TOA-Based Localization With NLOS Mitigation via Robust Multidimensional Similarity Analysis

Xiong, Wenxin; So, Hing Cheung

doi:10.1109/lsp.2019.2929860

Cited by 33 publications

(21 citation statements)

References 31 publications

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“…While the recent efforts tend to perform error mitigation using as little NLOS information as possible, it is increasingly common to generalize the NLOS bias error term (i.e., one does not assume any specific non-Gaussian distribution) in the derivation of robust location estimators [4,[19][20][21]23,25,32]. Depending on what kind of distributions are applied to generate the NLOS errors for simulation, these studies can be classified into the exponential [21] and uniform [4,19,20,23,25,32] ones.…”

Section: Preliminaries and Problem Formulationmentioning

confidence: 99%

“…The dominant complexity of our SR-MCC algorithm is thus O(N HQ K L), where N HQ denotes the number of HQ iterations. In Table 1, the computational complexity of SR-MCC is compared to several state-of-the-art approaches for TOA-based localization with NLOS mitigation, 2 where N ADMM is the iteration number of the alternating direction method of multipliers in [25]. As our empirical results show, the proposed SR-MCC algorithm can already exhibit decent performance with a few number of N HQ and K and, hence, is fairly computationally simple.…”

Section: Mcc-based Optimization Problem (mentioning

confidence: 99%

“…The NLOS error in a contaminated TOA appears as a positive bias because of additional propagation delay, indicating that special attention has to be paid to alleviating its adverse impacts on positioning accuracy. While studies of TOA-based localization under NLOS conditions may date back more than one-and-a-half decades [7], NLOS mitigation schemes subject to relatively few specific assumptions about the errors have yet only lately been investigated in the literature [4,7,14,[19][20][21]23,[25][26][27]32].…”

Section: Introductionmentioning

confidence: 99%

“…Bearing a close resemblance to [23], the S-procedure is followed to eliminate the maximization part of the cumbersome minimax problem, whereupon the semidefinite relaxation is conducted to yield a tractable convex program. To boost the resilience of TOA-based localization system, there are also frequently chosen options other than the worst-case formulation which are less heavily dependent on the prior knowledge of NLOS information, e.g., the recursive Bayesian approaches with robust statistics in [14], model parameter determination of probability density function for the non-Gaussian distributions in [26,27], and robust multidimensional similarity analysis (RMDSA) in [25] borrowing the idea from outlier-resistant low-rank matrix completion, to name just a few.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Maximum Correntropy Criterion for Robust TOA-Based Localization in NLOS Environments

Xiong

Schindelhauer

et al. 2021

Circuits Syst Signal Process

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We investigate the problem of time-of-arrival (TOA)-based localization under possible non-line-of-sight (NLOS) propagation conditions. To robustify the squared-range-based location estimator, we follow the maximum correntropy criterion, essentially the Welsch M-estimator with a redescending influence function which behaves like $$\ell _0$$ ℓ 0 -minimization toward the grossly biased measurements, to derive the formulation. The half-quadratic technique is then applied to settle the resulting optimization problem in an alternating maximization (AM) manner. By construction, the major computational challenge at each AM iteration boils down to handling an easily solvable generalized trust region subproblem. It is worth noting that the implementation of our localization method requires nothing but merely the TOA-based range measurements and sensor positions as prior information. Simulation and experimental results demonstrate the competence of the presented scheme in outperforming several state-of-the-art approaches in terms of positioning accuracy, especially in scenarios, where the percentage of NLOS paths is not large enough.

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Section: Preliminaries and Problem Formulationmentioning

confidence: 99%

Section: Mcc-based Optimization Problem (mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Maximum Correntropy Criterion for Robust TOA-Based Localization in NLOS Environments

Xiong

Schindelhauer

et al. 2021

Circuits Syst Signal Process

Self Cite

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show abstract

“…To avoid the use of triangular inequality by the S-Lemma, the final target position could be obtained through the optimization problems. In [ 20 ], in order to deal with the abnormal row and column structure in the multi-dimensional similarity (MDS) matrix caused by NLOS propagation, the authors propose an improved robust matrix approximation program that uses the 2,1-norm and applies alternating directions of multipliers method to resolve the resulting nonlinear constraint optimization problem. Voting is a good idea that can be used to mitigate the influence of NLOS.…”

Section: Related Workmentioning

confidence: 99%

An Indoor Robust Localization Algorithm Based on Data Association Technique

Cheng

Wang

Xue

et al. 2020

Sensors

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As a key technology of the Internet of Things, wireless sensor network (WSN) has been used widely in indoor localization systems. However, when the sensor is transmitting signals, it is affected by the non-line-of-sight (NLOS) transmission, and the accuracy of the positioning result is decreased. Therefore, solving the problem of NLOS positioning has become a major focus for indoor positioning. This paper focuses on solving the problem of NLOS transmission that reduces positioning accuracy in indoor positioning. We divided the anchor nodes into several groups and obtained the position information of the target node for each group through the maximum likelihood estimation (MLE). By identifying the NLOS method, a part of the position estimates polluted by NLOS transmission was discarded. For the position estimates that passed the hypothesis testing, a corresponding poly-probability matrix was established, and the probability of each position estimate from line-of-sight (LOS) and NLOS was calculated. The position of the target was obtained by combining the probability with the position estimate. In addition, we also considered the case where there was no continuous position estimation through hypothesis testing and through the NLOS tracking method to avoid positioning errors. Simulation and experimental results show that the algorithm proposed has higher positioning accuracy and higher robustness than other algorithms.

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Efficient iteratively reweighted algorithms for robust hyperbolic localization

Zhai

Xiong

2023

Journal of the Franklin Institute

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TOA-Based Localization With NLOS Mitigation via Robust Multidimensional Similarity Analysis

Cited by 33 publications

References 31 publications

Maximum Correntropy Criterion for Robust TOA-Based Localization in NLOS Environments

Maximum Correntropy Criterion for Robust TOA-Based Localization in NLOS Environments

An Indoor Robust Localization Algorithm Based on Data Association Technique

Efficient iteratively reweighted algorithms for robust hyperbolic localization

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