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This letter proposes a low-cost non-line-of-sight (NLOS) identification and mitigation technique for ultra-wideband (UWB) ranging in both static and dynamic environments. Unlike conventional ranging that employs one mobile tag and a single fixed anchor at a known location, this technique utilizes two anchors and one tag. By utilizing Fresnel zones, this approach is effective in variable environments without requiring large quantities of prior knowledge. The proposed method was evaluated using simulations and experiments, achieving an identification accuracy of 96.41%. After error mitigation, ranging errors were reduced by 88.80%, 69.91%, and 96.12% in line-of-sight (LOS), NLOS, and dynamic NLOS environments, respectively.

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

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.

Localization based on range measurements may suffer from non-line-of-sight (NLOS) bias, which can significantly degrade the accuracy of localization. In this paper, the time-of-arrival (TOA) based localization problem in NLOS environments is addressed. In particular, we approximately model the hybrid noise formed by measurement noise and NLOS bias errors with a Gaussian distribution, and develop a robust estimator based on maximum likelihood (ML) which can mitigate the NLOS bias errors while estimating the location of the source. The Lagrange programming neural network (LPNN) is then applied to address the obtained nonlinear constrained optimization problem. Furthermore, a weighted version of the proposed algorithm is developed by incorporating the distances as weight factors in the formulation. Simulation results show that the proposed algorithms can provide better results as compared with several the state-of-the-art methods.

It is an attempt to apply machine learning algorithm to Non-line-of-sight (NLOS) ranging error mitigation in Ultra wide-band (UWB) positioning. However, there are two problems when using the kind of methods to mitigate NLOS ranging errors. One is the computational complexity and the other is the positioning accuracy in complex environments. In order to solve these two problems, this paper proposes an adaptive approach. This paper divides received signals into Line-of-sight (LOS) signals, mild and severe obstruction propagation signals according to the magnitude of the ranging error value for the first time, and proposes a fuzzy comprehensive evaluation-Gaussian process regression fusion (F-GPR) to mitigate the two classes of NLOS ranging error. The proposed approach is verified by measuring data and designing simulation scenario. The simulation results verily that the proposed approach can obtain more accurate NLOS error estimates and higher positioning accuracy.

Ultra-wideband (UWB) sensors have been widely used in multi-robot systems for cooperative tracking and positioning purposes due to their advantages such as high ranging accuracy and good real-time performance. In order to reduce the influence of non-line-of-sight (NLOS) UWB communication caused by the presence of obstacles on ranging accuracy in indoor environments, the paper proposes a novel Bayesian filtering approach for UWB ranging error mitigation. Nonparametric UWB sensor models, namely received signal strength (RSS) model and time of arrival (TOA) model, are constructed to capture the probabilistic noise characteristics under the influence of different obstruction conditions and materials within a typical indoor environment. The proposed Bayesian filtering approach can be used either as a standalone error mitigation approach for peer-to-peer (P2P) ranging, or as a part of a higher level Bayesian state estimation framework. Experiments were conducted to validate and evaluate the proposed approach in two configurations, i.e., inter-robot ranging, and mobile robot tracking in a wireless sensor network. The experimental results show that the proposed method can accurately identify the line-of-sight (LOS) and NLOS scenarios with wood and metal obstacles in a probabilistic representation and effectively improve the ranging/tracking accuracy. In addition, the low computational overhead of the approach makes it attractive in real-time systems.

A new improved Least Trimmed Squares (LTS) based algorithm for Non-line-of sight (NLOS) error mitigation is proposed for indoor localisation systems. The conventional LTS algorithm has hard threshold to decide the final set of base stations (BSs) to be used in position calculations. When the number of Line of Sight (LOS) base stations is more than the number of NLOS BSs the conventional LTS algorithm does not include some of them in position estimation due to principle of LTS algorithm or under heavy NLOS environments it cannot separate least biased BSs to use. To improve the performance of the conventional LTS algorithm in dynamic environments we have proposed a method that selects BSs for position calculation based on ordered residuals without discarding half of the BSs. By choosing a set of BSs which have least residual errors among all combinations as a final set for position calculation, we were able to decrease the localisation error of the system in dynamic environments. We demonstrate the robustness of the new improved method based on computer simulations under realistic channel environments.

The existence of non-line-of-sight (NLOS) errors can significantly degrade the localization performance. In this letter, we address the time-of-arrival (TOA) based localization problem under NLOS conditions. To accurately localize a source, we propose a novel robust second-order cone relaxation (SOCR) method, which is not sensitive to NLOS errors. The proposed method does not require one to know any statistics of the NLOS errors, and instead, it only requires to know the upper bound on the magnitudes of the NLOS errors, which is relatively easy to obtain in practice. Simulation results show that the proposed robust SOCR method has good performance in various NLOS scenarios, and also it has a relatively lower computational complexity than the existing methods.

Node localization is one of key technologies in wireless sensor networks (WSN). In practice, the occlusion of obstacles often leads to large non-line-of-sight (NLOS) error. Therefore, the misidentification of LOS (line-of-sight)/NLOS conditions can seriously reduce the accuracy of localization algorithms in WSN. In this paper, we propose a novel localization algorithm based on hypothesis testing method, which combines the NLOS condition identification algorithm and the extended Kalman filter method to mitigate the NLOS error. Moreover, we introduce the false alarm rate fa to fuse the estimated values in the LOS/NLOS condition to obtain more accurate results. The simulations demonstrate that the proposed algorithm can effectively reduce the influence of NLOS error and improve the localization accuracy.

The major challenges for Ultra-wide Band (UWB) indoor ranging systems are the dense multipath and non-line-of-sight (NLOS) problems of the indoor environment. To precisely estimate the time of arrival (TOA) of the first path (FP) in such a poor environment, a novel approach of entropy-based TOA estimation and support vector machine (SVM) regression-based ranging error mitigation is proposed in this paper. The proposed method can estimate the TOA precisely by measuring the randomness of the received signals and mitigate the ranging error without the recognition of the channel conditions. The entropy is used to measure the randomness of the received signals and the FP can be determined by the decision of the sample which is followed by a great entropy decrease. The SVM regression is employed to perform the ranging-error mitigation by the modeling of the regressor between the characteristics of received signals and the ranging error. The presented numerical simulation results show that the proposed approach achieves significant performance improvements in the CM1 to CM4 channels of the IEEE 802.15.4a standard, as compared to conventional approaches.

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., This work was supported in part by the Foundation for Science and Technology (FCT)—within the Project Scope under Grant Pest-OE/EEI/UI0319/2014, in part by the FEDER funds through the Competitively Factor Operational Programmed—COMPETE, and in part by the national founds through FCT within the scope under Project POCI-01-0145-FEDER-007136. The work of A. G. Ferreira and D. Fernandes was supported by the FCT under Grant SFRH/BD/91477/2012 and Grant SFRH/BD/92082/2012., info:eu-repo/semantics/publishedVersion

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.

超宽带穿墙定位中,需考虑NLOS传播效应对定位结果的影响以提高定位精度.在对室内目标穿墙定位的应用中,为了减小由NLOS传播引起的定位误差,建立了信号的穿墙传播模型,基于该模型提出了一种NLOS误差减小方法,该方法能够有效减小误差并提高对目标的定位精度.文中推导了定位误差与几何的关系,为实际系统的布设提供了理论依据.仿真结果验证了基于传播模型的NLOS误差减小方法对提高定位精度的有效性.

Accurate indoor localization requires non line-of-sight ranging error identification and mitigation. Current research has focused on methods that improve accuracy but are too complex to be executed in resource-constrained hardware in real-time. In this paper, we propose and evaluate a non line-of-sight mitigation technique using impulse radio ultra-wideband (IR-UWB) experimental ranging data. We show that using this technique, ranging non-line-of-sight (NLOS) errors can be significantly reduced in the location estimate. The method is less complex than other methods used in related work and can potentially be implemented on microcontrollers.

The accuracy of indoor positioning systems could be significantly reduced by non-line-of-sight (NLOS) propagation. The bulk of existing work on NLOS error mitigation for time-of-arrival (TOA) systems assumes that the NLOS links can be identified and/or the NLOS error statistics are known. To avoid requiring such information that is often unavailable in practice, recent work has applied convex optimization for NLOS error mitigation. However, convex optimization for NLOS error mitigation in TOA systems is often an infeasible problem. A strategy to reduce the infeasible problem probability is to relax the constraints for the optimization at the expenses of a reduced positioning accuracy. In this paper, we develop a soft-minimum method for NLOS error mitigation TOA systems. The major advantages of the proposed method include: 1) like existing convex optimization schemes, it does not require any a priori information about NLOS links or NLOS error statistics; 2) unlike existing convex optimization schemes, it does not have infeasibility issues; and 3) it results in a higher positioning accuracy than with existing convex optimization schemes.

In this paper, we propose a low complexity and robust non-line-of-sight error mitigation technique for positioning applications. It is based on the least trimmed squares estimation and it can reject BSs with large residuals from measured data while using the small ones for final position calculation. In moderate non-line-of-sight environments, our proposed algorithm achieves the best result while keeping the estimation simple. In heavy non-line-of-sight environments, our proposed algorithm achieves a similar performance to a more complex, more accurate techniques. We show that the least trimmed squares estimation can be used to mitigate non-line-of-sight errors under any channel condition.

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.

Because there are always non-line-of-sight effects in signal propagation, researchers have proposed various algorithms to mitigate the measured error caused by non-line-of-sight. Initially inspired by flocking birds, particle swarm optimization is an evolutionary computation tool for optimizing a problem by iteratively attempting to improve a candidate solution with respect to a given measure of quality. In this article, we propose a new location algorithm that uses time-of-arrival measurements to improve the mobile station location accuracy when three base stations are available. The proposed algorithm uses the intersections of three time-of-arrival circles based on the particle swarm optimization technique to give a location estimation of the mobile station in non-line-of-sight environments. An object function is used to establish the nonlinear relationship between the intersections of the three circles and the mobile station location. The particle swarm optimization finds the optimal solution of the object function and efficiently determines the mobile station location. The simulation results show that the proposed algorithm performs better than the related algorithms in wireless positioning systems, even in severe non-line-of-sight propagation conditions.

Mobile localization using distributed sensor network has attracted significant research interest in recent years. One of the main challenges for mobile location estimation is the presence of the non-line-of-sight (NLOS) propagation. It results in erroneous measurements containing NLOS error due to the reflection and diffraction. The NLOS error could seriously reduce the localization accuracy. This paper presents a novel mobile localization method for distributed sensor network in NLOS propagation scenarios. The aim of the method is to mitigate the NLOS error and enhance the localization accuracy. The transition between the LOS and NLOS is described by two-state Markov model. The interacting multiple model frame is employed to estimate the position of unknown node. The probability data association algorithm is used to filter the estimated location. The simulation results show that the proposed method outperforms the traditional filter method in NLOS scenarios.

In this paper, we propose an efficient non-line-of-sight (NLOS) error mitigation method for TOA measurement in indoor environments. In order to meet era of ubiquitous computing, there is a developing need for an accurate real-time locating system (RTLS). In the presence of a NLOS error, the location estimation performance is likely to be degraded. For effective location estimation, NLOS errors should be recognized and mitigated before measurements are used in conventional location estimation method such as non-linear least squares (NLS), linear least squares (LLS), weighted-linear least squares (WLS), and least median squares (LMS). For improve location estimation, we assumed that measured distances using weighing mitigation factor are exploited for location estimation. Through simulation results, theoretical analysis of the performance is demonstrated.

Non Line of Sight (NLOS) propagation is a challenging issue which cause large errors in wireless localization systems. NLOS is especially a dominant source of errors in metropolitan area wireless networks like cellular ones. We discuss our algorithms within cellular network terminology. This paper contributes two novel algorithms. The first algorithm is to extract the NLOS state of base stations and the second one is a correction algorithm to enhance the measurements accuracy. Our introduced algorithms can easily operate with other wireless localization algorithms and reduced NLOS error. These algorithms are proposed for network-based localization because of the amount of calculation.

This chapter contains sections titled: Introduction System Model NLOS Scenarios: Fundamental Limits and ML Solutions Least‐squares Techniques for NLOS Localization Constraint‐based Techniques for NLOS Localization Robust Estimators for NLOS Localization Identify and Discard Techniques for NLOS Localization Conclusions

In cellular wireless location, time of arrival (TOA) is sometimes corrupted by non-line-of-sight (NLOS) error due to multi-path propagation, which results in poor location accuracy. So, NLOS error is the main difficulty for mobile user location in wireless communication system. In this paper, we suggest a new NLOS error mitigation algorithm based on kalman filter. Both the true value of TOA and NLOS error are served as the state variances of kalman filter, according to the construction features of the former and the statistic characteristics of the latter, we theoretically deduce the condition on which we can obtain the unbiased estimation of the true value of TOA. At the same time, the paper fixes on the state transition matrix of kalman filter. At last, we take the situation when NLOS is exponential for example, the simulation results show that the approach we present need not detect NLOS error, and the location performance is improved with better estimation accuracy and higher calculating speed.

Nowadays, almost everyone has a mobile telephone. This requires GSM localization systems to have greater applicability all of the time. The ldquokiller issuerdquo in this field is the well-known non-line-of-sight (NLOS) error, which hinders localization robustness and accuracy in real scenarios. Many techniques have been developed to deal with this problem, but they usually require prior statistical information or error modeling, or are computationally expensive. In this paper, we present a simple method based on the least median of squares technique to mitigate the NLOS effect. The method we propose is widely used in artificial vision applications and manages to overcome NLOS error effects, yielding higher location accuracy and robustness than other techniques. It can successfully deal with more than half of the available corrupted measurements- no matter how severely-without any previous statistical knowledge-corrupted measurements are not identifiable-and with reduced computation load.

Mobile positioning is made difficult by contamination of measured time of arrival (TOA) data caused by NLOS propagation. In this paper, a adaptive NLOS error mitigation algorithm is proposed without prior NLOS error distribution. Simulations in difference channel environments show that the performance of our algorithm comes close to meeting Cramer Rao lower bound (CRLB).

To mitigate the non-line-of-sight (NLOS) error of ultra-wideband (UWB) ranging caused by obstructions in dense multipath environments, this paper proposed a novel NLOS error mitigation method. The principles and characteristics of NLOS error are analyzed. Based on the signal propagation path loss model, the NLOS error estimation expression is deduced and further used to calibrate the ranging results. Low complexity path detection algorithms are proposed for implementation of the method. Test on measured data shows that the method can improve the ranging precision greatly.

In this paper, we propose a linear programming approach to the problem of non-line-of-sight (NLOS) error mitigation in sensor networks. The locations of sensor nodes can be estimated using range or distance estimates from location-aware "anchor" nodes. In the absence of line-of-sight (LOS) between the sensor and anchor nodes, e.g., in indoor networks, the NLOS range estimates can be severely biased. If these biased range estimates are directly incorporated into practical location estimators such as the least-squares (LS) estimator without the mitigation of these bias errors, this can potentially lead to degradation in the accuracy of sensor location estimates. On the other hand, discarding the biased range estimates may not be a viable option, since the number of range estimates available may be limited. We present a novel NLOS bias mitigation scheme, based on linear programming, which (i) allows us to incorporate NLOS range information into sensor location-estimation, but (ii) does not allow NLOS bias errors to degrade sensor localization accuracy.

One of the major challenges to finding accurate mobile terminal location is the non-line-of-sight (NLOS) propagation caused by the blocking of the signal's direct path by obstacles. Traditional location algorithms perform poorly in NLOS environments and this has made it necessary to develop techniques to identify NLOS transmissions and then employ suitable error mitigation techniques. It is extremely difficult to characterize NLOS error because it varies with time and is location specific. We present statistical based techniques to distinguish LOS and NLOS propagation for various scenarios and present simulation results to validate them.