Non-line-of-sight Error Mitigation Research Articles

Survey on NLOS Identification and Error Mitigation for UWB Indoor Positioning

Ultra-wideband (UWB) positioning systems often operate in a non-line-of-sight (NLOS) environment. NLOS propagation has become the main source of ultra-wideband indoor positioning errors. As such, how to identify and correct NLOS errors has become a key problem that must be solved in high-accuracy indoor positioning technology. This paper firstly describes the influence of the NLOS propagation path on localization accuracy and the generation method of ultra-wideband signals, and secondly classifies and analyzes the currently available algorithms for ultra-wideband non-line-of-sight (NLOS) identification and error suppression. For the identification of NLOS, the residual analysis judgement method, statistical feature class identification method, machine learning method and geometric feature judgement method are discussed. For the suppression of NLOS propagation errors, weighting-based methods, filtering-based methods, line-of-sight reconstruction algorithms, neural network algorithms, optimization methods with constraints, and path tracing methods are discussed. Finally, we conclude the paper and point out the problems that need to be solved in NLOS indoor positioning.

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.

A Convex Optimization Approach For NLOS Error Mitigation in TOA-Based Localization

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.

Indoor Carrier Phase Positioning Technology Based on OFDM System.

Carrier phase measurement is a ranging technique that uses the receiver to determine the phase difference between the received signal and the transmitted signal. Carrier phase ranging has a high resolution; thus, it is an important research direction for high precision positioning. It is widely used in global navigation satellite systems (GNSS) systems but is not yet commonly used inwireless orthogonal frequency division multiplex (OFDM) systems. Applying carrier phase technology to OFDM systems can significantly improve positioning accuracy. Like GNSS carrier phase positioning, using the OFDM carrier phase for positioning has the following two problems. First, multipath and non-line-of-sight (NLOS) propagation have severe effects on carrier phase measurements. Secondly, ambiguity resolution is also a primary issue in the carrier phase positioning. This paper presents a ranging scheme based on the carrier phase in a multipath environment. Moreover, an algorithm based on the extended Kalman filter (EKF) is developed for fast integer ambiguity resolution and NLOS error mitigation. The simulation results show that the EKF algorithm proposed in this paper solves the integer ambiguity quickly. Further, the high-resolution carrier phase measurements combined with the accurately estimated integer ambiguity lead to less than 30-centimeter positioning error for 90% of the terminals. In conclusion, the presented methods gain excellent performance, even when NLOS error occur.

An algorithm of NLOS error identification and mitigation in mobile location estimation

For the purpose of improving positioning accuracy of mobile station in the complex environment, a real-time non-line-of-sight (NLOS) identification and elimination method is proposed based on the c...

Echo State Network and Echo State Gaussian Process for Non-Line-of-Sight Target Tracking

Non-line-of-sight (NLOS) propagation blocks the direct path between the anchors and the agent, therefore, results in large positive bias in localization. Mobile target tracking in a NLOS scenario is much more challenging than static localization because of the need of not only dynamic modeling but also NLOS error mitigation. Gaussian Process (GP) regression is the state-of-the-art machine learning approach that resolves the issue. This article proposes two novel nonparametric strategies for NLOS target tracking, namely Echo State Network (ESN) and Echo State Gaussian Process (ESGP), both of which offers significant enhancement to the tracking performance compared to GP regression. Moreover, ESN is much more computationally efficient than GP regression and ESGP also provides a measure of confidence on the tracking results. Monte Carlo simulations and experiments prove the robustness and effectiveness of ESN and ESGP.

An Efficient NLOS Errors Mitigation Algorithm for TOA-Based Localization.

In time-of-arrival (TOA) localization systems, errors caused by non-line-of-sight (NLOS) signal propagation could significantly degrade the location accuracy. Existing works on NLOS error mitigation commonly assume that NLOS error statistics or the TOA measurement noise variances are known. Such information is generally unavailable in practice. The goal of this paper is to develop an NLOS error mitigation scheme without requiring such information. The core of the proposed algorithm is a constrained least-squares optimization, which is converted into a semidefinite programming (SDP) problem that can be easily solved by using the CVX toolbox. This scheme is then extended for cooperative source localization. Additionally, its performance is better than existing schemes for most of the scenarios, which will be validated via extensive simulation.

TOA-based NLOS error mitigation algorithm for 3D indoor localization

In the process of indoor localization, the existence of the non-line of sight (NLOS) error will greatly reduce the localization accuracy. To reduce the impact of this error, a 3 dimensional (3D) indoor localization algorithm named LMR (LLS-Minimum-Re-sidual) is proposed in this paper. We first estimate the NLOS error and use it to correct the measurement distances, and then calculate the target location with linear least squares (LLS) solution. The final nodes location can be obtained accurately by NLOS error mitigation. Our algorithm can work efficiently in both indoor 2D and 3D environments. The simulation results show that the proposed algorithm has better performance than traditional algorithms and it can significantly improve the localization accuracy.

Semidefinite Programming for NLOS Error Mitigation in TDOA Localization

Non-line-of-sight (NLOS) error mitigation for the time-of-arrival (TOA) localization has been extensively studied, but these methods cannot be directly applied for the time-difference-of-arrival (TDOA) systems. Recent work has applied convex optimization for NLOS error mitigation in TDOA systems. Issues remain unsolved with this technique include the convex hull problem, reference-anchor selection problem, and difficulties dealing with a wide range of NLOS-caused ranging errors. This letter proposes a new technique to transform a TDOA model into a TOA model and develops a semidefinite programming method with new constraints for effective NLOS error mitigation in TDOA systems. Major advantages of this method include: 1) it resolves the issues such as convex hull and reference-anchor selection issues that existing schemes are facing; 2) it does not require any a priori information about NLOS links or NLOS error statistics; and 3) it achieves a better performance than existing convex optimization schemes, which is verified in both simulation and real experiments.

Performance Analysis of ToA-Based Positioning Algorithms for Static and Dynamic Targets with Low Ranging Measurements.

Indoor Positioning Systems (IPSs) for emergency responders is a challenging field attracting researchers worldwide. When compared with traditional indoor positioning solutions, the IPSs for emergency responders stand out as they have to operate in harsh and unstructured environments. From the various technologies available for the localization process, ultra-wide band (UWB) is a promising technology for such systems due to its robust signaling in harsh environments, through-wall propagation and high-resolution ranging. However, during emergency responders’ missions, the availability of UWB signals is generally low (the nodes have to be deployed as the emergency responders enter a building) and can be affected by the non-line-of-sight (NLOS) conditions. In this paper, the performance of four typical distance-based positioning algorithms (Analytical, Least Squares, Taylor Series, and Extended Kalman Filter methods) with only three ranging measurements is assessed based on a COTS UWB transceiver. These algorithms are compared based on accuracy, precision and root mean square error (RMSE). The algorithms were evaluated under two environments with different propagation conditions (an atrium and a lab), for static and mobile devices, and under the human body’s influence. A NLOS identification and error mitigation algorithm was also used to improve the ranging measurements. The results show that the Extended Kalman Filter outperforms the other algorithms in almost every scenario, but it is affected by the low measurement rate of the UWB system.

Kernel Methods for Accurate UWB-Based Ranging With Reduced Complexity

Accurate and robust positioning in multipath environments can enable many applications, such as search-and-rescue and asset tracking. For this problem, ultra-wideband (UWB) technology can provide the most accurate range estimates, which are required for range-based positioning. However, UWB still faces a problem with non-line-of-sight (NLOS) measurements, in which the range estimates based on time-of-arrival (TOA) will typically be positively biased. There are many techniques that address this problem, mainly based on NLOS identification and NLOS error mitigation algorithms. However, these techniques do not exploit all available information in the UWB channel impulse response. Kernel-based machine learning methods, such as Gaussian Process Regression (GPR), are able to make use of all information, but they may be too complex in their original form. In this paper, we propose novel ranging methods based on kernel principal component analysis (kPCA), in which the selected channel parameters are projected onto a nonlinear orthogonal high-dimensional space, and a subset of these projections is then used as an input for ranging. We evaluate the proposed methods using real UWB measurements obtained in a basement tunnel, and found that one of the proposed methods is able to outperform state-of-the-art, even if little training samples are available.

NLOS Error Mitigation for TOA-Based Localization via Convex Relaxation

In this paper, we address the time-of-arrival (TOA) based localization problem in an adverse environment, where line-of-sight (LOS) signal propagation between the source and the sensor is not readily available, in which case we have to resort to non-line-of-sight (NLOS) signals. Two convex relaxation methods, i.e., the semidefinite relaxation (SDR) and the second-order cone relaxation (SOCR) methods, are proposed to mitigate the effect of NLOS errors on the localization performance. We consider two separate cases in which the information of the NLOS status is totally unknown and perfectly known, respectively. The proposed methods can be applied without knowing the distribution of NLOS errors. Moreover, we propose a NLOS error mitigation method that is robust to detection errors, which are generated in the process of detecting NLOS paths. Simulation results show that the proposed convex relaxation methods outperform some existing state-of-the-art methods.

A Hybrid TOA-Fingerprinting Based Localization of Mobile Nodes Using UWB Signaling for Non Line-Of-Sight Conditions

Recently, Impulse Radio Ultra Wideband (IR-UWB) signaling has become popular for providing precise location accuracy for mobile and wireless sensor node localization in the indoor environment due to its large bandwidth and high time resolution while providing ultra-high transmission capacity. However, the Non-line-of-sight (NLOS) error mitigation has considerable importance in localization of wireless nodes. In order to mitigate NLOS errors in indoor localization this paper proposes and investigates a novel approach which creates a hybrid combination of channel impulse response (CIR)-based fingerprinting (FP) positioning and an iterative Time of Arrival (TOA) real time positioning method using Ultra Wideband (UWB) signaling. Besides, to reduce the calculation complexities in FP method, this paper also introduces a unique idea for the arrangement of reference nodes (or tags) to create a fingerprinting database. The simulation results confirm that the proposed hybrid method yields better positioning accuracies and is much more robust in NLOS error mitigation than TOA only and FP only and a conventional iterative positioning method.

NLOS error mitigation with information fusion algorithm for UWB ranging systems

This article puts forward a scalar weighting information fusion (IF) smoother with modified biased Kalman filter (BKF) and maximum likelihood estimation (MLE) to mitigate the ranging errors in ultra wide band (UWB) systems. The information fusion algorithm uses both the time of arrival (TOA) and received signal strength (RSS) measurement data to improve the ranging accuracy. At first, the ranging protocol of IEEE 802.15.4a acts as a multi-sensor system with multi-scale sampling. Then the scalar-based IF smoother accurately estimates the range measurement in the line of sight (LOS) and non-line of sight (NLOS) condition of UWB sensor network, during which the effectiveness of the IF in mitigating errors is especially focused during the LOS/NLOS transitions. Simulation results show that the proposed hybrid TOA-RSS fusion approach indicates a performance improvement compared with the usual TOA-only and other IF method, and the estimated ranging metrics can be used for achieving higher accuracy in location estimation and target tracking.

Non-line of Sight Error Mitigation in Ultra-wideband Ranging Systems Using Biased Kalman Filtering

In this paper, a non-line of sight (NLOS) error mitigation method based on biased Kalman filtering for ultra-wideband (UWB) ranging is proposed. The NLOS effect on the measures of signal arrival ti...

Survey of NLOS identification and error mitigation problems in UWB-based positioning algorithms for dense environments

In this survey, the currently available ultra-wideband-based non-line-of-sight (NLOS) identification and error mitigation methods are presented. They are classified into several categories and their comparison is presented in two tables: one each for NLOS identification and error mitigation. NLOS identification methods are classified based on range estimates, channel statistics, and the actual maps of the building and environment. NLOS error mitigation methods are categorized based on direct path and statistics-based detection.

A minimum entropy estimation based mobile positioning algorithm

The problem of locating a mobile terminal has received significant attention in the field of wireless communications. The wireless location problem is made difficult by nonsymmetric contamination of measured time of arrival (TOA) data caused by non-line-of-sight (NLOS) propagation. In this paper, a novel robust NLOS error mitigation algorithm based on minimum entropy estimation is proposed without prior statistics knowledge of NLOS propagation error.We compare the proposed algorithm with two additional ones, the normal least-squares estimator and the Huber estimator, through MATLAB simulation in different COST 259 channel environment. Results reveal that the proposed algorithm is more robust to NLOS error than the other two, although it is not always superior to the other two on location accuracy.

Bootstrapping<tex>$M$</tex>-Estimators for Reducing Errors due to Non-Line-of-Sight (NLOS) Propagation

Mobile positioning is made difficult by nonsymmetric contamination of measured time-of-arrival (TOA) data caused by non-line-of-sight (NLOS) propagation. In this letter, a robust NLOS error mitigation algorithm based on Bootstrapping M-estimation is proposed without prior NLOS error distribution. A simulation comparison is performed in different channel environments between the proposed algorithm and two additional ones (least-square and Huber estimator). The proposed algorithm clearly outperforms the other two.