NLOS Error Research Articles

Enhancing Performance for Wireless Location: Leveraging Linear Trend and Uncertainty Inherent in NLOS Error

Enhancing Performance for Wireless Location: Leveraging Linear Trend and Uncertainty Inherent in NLOS Error

An Enhanced Collaborative Localization Method Based on Belief Propagation Aided by 3D Terrain Modelling

Navigation system performance degrades significantly in complex environments. It is important to analyze satellite visibility through 3D terrain modelling and separate the satellite signals propagated by NLOS to suppress the NLOS error. However, the traditional 3D terrain modelling visibility analysis method based on the pure terrain cover angle is only suitable for determining the visibility of GNSS satellites and may incorrectly separate LOS propagate measurement signals from members with low relative ranges and elevation angles under air–ground swarm conditions. To this end, this paper proposes a belief-propagating cooperative navigation method based on air–ground visibility analysis, which avoids mistakenly separating close-range LOS cooperative navigation signals by simultaneously considering the distances, elevation angles, and azimuths of the signal sources relative to the air–ground swarm members. The simulation shows that the cooperative navigation NLOS identification method based on air–ground visibility analysis proposed in this paper can more accurately realize the separation of NLOS signals under cooperative conditions than the traditional pure angular 3D terrain modelling visibility analysis method can, and the localization error of the members to be assisted is significantly reduced.

A Novel NLOS Identification and Error Mitigation Method for UWB Ranging and Positioning

A Novel NLOS Identification and Error Mitigation Method for UWB Ranging and Positioning

ACGA: Adaptive Conjugate Gradient Algorithm for non-line-of-sight hybrid TDOA-AOA localization

This study proposes a novel hybrid localization method that combines the time difference of arrival (TDOA) and angle of arrival (AOA) measurements to achieve improved accuracy with two base stations. Unlike other subspace-based methods that require calculating the eigenbasis, our proposed method constructs a Krylov subspace basis using the residual vectors of the Conjugate Gradient (CG) algorithm, which reduces computational complexity. In our approach, the measurements obtained from AOA estimation serve as initial estimates in our Adaptive Conjugate Gradient Algorithm (ACGA). ACGA incorporates an innovative adaptation mechanism that utilizes the residual error for updating and refining the estimated target position. This adaptation strategy allows our algorithm to iteratively improve localization accuracy, especially in the presence of NLOS errors. To assess the performance of our method, we conduct exhaustive simulations, considering various NLOS error scenarios and comparing them against conventional methods and extensively developed algorithms. The results demonstrate that our proposed method surpasses existing techniques, achieving a more densely spaced estimated position of the target in noisy environments with only two base stations. Furthermore, our method approaches the Cramer Rao Lower Bound (CRLB), indicating its high accuracy and efficiency in estimating the target position in typical NLOS environments. The effectiveness and potential of the proposed hybrid TDOA-AOA localization method are evident from the superior performance demonstrated in our simulations. These findings highlight the practical significance of our approach for real-world applications where accurate target localization is crucial, such as wireless communication, radar systems, and autonomous navigation.

Optimum Model for Tracking of Moving Objects

In wireless communication, it is a challenge to find the accurate position of moving objects. The presented work proposes the GPS receiver to measure the position and velocity of moving objects. These measurements are dynamic, the Least Squares (LS) technique is used to linearize the measurement for further processing. Afterward, the time difference of arrival (TDOA) mythology is applied. The obtained data is then processed through a Kalman filter to mitigate non-line-of-sight errors and smoothen the range values. The Kalman filter applies standard deviation on received data and performs an NLOS/LOS hypothesis test. By processing the received data, the algorithm generates readings that mitigate the NLOS error and reduces position error. The simulations demonstrate that the proposed algorithm reduced the noise by 28.64% and 34.4% in LOS and NLOS regions respectively. These findings indicate that the accuracy of object tracking was significantly improved as compared to other algorithms while also being less computationally intensive and cost efficient.

A Method for UWB Localization Based on CNN-SVM and Hybrid Locating Algorithm

In this paper, aiming at the severe problems of UWB positioning in NLOS-interference circumstances, a complete method is proposed for NLOS/LOS classification, NLOS identification and mitigation, and a final accurate UWB coordinate solution through the integration of two machine learning algorithms and a hybrid localization algorithm, which is called the C-T-CNN-SVM algorithm. This algorithm consists of three basic processes: an LOS/NLOS signal classification method based on SVM, an NLOS signal recognition and error elimination method based on CNN, and an accurate coordinate solution based on the hybrid weighting of the Chan–Taylor method. Finally, the validity and accuracy of the C-T-CNN-SVM algorithm are proved through a comparison with traditional and state-of-the-art methods. (i) Focusing on four main prediction errors (range measurements, maxNoise, stdNoise and rangeError), the standard deviation decreases from 13.65 cm to 4.35 cm, while the mean error decreases from 3.65 cm to 0.27 cm, and the errors are practically distributed normally, demonstrating that after training a SVM for LOS/NLOS signal classification and a CNN for NLOS recognition and mitigation, the accuracy of UWB range measurements may be greatly increased. (ii) After target positioning, the proposed method can realize a one-dimensional X-axis and Y-axis accuracy within 175 mm, and a Z-axis accuracy within 200 mm; a 2D (X,Y) accuracy within 200 mm; and a 3D accuracy within 200 mm, most of which fall within (100 mm, 100 mm, 100 mm). (iii) Compared with the traditional algorithms, the proposed C-T-CNN-SVM algorithm performs better in location accuracy, cumulative error probability (CDF), and root-mean-square difference (RMSE): the 1D, 2D, and 3D accuracy of the proposed method is 2.5 times that of the traditional methods. When the location error is less than 10 cm, the CDF of the proposed algorithm only reaches a value of 0.17; when the positioning error reaches 30 cm, only the CDF of the proposed algorithm remains in an acceptable range. The RMSE of the proposed algorithm remains ideal when the distance error is greater than 30 cm. The results of this paper and the idea of a combination of machine learning methods with the classical locating algorithms for improved UWB positioning under NLOS interference could meet the growing need for wireless indoor locating and communication, which indicates the possibility for the practical deployment of such a method in the future.

A Novel 3D Indoor Node Localization Technique Using Weighted Least Square Estimation with Oppositional Beetle Swarm Optimization Algorithm

In this research work an effective indoor localization mechanism has been implemented for smart devices, advancements of mobile-Internet and multimedia applications. Indoor localization is one of recent technology, it is a location-based service (LBS), this work has been facilitated to commercial and civilian industries. The LSB can useful in many tools such as public security, disaster management, and positioning navigation. Several research works have been concentrated on design of accurate 2D indoor localization techniques. Since 3D indoor localization techniques offer numerous benefits compared to 2D model. In this investigation a Novel 3D Indoor Node Localization Technique has been proposed using Oppositional Beetle Swarm Optimization with Weighted Least Square Estimation (OBSO-WLSE) algorithm. The proposed OBSO- WLSE algorithm aims to develop the localization accuracy with reduced computational time. The OBSO algorithm is employed for approximating initial locations of the targets, these results can minimize NLOS error. The precise location of target has been identified through WLSE technique as well as OBSO can predict initial location. To improve the efficiency of the OBSO technique, the concept of oppositional based learning (OBL) is integrated into the traditional BSO algorithm. The designed model prototype simulation has been run on MATLAB software with NS3 Tool Box. The measures like accuracy 98.45%, sensitivity 96.34%, recall 94.67%, 3D indoor localization detection rate 19.25% improvement and throughput 97.34% have been attained. The localization error, range error and transmission range performance measures are used for experimental evolution. The results recommended that proposed model is robust for navigation associated apps.

An Indoor Tracking Algorithm Based on Particle Filter and Nearest Neighbor Data Fusion for Wireless Sensor Networks

Wireless indoor localization technology is a hot research field at present. Its basic principle is to estimate the geometric position of the mobile node by measuring the characteristic parameters of the propagation signal between the mobile node and the beacon node. However, in the process of position estimation, there are non-line-of-sight errors such as multipath propagation, which greatly reduces the localization accuracy. This paper proposes an enhanced closest neighbor data association approach based on ultra-wide band (UWB) measurement. First, the measured values were grouped to obtain a series of undetermined prediction position points, and the undetermined points were put into our set verification gate for screening. Then, the particle filter was introduced to weight and redistribute the position estimation after screening, removing the NLOS-contaminated location estimation from consideration. The position estimation group with low error was finally confirmed and weighted again by the nearest neighbor association algorithm. Simulation results showed that the average localization accuracy of the proposed method was about 1 m. Compared with the existing localization algorithms, the proposed method can successfully reduce the influence of NLOS error and obtain higher localization accuracy.

Research on UWB fusion location algorithm

In order to solve the problem that ultra-wideband (ultra-wideband, UWB) positioning is greatly affected by the environment in the indoor environment of mobile robot, an indoor positioning algorithm based on UWB, odometer and inertial measurement unit is proposed. Taking advantage of the high positioning accuracy of UWB in indoor environment and the high accuracy of odometer positioning in a small range, the adaptive parameter equation of error is constructed by using linear regression function. The weight coefficient is obtained by combining the position data solved by UWB and the previous moment fusion algorithm to solve the position difference and the mileage displacement formula, and the weight value of angle increment is given according to the state of the system. Through the calculation, the displacement increment matrix of each measurement of the system is obtained, and the final linear optimal value is obtained by substituting the extended Kalman filter algorithm. Compared with the single UWB positioning method, the results show that the system can maintain good accuracy and robustness, and the mean error of the whole system is less than 8cm, which provides a solution for the single UWB positioning of mobile robot affected by NLOS error.

An area optimization based cooperative localization algorithm with node selection

In recent years, position information has become a key feature to drive location and context aware services in mobile communication. Researchers from all over the world have proposed many solutions for indoor positioning over the past several years. However, due to weak signals, multipath or non-line-of-sight signal propagation, accurately and efficiently localizing targets in harsh indoor environments remains a challenging problem. To improve the performance in harsh environment with insufficient anchors, cooperative localization has emerged. In this paper, a novel cooperative localization algorithm, named area optimization and node selection based sum-product algorithm over a wireless network (AN-SPAWN), is described and analyzed. To alleviate the high computational complexity and build optimized cooperative cluster, a node selection method is designed for the cooperative localization algorithm. Numerical experiment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional cooperative localization algorithms in the harsh indoor environments.

UWB/IMU integrated inspection robot positioning in underground substation

The task of the underground inspection robot is to detect the environment of the underground substation and the operating parameters of the electrical equipment in it, and precise positioning is the guarantee for the inspection robot to complete the above tasks. In order to solve the problems of large drift of inertial sensors based on micro-electro-mechanical systems, and susceptibility to NLOS errors in UWB positioning, a fusion positioning algorithm with extended Kalman filtering as the core was designed. The algorithm suppresses the drift of position information by estimating the deviation of the inertial device, and integrates the UWB ranging information to improve the long-term positioning stability of the carrier. Finally, a parallel window smoother is designed to smooth the fusion result. The simulation results show that the fusion algorithm has a strong ability to resist drift and non-line-of-sight errors. Compared with the traditional fusion algorithm, the error is reduced by 38%.

Data-Selective Least Squares Methods for Elliptic Localization With NLOS Mitigation

In this letter, we consider the problem of 2-D elliptic localization, where multiple spatially separated sensors, including the transmitters and receivers, are exploited to locate the signal reflecting/relaying target in the mixed line-of-sight/nonline-of-sight (NLOS) environments. We begin by revisiting a plain closed-form linear least squares (LS) solution. As it is vulnerable to the existence of erroneous time-sum-of-arrival (TSOA) measurements under the NLOS conditions, we then devise two new data-selective LS methods, by which the outliers can be identified and mitigated and a higher level of resistance to the NLOS bias errors can be provided. To conduct data selection, the first algorithm combines the use of the traditional linear LS estimator and an additional cost function, whereas the second relies on the parameterization of the TSOA-defined ellipses and follows a nonlinear LS estimation criterion. Based on the simulations, we demonstrate the effectiveness of the proposed methods in NLOS error mitigation at acceptable computational costs.

Constrained L1-Norm Minimization Method for Range-Based Source Localization under Mixed Sparse LOS/NLOS Environments.

Under mixed sparse line-of-sight/non-line-of-sight (LOS/NLOS) conditions, how to quickly achieve high positioning accuracy is still a challenging task and a critical problem in the last dozen years. To settle this problem, we propose a constrained L1 norm minimization method which can reduce the effects of NLOS bias for improve positioning accuracy and speed up calculation via an iterative method. We can transform the TOA-based positioning problem into a sparse optimization one under mixed sparse LOS/NLOS conditions if we consider NLOS bias as outliers. Thus, a relatively good method to deal with sparse localization problem is L1 norm. Compared with some existing methods, the proposed method not only has the advantages of simple and intuitive principle, but also can neglect NLOS status and corresponding NLOS errors. Experimental results show that our algorithm performs well in terms of computational time and positioning accuracy.

Alternating Direction Method of Multipliers for TOA-Based Positioning Under Mixed Sparse LOS/NLOS Environments

For positioning system based on wireless sensor networks, NLOS errors are one of the main factors to degrade localization performance of an algorithm, about which lots of research results and analysis have been published in previous literatures to enhance localization performance. However, those literatures have neglected computational time, another important index to performance. To decrease computational time and improve localization accuracy simultaneously, we firstly consider NLOS errors as outliers and transform the TOA-based localization problem into a sparse optimization one in LOS-dominating environment. Then, we introduce sparse technology into line-of-sight/none-line-of-sight (LOS/NLOS) scenarios formulating a L1-norm minimization problem, and solve it by alternating direction method of multipliers (ADMM) with a strategy of iterative adaptive. Monte Carlo simulation results show that our method has advantages of high computation speed and positioning accuracy under mixed sparse LOS/NLOS scenarios.

A Multifilter Location Optimization Algorithm Based on Neural Network in LOS/NLOS Mixed Environment

While the modern communication system, embedded system, and sensor technology have been widely used at the moment, the wireless sensor network (WSN) composed of microdistributed sensors is favored due to its relatively excellent communication interaction, real‐time computing, and sensing capabilities. Because GPS positioning technology cannot meet the needs of indoor positioning, positioning based on WSN has become the better option for indoor localization. In the field of WSN indoor positioning, how to cope with the impact of NLOS error on positioning is still a big problem to be solved. In order to mitigate the influence of NLOS errors, a Neural Network Modified Multiple Filter Localization (NNMML) algorithm is proposed in this paper. In this algorithm, LOS and NLOS cases are distinguished firstly. Then, KF and UKF are applied in the LOS case and the NLOS case, respectively, and appropriate grouping processing is carried out for NLOS data. Finally, the positioning results after multiple filtering are corrected by neural network. The simulation results illustrate that the location accuracy of NNMML algorithm is better than that of KF, EKF, UKF, and the version without neural network correction. It also shows that NNMML is suitable for the situation with large NLOS error.

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

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

MS Location Estimation Based on the Artificial Bee Colony Algorithm.

With the mature technology of wireless communications, the function of estimating the mobile station (MS) position has become essential. Suppressing the bias resulting from non-line-of-sight (NLSO) scenarios is the main issue for a wireless location network. The artificial bee colony (ABC) algorithm, based on the depiction of bee swarm’s foraging characteristics, is widely applied to solve optimization problems in several fields. Based on three measurements of time-of-arrival (TOA), an objective function is used to quantify the additional NLOS error on the MS positioning scheme. The ABC algorithm is adopted to locate the most precise MS location by minimizing the objective function value. The performance of the proposed positioning methods is verified under various error distributions through computer simulations. Meanwhile, the localization accuracy achieved by other existing methods is also investigated. According to the simulation results, accurate estimation of the MS position is derived and therefore the efficiency of the localization process is increased.

Cooperative Multiple Target Nodes Localization Using TOA in Mixed LOS/NLOS Environments

In this paper, the time-of-arrival based cooperative localization problem under mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions is addressed. Under the conditions of unknown statistics of NLOS errors and unknown path status, we formulate a non-convex robust weighted least squares (RWLS) problem to locate the multiple stationary target nodes. In this problem, we introduce some “balancing parameters” to alleviate the possible improper upper bounds problem of the existing robust localization methods, and jointly estimate the target node positions and the balancing parameters. With the aid of the semidefinite relaxation technique, the RWLS problem is transformed into a convex mixed semidefinite and second order cone programming problem, which can be solved efficiently. The idea is then extended to the moving target nodes localization problem, where the velocity is assumed to be fixed within a short time period. Both simulated and real experimental data are used to verify the performance of the proposed method. The results show that the proposed method works well for both the sparse and dense NLOS environments, and also confirm the superior performance of the proposed method over the existing methods.

Improved Robust TOA-Based Localization via NLOS Balancing Parameter Estimation

In this paper, the time-of-arrival-based localization problem under mixed line-of-sight/non-line-of-sight (LOS/NLOS) conditions is addressed. Previous studies show that existing robust methods perform well in dense NLOS environments, but generally perform badly in sparse NLOS environments. To alleviate this problem, we introduce a “balancing parameter” related to the NLOS errors and formulate a new robust weighted least squares (RWLS) problem with the source position and the NLOS balancing parameter as the estimation variables. The proposed method does not require the statistics of NLOS errors and the path status. By leveraging the S-Lemma, the RWLS problem is transformed into a non-convex optimization problem, which is then relaxed into a convex semidefinite program. Simulation results show that the proposed method works well for both the sparse and dense NLOS environments.

A Map-Matching Based Approach to Compute and Modelize NLOS and Multipath Errors for GNSS Positioning in Hard Areas.

In Global Navigation Satellite systems (GNSS), the performances of classical localization methods show a significant degradation in constrained environments (urban and indoor environments), due to ...