scholarly journals Non-Line-of-Sight Error Mitigation in Wireless Communication Systems

2011 ◽  
Vol 1 ◽  
pp. 173-177
Author(s):  
Szu Lin Su ◽  
Yi Wen Su ◽  
Ho Nien Shou ◽  
Chien Sheng Chen
Keyword(s):  
Communication Systems ◽  
Hybrid Methods ◽  
Mobile Station ◽  
Base Stations ◽  
Superior Performance ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Angle Of Arrival ◽  
Error Statistics ◽  
Non Line Of Sight

When there is non-line-of-sight (NLOS) path between the mobile station (MS) and base stations (BSs), it is possible to integrate many kinds of measurements to achieve more accurate measurements of the MS location. This paper proposed hybrid methods that utilize time of arrival (TOA) at five BSs and angle of arrival (AOA) information at the serving BS to determine the MS location in NLOS environments. The methods mitigate the NLOS effect simply by the weighted sum of the intersections between five TOA circles and the AOA line without requiring priori knowledge of NLOS error statistics. Simulation results show that the proposed methods always give superior performance than Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP).

scholarly journals Combination linear lines of position and neural network for mobile station location estimation

2017 ◽  
Vol 13 (7) ◽  
pp. 155014771771738 ◽  
Author(s):  
Chien-Sheng Chen
Keyword(s):  
Mobile Station ◽  
Base Station ◽  
Location Estimation ◽  
Base Stations ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Angle Of Arrival ◽  
Marquardt Algorithm ◽  
Station Location ◽  
Non Line Of Sight

To enhance the effectiveness and accuracy of mobile station location estimation, author utilizes time of arrival measurements from three base stations and one angle of arrival information at the serving base station to locate mobile station in non-line-of-sight environments. This article makes use of linear lines of position, rather than circular lines of position, to give location estimation of the mobile station. It is much easier to solve two linear line equations rather than nonlinear circular ones. Artificial neural networks are widely used techniques in various areas due to overcoming the problem of exclusive and nonlinear relationships. The proposed algorithms employ the intersections of three linear lines of position and one angle of arrival line, based on Levenburg–Marquardt algorithm, to determine the mobile station location without requiring a priori information about the non-line-of-sight error. The simulation results show that the proposed algorithms can always provide much better location estimation than Taylor series algorithm, hybrid lines of position algorithm as well as the geometrical positioning methods for different levels of biased, unbiased, and distance-dependent non-line-of-sight errors.

scholarly journals Probability Weighting Localization Algorithm Based on NLOS Identification in Wireless Network

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Shixun Wu ◽  
Shengjun Zhang ◽  
Kai Xu ◽  
Darong Huang
Keyword(s):  
Base Station ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Probability Weighting ◽  
Localization Algorithm ◽  
Angle Of Arrival ◽  
Chi Square ◽  
Home Base ◽  
Angle Measurements ◽  
Non Line Of Sight

In this paper, a localization scenario that the home base station (BS) measures time of arrival (TOA) and angle of arrival (AOA) while the neighboring BSs only measure TOA is investigated. In order to reduce the effect of non-line of sight (NLOS) propagation, the probability weighting localization algorithm based on NLOS identification is proposed. The proposed algorithm divides these range and angle measurements into different combinations. For each combination, a statistic whose distribution is chi-square in LOS propagation is constructed, and the corresponding theoretic threshold is derived to identify each combination whether it is LOS or NLOS propagation. Further, if those combinations are decided as LOS propagation, the corresponding probabilities are derived to weigh the accepted combinations. Simulation results demonstrate that our proposed algorithm can provide better performance than conventional algorithms in different NLOS environments. In addition, computational complexity of our proposed algorithm is analyzed and compared.

scholarly journals NLOS Mitigation for TOA Location Based on a Modified Deterministic Model

2008 ◽  
Vol 2008 ◽  
pp. 1-4 ◽  
Author(s):  
Hong Tang ◽  
Yongwan Park ◽  
Tianshuang Qiu
Keyword(s):  
Numerical Solution ◽  
Deterministic Model ◽  
The Other ◽  
Base Stations ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Wireless Location ◽  
Scale Factors ◽  
Measured Time ◽  
Non Line Of Sight

Wireless location becomes difficult due to contamination of measured time-of-arrival (TOA) caused by non-line-of-sight. In this letter, TOA measurements seen at base stations are adjusted by scale factors, and a modified deterministic model is built. An effective numerical solution is proposed to resolve the scale factors and mobile position. A simulation comparison of four algorithms indicates that the proposed algorithm outperforms the other three algorithms.

scholarly journals A non-line-of-sight error mitigation method for location estimation

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668273 ◽  
Author(s):  
Chien-Sheng Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Mobile Station ◽  
Location Estimation ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Object Function ◽  
Swarm Optimization ◽  
Station Location ◽  
Non Line Of Sight

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.

scholarly journals Estimating Directional Data From Network Topology for Improving Tracking Performance

2019 ◽  
Vol 8 (2) ◽  
pp. 30 ◽  
Author(s):  
Slavisa Tomic ◽  
Marko Beko ◽  
Rui Dinis ◽  
Paulo Montezuma
Keyword(s):  
Prior Knowledge ◽  
Polar Space ◽  
Reference Points ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Angle Of Arrival ◽  
Measurement Models ◽  
Novel Approach ◽  
Negative Effect ◽  
Non Line Of Sight

This work proposes a novel approach for tracking a moving target in non-line-of-sight (NLOS) environments based on range estimates extracted from received signal strength (RSS) and time of arrival (TOA) measurements. By exploiting the known architecture of reference points to act as an improper antenna array and the range estimates, angle of arrival (AOA) of the signal emitted by the target is first estimated at each reference point. We then show how to take advantage of these angle estimates to convert the problem into a more convenient, polar space, where a linearization of the measurement models is easily achieved. The derived linear model serves as the main building block on top of which prior knowledge acquired during the movement of the target is incorporated by adapting a Kalman filter (KF). The performance of the proposed approach was assessed through computer simulations, which confirmed its effectiveness in combating the negative effect of NLOS bias and superiority in comparison with its naive counterpart, which does not take prior knowledge into consideration.

Non-Line-of-Sight Positioning Algorithm Based on Robust Principal Component Analysis

2014 ◽  
Vol 998-999 ◽  
pp. 889-893
Author(s):  
Zhu Lin Xiong ◽  
Ce Lun Liu ◽  
Wei Du ◽  
Ze Bin Xie
Keyword(s):  
Principal Component Analysis ◽  
Mobile Station ◽  
Principal Component ◽  
Component Analysis ◽  
Low Rank ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Robust Principal Component Analysis ◽  
Error Matrix ◽  
Non Line Of Sight

Non-Line-of-Sight (NLOS) propagation problems badly degrade the accuracy of wireless mobile positioning algorithms, which incurs a large positive bias in the Time-of-Arrival (TOA) measurements. Under several assumptions, the Hankel matrix of TOA data can be decomposed into a low-rank distance matrix and a sparse error matrix. This paper utilizes the robust principal component analysis (RPCA) method to solve the decomposition problem. After estimating the distance, the positioning problem can use existing Line-of-Sight (LOS) based algorithms to calculate the coordinate of the mobile station (MS). Simulation results show our method outperforms other existing NLOS positioning methods and the RPCA based matrix decomposition process can eliminate NLOS effect efficiently.

scholarly journals An Efficient NLOS Errors Mitigation Algorithm for TOA-Based Localization

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1403
Author(s):  
Yanbin Zou ◽  
Huaping Liu
Keyword(s):  
Signal Propagation ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Error Statistics ◽  
The Core ◽  
Error Mitigation ◽  
Least Squares Optimization ◽  
Better Than ◽  
Localization Systems ◽  
Non Line Of Sight

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.

scholarly journals Hybrid TOA/AOA Approximate Maximum Likelihood Mobile Localization

2010 ◽  
Vol 2010 ◽  
pp. 1-5 ◽  
Author(s):  
Mohamed Zhaounia ◽  
Mohamed Adnan Landolsi ◽  
Ridha Bouallegue
Keyword(s):  
Maximum Likelihood ◽  
Communication Systems ◽  
Base Stations ◽  
Time Of Arrival ◽  
Angle Of Arrival ◽  
Arrival Angle ◽  
Wireless Location ◽  
Approximate Maximum Likelihood ◽  
Mobile Localization ◽  
Location Algorithm

This letter deals with a hybrid time-of-arrival/angle-of-arrival (TOA/AOA) approximate maximum likelihood (AML) wireless location algorithm. Thanks to the use of both TOA/AOA measurements, the proposed technique can rely on two base stations (BS) only and achieves better performance compared to the original approximate maximum likelihood (AML) method. The use of two BSs is an important advantage in wireless cellular communication systems because it avoids hearability problems and reduces network signaling burden. Simulation results show that, for certain scenarios, the proposed hybrid TOA/AOA AML with two BSs can outperform the AML with up to six BSs.

scholarly journals Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 230 ◽  
Author(s):  
Slavisa Tomic ◽  
Marko Beko
Keyword(s):  
State Of The Art ◽  
Hybrid Approach ◽  
Critical Distance ◽  
Heuristic Approach ◽  
Target Localization ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Individual Measurement ◽  
New Approach ◽  
Non Line Of Sight

This work addresses the problem of target localization in adverse non-line-of-sight (NLOS) environments by using received signal strength (RSS) and time of arrival (TOA) measurements. It is inspired by a recently published work in which authors discuss about a critical distance below and above which employing combined RSS-TOA measurements is inferior to employing RSS-only and TOA-only measurements, respectively. Here, we revise state-of-the-art estimators for the considered target localization problem and study their performance against their counterparts that employ each individual measurement exclusively. It is shown that the hybrid approach is not the best one by default. Thus, we propose a simple heuristic approach to choose the best measurement for each link, and we show that it can enhance the performance of an estimator. The new approach implicitly relies on the concept of the critical distance, but does not assume certain link parameters as given. Our simulations corroborate with findings available in the literature for line-of-sight (LOS) to a certain extent, but they indicate that more work is required for NLOS environments. Moreover, they show that the heuristic approach works well, matching or even improving the performance of the best fixed choice in all considered scenarios.

scholarly journals NLOS Error Mitigation in Cellular Positioning using PSO Optimization Algorithm

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Stevo Lukić ◽  
Mirjana Simić
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Mobile Station ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Cellular Radio ◽  
Object Function ◽  
Swarm Optimization ◽  
Station Location

Non-Line-Of-Sight conditions pose a major challenge to cellular radio positioning. Such conditions, when the direct Line-Of-Sight path is blocked, result in additional propagation delay for the signal, additional attenuation, and an angular bias. Therefore,many researchers have proposed various algorithms to mitigate the measured error caused by this phenomenon. This paper presentsthe procedure for improving accuracy of determining the mobile station location in cellular radio networks in Non-Line-of-Sightpropagation environment, based on the Time Of Arrival oriented estimator using the Particle Swarm Optimization algorithm. Incomputer science, Particle Swarm Optimization is an evolutionary computational method that optimizes a problem by iteratively tryingto improve a candidate solution with regard to a given measure of quality. The proposed algorithm uses the repeating Time-Of-Arrivaltest measurements using the four base stations and for simulation selects the measurement combination that give the smallest regionenclosed by the overlap of four circles. In this way, the smallest intersect area of the four Time-Of-Arrival circles is obtained, andtherefore the smallest positioning error. After that, we consider the complete problem as a combinatorial optimization problem withthe corresponding object function that represents the nonlinear relationship between the intersection of the four circles and the mobilestation location. The Particle Swarm Optimization finds the optimal solution of the object function and efficiently determines themobile station location. The simulation results show that the proposed method outperforms conventional algorithms such as theWeighted Least Squares and the Levenberq-Marquardt method.

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