Non-line-of-sight Measurements Research Articles

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.

NLOS Identification- and Correction-Focused Fusion of UWB and LiDAR-SLAM Based on Factor Graph Optimization for High-Precision Positioning with Reduced Drift

In this study, we propose a tightly coupled integrated method of ultrawideband (UWB) and light detection and ranging (LiDAR)-based simultaneous localization and mapping (SLAM) for global navigation satellite system (GNSS)-denied environments to achieve high-precision positioning with reduced drift. Specifically, we focus on non-line-of-sight (NLOS) identification and correction. In previous work, we utilized laser point cloud maps to identify and exclude NLOS measurements in real time to attenuate their severe effects on the integrated system. However, the complete exclusion of NLOS measurements will likely lead to deterioration in the dilution of precision (DOP) for the remaining line-of-sight (LOS) anchors, counterproductively introducing large positioning errors into the integrated system. Therefore, this study considers the ranging accuracy and geometric distribution of UWB anchors and innovatively proposes an NLOS correction method using a grey prediction model. For a poor line-of-sight (LOS) anchor geometric distribution, the grey prediction model is used to fill in the gaps by predicting the NLOS measurements based on historical measurements. Including the corrected measurements effectively improves the original poor geometric configuration, improving the system positioning accuracy. Since conventional filtering-based fusion methods are exceedingly sensitive to measurement outliers, we use state-of-the-art factor graph optimization (FGO) to tightly integrate the UWB measurements (LOS and corrected measurements) with LiDAR-SLAM. The temporal correlation between measurements and the redundant system measurements effectively enhance the robustness of the integrated system. Experimental results show that the tightly coupled integrated method combining NLOS correction and FGO improves the positioning accuracy under a poor geometric distribution, increases the system availability, and achieves better positioning than filtering-based fusion methods with a root-mean-square error of 0.086 m in the plane direction, achieving subdecimeter indoor high-precision positioning.

Improved-UWB/LiDAR-SLAM Tightly Coupled Positioning System with NLOS Identification Using a LiDAR Point Cloud in GNSS-Denied Environments

Reliable absolute positioning is indispensable in long-term positioning systems. Although simultaneous localization and mapping based on light detection and ranging (LiDAR-SLAM) is effective in global navigation satellite system (GNSS)-denied environments, it can provide only local positioning results, with error divergence over distance. Ultrawideband (UWB) technology is an effective alternative; however, non-line-of-sight (NLOS) propagation in complex indoor environments severely affects the precision of UWB positioning, and LiDAR-SLAM typically provides more robust results under such conditions. For robust and high-precision positioning, we propose an improved-UWB/LiDAR-SLAM tightly coupled (TC) integrated algorithm. This method is the first to combine a LiDAR point cloud map generated via LiDAR-SLAM with position information from UWB anchors to distinguish between line-of-sight (LOS) and NLOS measurements through obstacle detection and NLOS identification (NI) in real time. Additionally, to alleviate positioning error accumulation in long-term SLAM, an improved-UWB/LiDAR-SLAM TC positioning model is constructed using UWB LOS measurements and LiDAR-SLAM positioning information. Parameter solving using a robust extended Kalman filter (REKF) to suppress the effect of UWB gross errors improves the robustness and positioning performance of the integrated system. Experimental results show that the proposed NI method using the LiDAR point cloud can efficiently and accurately identify UWB NLOS errors to improve the performance of UWB ranging and positioning in real scenarios. The TC integrated method combining NI and REKF achieves better positioning effectiveness and robustness than other comparative methods and satisfactory control of sensor errors with a root-mean-square error of 0.094 m, realizing subdecimeter indoor positioning.

Multiple-Model Mobile Localization Based on Unscented Kalman Filter in Mixed Indoor

In this paper, we present an Interactive multiple mode-Unscented Kalman filter (IMM-UKF) algorithm to achieve mobile node location under wireless sensor networks environments. In the IMM structure, UKF and Variational Bayesian Adaptive Method based on UKF are adapted in parallel, which can improve positioning accuracy in the process of line-of sight (LOS) and non-line-of-sight (NLOS) signal state switching. The estimated values by filtering are fused according to the weighting factors to get the estimated positions. Moreover, when NLOS measurement noise covariance change, we propose Variational Bayesian Adaptive Method based on UKF to improve robustness. Both Simulation and experiments illustrate that the propose algorithm performs can achieve competitive localization accuracy.

TOA NLOS mitigation cooperative localisation algorithm based on topological unit

The accuracy of cooperative localisation can be severely degraded in non-line-of-sight (NLOS) environments. To mitigate the NLOS errors, the cooperative localisation problem based on time of arrival (TOA) under the mixed line-of-sight (LOS)/NLOS conditions is addressed. By studying the topological relationship between nodes, a TOA NLOS mitigation cooperative localisation algorithm based on the topological unit is proposed. This algorithm is implemented under the classical multidimensional scaling framework. The adjacent topological unit of NLOS measurements are successfully identified by using the LOS matrix, and the NLOS measurements are re-estimated using topological units. The least-square method is used to transform the relative coordinates into absolute coordinates depending on the location of the anchor nodes. Compared to the existing methods, by employing the topological unit, this algorithm only requires the number of LOS anchor nodes to be 2 in the two-dimensional plane, and the better localisation performance can be achieved. Simulation results show that the proposed method works well for both the sparse and dense NLOS environments.

How NLOS Signals affect GNSS relative positioning

Owing to the existence of non-line-of-sight (NLOS) signals in global navigation satellite system (GNSS) challenged environment, the accuracy of GNSS relative positioning is seriously damaged. This paper demonstrates the effects of NLOS measurement error on GNSS relative positioning for short-baseline in both theory and field test. The results show that the effects of NLOS signal in both pseudorange and carrier phase make the ambiguity unable to be successfully fixed. In addition, we have found that after removing the labelled NLOS observations, the positioning accuracy can be significantly improved. The more the available LOS satellites, the more the robustness against NLOS satellites of the ambiguity resolution. This implies that GNSS Multi-Frequency Multi-System technology might improve the performance of GNSS relative positioning because there would be sufficient LOS signals after the NLOS signals are removed.

A phase-difference-of-arrival assisted ultra-wideband positioning method for elderly care

Ultra-wideband (UWB) is a promising mean for elderly care in line-of-sight (LOS) environments. One major limitation of UWB-based localization is the non-line-of-sight (NLOS) for signals caused by severe environments, resulting in dramatic performance degradation. Although many NLOS identification and compensation methods in presence of complex indoor environments have been proposed, they are only applicable to specific environments or require enough LOS BSs to detect NLOS measurements. To address these issues comprehensively, we propose a phase-difference-of-arrival (PDOA) assisted UWB positioning method (PDOA-UWB) for elderly care in this paper, which consists of three steps: (1) coarse localization, calculated from PDOA measurements at multiple receiver antennas of UWB base stations (BSs), (2) LOS BSs selection, and (3) fine localization through a convex regression estimator. Our method is simple and effective and can be used in various practical environments without prior information about the NLOS error. Overall experimental results verify that PDOA-UWB can achieve better positioning accuracy than existing methods in severe NLOS environments.

Non-Line-of-Sight Identification for UWB Positioning Using Capsule Networks

The accuracy of positioning in ultra-wide band (UWB) systems can be improved by identifying the conditions of line-of-sight (LOS) and non-line-of-sight (NLOS) propagation and taking appropriate measures. At present, machine learning methods have already been extensively applied to NLOS identification. Unfortunately, those traditional methods are incapable of performing well in the cases of small samples and class-imbalance samples. In this letter, a novel method is proposed to identify LOS and NLOS components by utilizing Capsule Networks (CapsNet). As indicated by simulation results, the CapsNet-based method allows LOS and NLOS measurements to be classified with 94.63% accuracy, 95.58% precision, 94.74% recall rate, and a 0.9490 F2-score. In addition, CapsNet is compared against decision tree (DT), least squares vector machine (LS-SVM) and K nearest neighbor (KNN) based on experimental data. The comparison results show that the proposed CapsNet outperforms benchmark methods in the cases of both small samples and class-imbalance samples.

Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons

Robust and globally-referenced positioning is indispensable for autonomous driving vehicles. Global navigation satellite system (GNSS) is still an irreplaceable sensor. Satisfactory accuracy (about 1 m) can be obtained in sparse areas. However, the GNSS positioning error can be up to 100 m in dense urban areas due to the multipath effects and non-line-of-sight (NLOS) receptions caused by reflection and blockage from buildings. NLOS is currently the dominant factor degrading the performance of GNSS positioning. Recently, the camera has been employed to detect the NLOS and then to exclude the NLOS measurements from GNSS calculation. The exclusion of NLOS measurements can cause severe distortion of satellite distribution, due to the excessive NLOS receptions in deep urban canyons. Correcting the NLOS receptions with the aid of 3D light detection and ranging after detection of NLOS receptions using a fish-eye camera was proposed in this study. Finally, the GNSS positioning was improved by using the healthy and corrected NLOS pseudo-range measurements. The proposed method is evaluated through real road tests in typical highly urbanised canyons of Hong Kong. The evaluation results show that the proposed method can effectively improve the positioning performance.

Extending Shadow Matching to Tightly-Coupled GNSS/INS Integration System

Performing precise positioning is still challenging for autonomous driving. Global navigation satellite system (GNSS) performance can be significantly degraded due to the non-line-of-sight (NLOS) reception. Recently, the studies of 3D building model aided (3DMA) GNSS positioning show promising positioning improvements in urban canyons. In this study, the benefits of 3DMA GNSS are further extended to the GNSS/inertial navigation system (INS) integration system. Based on the shadow matching solution and scoring information of candidate positions, two methods are proposed to better classify the line-of-sight (LOS) and NLOS satellite measurements. Aided by the satellite visibility information, the NLOS-induced pseudorange measurement error can be mitigated before fusing GNSS with the INS in the loosely-coupled or tightly-coupled integration system. Both the proposed satellite visibility estimation methods achieve over 80% LOS/NLOS classification accuracy for most of the scenarios in the urban area, which are at least 10% improvement over the carrier-to-noise ratio ( $C/{N_0}$ )-based method. By further extending the satellite visibility estimation to exclude NLOS measurements and adjust the measurement noise covariance, the proposed 3DMA GNSS/INS tightly-coupled integrated positioning achieves nearly a factor of 3 improvements comparing to the conventional GNSS/INS integration method during the vehicular experiment in the urban canyon.

Robust Localization of Mobile Robot in Industrial Environments With Non-Line-of-Sight Situation

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.

Geometry-based non-line-of-sight error mitigation and localization in wireless communications

Recently, positioning services have received considerable attention. The primary source of the positioning error is non-line-of-sight (NLOS) propagation. To address this problem, we propose a novel NLOS mitigation scheme, in which the geometric relationship between a base station and a mobile station is used. This makes it possible to identify range measurements corrupted by NLOS errors, and the mobilestation can then estimate its position through line-of-sight (LOS)measurements. Moreover, the threshold of the NLOS detector is derivedvia a hybrid method using both the analytical derivationand computer simulation, which significantly reduces the difficultyof identifying thresholds. After identifying the NLOS measurements, a two-step weighted-least-squares algorithm is usedto obtain the localization, in which both range and anglemeasurements are considered. The simulation results revealthat the proposed algorithm yields a high identification probabilityof NLOS measurements, which results in improved localizationperformance.

Wideband UHF and SHF long-range channel characterization

This paper presents an outdoor long-range (from 315 m up to 5.3 km) fixed channel campaign for both ultra high frequency and super high frequency bands with co-polarized horizontal and vertical antenna configurations. It investigates the channel characteristics of device to device communication scenarios underlaying the 5th generation networks by providing detailed research. Both line of sight and non-line of sight measurements in 1.3 GHz and 5.8 GHz frequency bands with bandwidth up to 600 MHz were conducted. The path loss, root mean square delay spread, coherence bandwidth, and channel frequency response variation are characterized. We observed that the variation is negligible in microcell line of sight environment for both above mentioned frequencies, whereas it significantly increases with frequency in different macrocell non-line of sight environments. The distance dependency of path loss was also derived. It was observed that the root mean square delay spread decreases with frequency for both line of sight microcell and non-line of sight macrocell measurements. A dependency between the root mean square delay spread and transmitter-receiver distance in non-line of sight environments was also captured. The relation between the coherence bandwidth and the root mean square delay spread was depicted. It demonstrates an exponential function in all considered channel combinations.

Rectification of GNSS-based collaborative positioning using 3D building models in urban areas

GNSS collaborative positioning receives great attention because of the rapid development of vehicle-to-vehicle communication. Its current bottleneck is in urban areas. During the relative positioning using GNSS double-difference pseudorange measurements, the multipath effects and non-line-of-sight (NLOS) reception cannot be eliminated, or even worse, both might be aggregated. It has been widely demonstrated that 3D map aided GNSS can mitigate or even correct the multipath and NLOS effects. We, therefore, investigate the potential of aiding GNSS collaborative positioning using 3D city models. These models are used in two phases. First, the building models are used to exclude NLOS measurements at a single receiver using GNSS shadow matching positioning. Second, the models are used together with broadcast ephemeris data to generate a predicted GNSS positioning error map. Based on this error map, each receiver will be identified as experiencing healthy or degraded conditions. The receiver experiencing degraded condition will be improved by the receiver experiencing the healthy condition, hence the aspect of collaborative positioning. Five low-cost GNSS receivers are used to conduct experiments. According to the result, the positioning accuracy of the receiver in a deep urban area improves from 46.2 to 14.4 m.

Improved MDS-based Localization with Non-line-of-sight RF Links

The performance of indoor localization techniques adopted in robot localization systems that use radio positioning is usually degraded in non-line-of-sight (NLOS) environments. In this paper, we propose a technique for estimating NLOS biases and measurement noise in distances under multidimensional scaling (MDS) based positioning with fixed nodes. An ideal matrix of pairwise distance measurements exhibits a symmetry that allows to compute mobile node positions from those of fixed ones, and then recompute exactly the fixed node positions from the earlier computed mobile node positions. In a NLOS environment, this symmetry is lost; fixed node positions can not be reproduced exactly. This work exploits the error in the recomputation of fixed node positions for the correction of NLOS biases and noise in the pairwise distances. A constrained-optimization problem is formulated to estimate biases for each measured distance and final mobile node positions under the MDS scheme. A supplementary approach is presented for special cases where the number of mobile nodes is less than 3. Experimental results show that position errors can be reduced by up to 28% for a set up of 4 fixed and 3 mobile nodes. Simulations are used to further validate the results for larger deployments of nodes.

A Triple-Filter NLOS Localization Algorithm Based on Fuzzy C-means for Wireless Sensor Networks.

With the rapid development of communication technology in recent years, Wireless Sensor Network (WSN) has become a promising research project. WSN is widely applied in a number of fields such as military, environmental monitoring, space exploration and so on. The non-line-of-sight (NLOS) localization is one of the most essential techniques for WSN. However, the NLOS propagation of WSN is largely influenced by many factors. Hence, a triple filters mixed Kalman Filter (KF) and Unscented Kalman Filter (UKF) voting algorithm based on Fuzzy-C-Means (FCM) and residual analysis (TF-FCM) has been proposed to cope with this problem. Firstly, an NLOS identification algorithm based on residual analysis is used to identify NLOS errors. Then, an NLOS correction algorithm based on voting and NLOS errors classification algorithm based on FCM are used to process the NLOS measurements. Hard NLOS measurements and soft NLOS measurements are classified by FCM classification. Secondly, KF and UKF are applied to filter two categories of NLOS measurements. Thirdly, maximum likelihood localization (ML) is employed to estimate the position of mobile nodes. The simulation result confirms that the accuracy and robustness of TF-FCM are better than IMM, UKF and KF. Finally, an experiment is conducted to test and verify our algorithm which obtains higher localization accuracy.

GPS Signal Reception Classification Using Adaptive Neuro-Fuzzy Inference System

The multipath effect and Non-Line-Of-Sight (NLOS) reception of Global Positioning System (GPS) signals both serve to degrade performance, particularly in urban areas. Although receiver design continues to evolve, residual multipath errors and NLOS signals remain a challenge in built-up areas. It is therefore desirable to identify direct, multipath-affected and NLOS GPS measurements in order improve ranging-based position solutions. The traditional signal strength-based methods to achieve this, however, use a single variable (for example, Signal to Noise Ratio (C/N0)) as the classifier. As this single variable does not completely represent the multipath and NLOS characteristics of the signals, the traditional methods are not robust in the classification of signals received. This paper uses a set of variables derived from the raw GPS measurements together with an algorithm based on an Adaptive Neuro Fuzzy Inference System (ANFIS) to classify direct, multipath-affected and NLOS measurements from GPS. Results from real data show that the proposed method could achieve rates of correct classification of 100%, 91% and 84%, respectively, for LOS, Multipath and NLOS based on a static test with special conditions. These results are superior to the other three state-of-the-art signal reception classification methods.

Enhancement of Localization Systems in NLOS Urban Scenario with Multipath Ray Tracing Fingerprints and Machine Learning.

A hybrid technique is proposed to enhance the localization performance of a time difference of arrival (TDOA) deployed in non-line-of-sight (NLOS) suburban scenario. The idea was to use Machine Learning framework on the dataset, produced by the ray tracing simulation, and the Channel Impulse Response estimation from the real signal received by each sensor. Conventional localization techniques mitigate errors trying to avoid NLOS measurements in processing emitter position, while the proposed method uses the multipath fingerprint information produced by ray tracing (RT) simulation together with calibration emitters to refine a Machine Learning engine, which gives an extra layer of information to improve the emitter position estimation. The ray-tracing fingerprints perform the target localization embedding all the reflection and diffraction in the propagation scenario. A validation campaign was performed and showed the feasibility of the proposed method, provided that the buildings can be appropriately included in the scenario description.

A Novel Non-Line-of-Sight Indoor Localization Method for Wireless Sensor Networks

The localization technology is the essential requirement of constructing a smart building and smart city. It is one of the most important technologies for wireless sensor networks (WSNs). However, when WSNs are deployed in harsh indoor environments, obstacles can result in non-line-of-sight (NLOS) propagation. In addition, NLOS propagation can seriously reduce localization accuracy. In this paper, we propose a NLOS localization method based on residual analysis to reduce the influence of NLOS error. The time of arrival (TOA) measurement model is used to estimate the distance. Then, the NLOS measurement is identified through the residual analysis method. Finally, this paper uses the LOS measurements to establish the localization objective function and proposes the particle swarm optimization with a constriction factor (PSO-C) method to compute the position of an unknown node. Simulation results show that the proposed method not only effectively identifies the LOS/NLOS propagation condition but also reduces the influence of NLOS error.

Performances Analysis of GNSS NLOS Bias Correction in Urban Environment Using a Three-Dimensional City Model and GNSS Simulator

The well-known conventional least squares (LS) and extended Kalman filter (EKF) are ones of the most widely used algorithms in science and particularly in localization with global navigation satellites systems (GNSS) measurements. However, these estimators are not optimal when the GNSS measurements become contaminated by nonGaussian errors including multipath (MP) and nonline-of-sight (NLOS) biases. On the other hand, this kind of ranging measurements errors occurs generally in urban areas where GNSS-based positioning applications require more accuracy and reliability. In this paper, we use additional information of the environment consisting of bias prediction from a three-dimensional (3-D) model and a GNSS simulator to exploit constructively NLOS measurements. We use this 3-D GNSS simulator to predict lower and upper bounds of these biases. Then, we integrate this information in the position estimation problem by considering these biases as additive error and exploiting the bounds to end-up with a constrained state estimation problem that we resolve with existing constrained least squares (CLS) and constrained EKF (CEFK) algorithms. Experimental results using real GPS signals in down-town Toulouse show that the proposed estimator is capable of improving the positioning accuracy compared with conventional algorithms. Theoretical conditions have been established to determine the acceptable bias prediction error allowing better positioning performance than conventional estimators. Tests are conducted then to validate these conditions and investigate the influence of the bias prediction error on the localization performance by proposing new accuracy metrics.