scholarly journals Robust Error Estimation Based on Factor-Graph Models for Non-Line-of-Sight Localization

2021 ◽  
Author(s):  
O. Arda Vanli ◽  
Clark N. Taylor
Keyword(s):  
Weighted Least Squares ◽  
Factor Graph ◽  
Line Of Sight ◽  
Graph Models ◽  
Variance Estimators ◽  
Measurement Models ◽  
Regression Problems ◽  
Robot Localization Problem ◽  
Non Line Of Sight

Abstract This paper presents a method to estimate the covariances of the inputs in a factor-graph formulation for localization under non-line-of-sight conditions. A general solution, based on covariance estimation and M estimators in linear regression problems, is presented that is shown to give unbiased estimators of multiple variances and are robust against outliers. An iteratively re-weighted least squares (IRLS) algorithm is proposed to jointly compute the proposed variance estimators and the state estimates for the non-linear factor graph optimization. The efficacy of the method is illustrated in a simulation study using a robot localization problem under various process and measurement models and measurement outlier scenarios. A case study involving a Global Positioning System (GPS) based localization in an urban environment and data containing multipath problems demonstrates the application of the proposed technique.

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.

A Two-Step Optimizing Algorithm for TOA Real-Time Dynamic Localization in NLOS Environment

2013 ◽  
Vol 347-350 ◽  
pp. 3604-3608
Author(s):  
Shan Long ◽  
Zhe Cui ◽  
Fei Song
Keyword(s):  
Real Time ◽  
Weighted Least Squares ◽  
Line Of Sight ◽  
Wireless Localization ◽  
Localization Accuracy ◽  
Time Dynamic ◽  
Real Time Tracking ◽  
Step Algorithm ◽  
Optimizing Algorithm ◽  
Non Line Of Sight

Non-line-of-sight (NLOS) is one of the main factors that affect the ranging accuracy in wireless localization. This paper proposes a two-step optimizing algorithm for TOA real-time tracking in NLOS environment. Step one, use weighted least-squares (WLS) algorithm, combined with the NLOS identification informations, to mitigate NLOS bias. Step two, utilize Kalman filtering to optimize the localization results. Simulation results show that the proposed two-step algorithm can obtain better localization accuracy, especially when there are serious NLOS obstructions.

scholarly journals Time-of-arrival source localization based on weighted least squares estimator in line-of-sight/non-line-of-sight mixture environments

2016 ◽  
Vol 12 (12) ◽  
pp. 155014771668382 ◽  
Author(s):  
Chee-Hyun Park ◽  
Joon-Hyuk Chang
Keyword(s):  
Least Squares ◽  
Source Localization ◽  
Weighted Least Squares ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Mean Square ◽  
Localization Algorithm ◽  
Absolute Deviation ◽  
Weighted Least Squares Estimator ◽  
Non Line Of Sight

In this article, we propose a line-of-sight/non-line-of-sight time-of-arrival source localization algorithm that utilizes the weighted least squares. The proposed estimator combines multiple sorted measurements using the spatial sign concept, Mahalanobis distance, and Stahel–Donoho estimator, that is, assigning less weight to the samples as they are far from the center of inlier distribution. Also, the eigendecomposition Kendall’s [Formula: see text] covariance matrix is utilized as the scatter measure instead of the conventional median absolute deviation. Thus, the adverse effects by outliers can be attenuated effectively. To validate the superiority of the proposed methods, the root mean square error performances are compared with that of the existing algorithms via extensive simulation.

scholarly journals Location of Moving Targets in Substation Non-Line-of-Sight Environment

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5321
Author(s):  
Yubo Wang ◽  
Weimin Yang ◽  
Zheng Wang ◽  
Wenjun Zhou ◽  
Liang Li ◽  
...  
Keyword(s):  
Distribution Function ◽  
Probability Distribution ◽  
Least Squares ◽  
Probability Distribution Function ◽  
Weighted Least Squares ◽  
Signal Propagation ◽  
Propagation Path ◽  
Line Of Sight ◽  
Measured Signal ◽  
Non Line Of Sight

In substations, a localization system based on a wireless sensor network (WSN) is a challenge, because the propagation of the measured signal could be blocked by various devices. In other words, non-line-of-sight (NLOS) propagation, where the signal propagation path is occluded, will affect measurement accuracy. A novel localization method based on a two-step weighted least squares and a probability distribution function is proposed to reduce the influence of NLOS error on the localization result. In this method, the initial multi-group localization result is obtained by the two-step weight weighted least-squares method, and the probability distribution function of the target is constructed by using the initial localization results, which can effectively reduce the influence of the NLOS error on the localization result. The simulation and test results show that the proposed method can keep the coordinate error within 30 cm in the substation. Compared with the localization result of two-step weighted least-squares (TSWLS) method, the average localization error is reduced by more than 1 m. Compared with the other two similar algorithms, the localization accuracy is improved by more than 50%. The tested results show that the localization performance of the method is robustness in the NLOS environment of the substation. While ensuring stability, the proposed algorithm is less efficient than some existing ones. However, under the calculation conditions of ordinary computers, the average single-point calculation time is less than 0.1 s, which can meet the needs of applications in substations.

Integrating the Non-Line of Sight Launching System (NLOS-LS) in the United States Navy

2007 ◽  
Author(s):  
Jonathon Emis ◽  
Bryan Huang ◽  
Timothy Jones ◽  
Mei Li ◽  
Don Tumbocon
Keyword(s):  
United States ◽  
United States Navy ◽  
The United States ◽  
Line Of Sight ◽  
Non Line Of Sight

scholarly journals Low-cost SPAD sensing for non-line-of-sight tracking, material classification and depth imaging

2021 ◽  
Vol 40 (4) ◽  
pp. 1-12
Author(s):  
Clara Callenberg ◽  
Zheng Shi ◽  
Felix Heide ◽  
Matthias B. Hullin
Keyword(s):  
Low Cost ◽  
Line Of Sight ◽  
Depth Imaging ◽  
Material Classification ◽  
Non Line Of Sight

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.

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