets

Indoor location localization using the time of arrival (TOA) of ultra-wideband (UWB) radio waves faces a challenge in obtaining accurate TOA values in a non-line-of-sight (NLOS) environment owing to obstacles and multipath reception. The accuracy of localization using such sensor data in an NLOS environment is significantly degraded. To address this problem in NLOS environment, we proposed a new localization method that utilizes different sensor combinations for estimation and sensor placement topology. Based on the conventional localization method using various combinations of sensors, the estimation results containing an NLOS ranging value were first removed. Subsequently, we introduced a new parameter called the topological expectancy of localization accuracy (TELA), which represents the effect of sensor placement on the accuracy of localization. The final estimation result was then obtained using the weighted average of the remaining localization results obtained by TELA. Computer simulations and experimental results showed that the performance of the proposed method is superior by 4 to 37% compared with that of conventional methods, even in the presence of many NLOS environment sensors.

Detection of Human Breathing in Non-Line-of-Sight Region by Using mmWave FMCW Radar

Detection of an actionless human hidden in the Non-Line-of-Sight (NLOS) region can be implemented by combining NLOS object detection and vital sign detection. Since the radar waves suffer from severe multipath effects in NLOS environment, a specific detection scheme is required for human position estimation and breathing detection with 77GHz frequency modulated continuous wave (FMCW) radar. After a radar surveying of the NLOS environment, local coordinates and the radar wave reflection and propagation model can be established on-site. An NLOS target positioning algorithm, referred to as the NLOS multipath target searching and position estimating algorithm (NLOS-mTSPE), is proposed to recognize the typical wave propagation paths in the NLOS area for accurate positioning of the human in the NLOS region. According to the recognized signal path, human breathing signals can be correctly extracted and the breathing rate can be acquired with an average error within 1%. The proposed scheme is demonstrated by the detection in a corridor behind an L-shaped corner, in which both the accurate human position and breathing rate can be acquired. The maximum detectable distance and the maximum localizable distance in the NLOS region are analyzed.