Robust Mobile Location Estimation in NLOS Environment Using GMM, IMM, and EKF

Cui, Wei; Li, Bing; Le, Zhang; Meng, Wei

doi:10.1109/jsyst.2018.2866592

Cited by 37 publications

(39 citation statements)

References 34 publications

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“…The simulation experiments were carried out under the conditions of NLOS errors obeying Gaussian, uniform, and exponential distributions. In this paper, we compared the proposed algorithm with the EKF [34], IMM-EKF [30], and MPDA [35]. The simulation results were obtained using 1000 Monte Carlo runs, and the root mean square error (RMSE) and the error cumulative distribution function (CDF) of the average positioning errors were used as the performance indicators for the evaluation algorithm:…”

Section: Experiments and Results Analysismentioning

confidence: 99%

A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network

et al. 2019

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As one of the core technologies of the Internet of Things, wireless sensor network technology is widely used in indoor localization systems. Considering that sensors can be deployed to non-line-of-sight (NLOS) environments to collect information, wireless sensor network technology is used to locate positions in complex NLOS environments to meet the growing positioning needs of people. In this paper, we propose a novel time of arrival (TOA)-based localization scheme. We regard the line-of-sight (LOS) environment and non-line-of-sight environment in wireless positioning as a Markov process with two interactive models. In the NLOS model, we propose a modified probabilistic data association (MPDA) algorithm to reduce the NLOS errors in position estimation. After the NLOS recognition, if the number of correct positions is zero continuously, it will lead to inaccurate localization. In this paper, the NLOS tracer method is proposed to solve this problem to improve the robustness of the probabilistic data association algorithm. The simulation and experimental results show that the proposed algorithm can mitigate the influence of NLOS errors and achieve a higher localization accuracy when compared with the existing methods.

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Section: Experiments and Results Analysismentioning

confidence: 99%

A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network

et al. 2019

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“…However, challenges arise in harsh environments such as indoor environments and urban areas [11,12] and where the positions of the anchor nodes are uncertain [13][14][15]. In environments where there are many obstructions or scatterers, the paths between the anchor nodes and the localizing node may contain non-LOS (NLOS) paths [16]. As a result, localization needs to be achieved in a mixed LOS-NLOS environment and becomes much challenging.…”

Section: A Motivation and Literature Reviewmentioning

confidence: 99%

“…Although the above-mentioned methods can achieve good localization accuracy, one would expect that the accuracy can be improved if the NLOS measurements are also employed in the localization instead of being discarded. To this end, [16] adopted the Gaussian mixture model (GMM) to represent the NLOS-LOS measurement errors. By using the extended Kalman filter (EKF), both NLOS and LOS measurements were combined to estimate the target's location.…”

Section: A Motivation and Literature Reviewmentioning

confidence: 99%

Robust Localization for Mixed LOS/NLOS Environments With Anchor Uncertainties

Yang

et al. 2020

IEEE Trans. Commun.

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Localization is particularly challenging when the environment has mixed line-of-sight (LOS) and non-LOS paths and even more challenging if the anchors' positions are also uncertain. In the situations in which the parameters of the LOS-NLOS propagation error model and the channel states are unknown and uncertainties for the anchors exist, the likelihood function of a localizing node is computationally intractable. In this paper, assuming the knowledge of the prior distributions of the error model parameters and that of the channel states, we formulate the localization problem as the maximization problem of the posterior distribution of the localizing node. Then we apply variational distributions and importance sampling to approximate the true posterior distributions and estimate the target's location using an asymptotic minimum mean-square-error (MMSE) estimator. Furthermore, we analyze the convergence and complexity of the proposed variational Bayesian localization (VBL) algorithm. Computer simulation results demonstrate that the proposed algorithm can approach the performance of the Bayesian Cramer-Rao bound (BCRB) and outperforms conventional algorithms. Index Terms Bayesian Cramer-Rao bound (BCRB), variational Bayesian localization (VBL), mixed LOS/NLOS measurements, anchor node uncertainties, asymptotic minimum mean square error (MMSE).

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“…The mixed filter can mitigate NLOS error effectively. Cui et al 31 suggested using a Gaussian mixture model (GMM), interacting multiple models, and EKF to locate the mobile node in the NLOS environment. They cope with the changing transmission channels with three conditions.…”

Section: Related Workmentioning

confidence: 99%

A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things

Lin

Luan

et al. 2020

International Journal of Distributed Sensor Networks

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As the key technology for Internet of things, wireless sensor networks have received more attentions in recent years. Mobile localization is one of the significant topics in wireless sensor networks. In wireless sensor network, non-line-of-sight propagation is a common phenomenon leading to the growing non-line-of-sight error. It is a fatal impact for the localization accuracy of the mobile target. In this article, a novel method based on the nearest neighbor variable estimation is proposed to mitigate the non-line-of-sight error. First, the linear regression model of the extended Kalman filter is used to obtain the residual of the distance measurement value. After that, the residual analysis is used to complete the identification of the measurement value state. Then, by analyzing the statistical characteristics of the non-line-of-sight residual, the nearest neighbor variable estimation is proposed to estimate the probability density function of residual. Finally, the improved M-estimation is proposed to locate the mobile robot. Experiment results prove that the accuracy and robustness of the proposed algorithm are better than other methods in the mixed line-of-sight/non-line-of-sight environment. The proposed algorithm effectively inhibits the non-line-of-sight error.

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Robust Mobile Location Estimation in NLOS Environment Using GMM, IMM, and EKF

Cited by 37 publications

References 34 publications

A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network

A Robust Tracking Algorithm Based on a Probability Data Association for a Wireless Sensor Network

Robust Localization for Mixed LOS/NLOS Environments With Anchor Uncertainties

A mobile localization method based on a robust extend Kalman filter and improved M-estimation in Internet of things

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