A Localization and Tracking Approach in NLOS Environment Based on Distance and Angle Probability Model

Tian, Xin; Wei, Guoliang; Wang, Jianhua; Zhang, Dianchen

doi:10.3390/s19204438

Cited by 7 publications

(7 citation statements)

References 28 publications

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“…Therefore, if multiple tags are present, it is better to use distances from the tags to estimate the location, just like the radio-frequency (RF) signal localization schemes do. [21][22][23][24][25][26][27][28][29][30]…”

Section: Image-tag-based Localization With DLmentioning

confidence: 99%

Image-tag-based indoor localization using end-to-end learning

Alarfaj

Liu

et al. 2021

International Journal of Distributed Sensor Networks

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Image or feature matching-based indoor localization still faces many technical challenges. Image-tag-based schemes using pose estimation are accurate and robust, but they still cannot be deployed widely because their performance degrades significantly when the tag-camera distance is large, which requires densely distributed tags, and the designed system generally is specific to some special tags and lenses. Also, the lens distortion degrades the performance appreciably and is difficult to correct, especially for the wide-angle lenses. This article develops an image-tag-based indoor localization system using end-to-end learning to overcome these issues. It is a deep learning–based system that can learn the mapping from the original tag image to the final 2D location directly from training examples through self-learned features. It achieves consistent performance even when the tag-camera distance is large or when the image has a low resolution. The mapping learned by the deep learning model factors in all kinds of distortions without requiring any distortion estimation. The tag design is based on shape features to make it robust to lighting changes. The system can be easily adapted to new lenses/cameras and/or new tags. Thus, it facilitates easy and rapid deployment without requiring knowledge from domain experts. A drawback of the general deep learning model is its high computational requirements. We discuss practical solutions to enable real-time applications of the proposed scheme even when it is running on a mobile or embedded device. The performance of the proposed scheme is evaluated via a set of experiments in a real setting and has achieved less than 20 cm of positioning errors.

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Section: Image-tag-based Localization With DLmentioning

confidence: 99%

Image-tag-based indoor localization using end-to-end learning

Alarfaj

Liu

et al. 2021

International Journal of Distributed Sensor Networks

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“…The authors of [20] jumped out of the framework of optimizing the algorithm and successfully extracted more accurate measurement data. Tian and his partners proposed a distance and angle probability model so as to identify the NLOS propagation in [21]. This model can work well in a more specific NLOS environment.…”

Section: Related Workmentioning

confidence: 99%

“…If the measurement value with such error is not properly handled, the positioning effect will be significantly worse. Up to now, although there have been a variety of ways to judge and solve NLOS errors such as those listed in Related Work [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], how to reduce and weaken the impact of NLOS error on positioning is still a big problem to be solved.…”

Section: Introductionmentioning

confidence: 99%

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

2021

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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.

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“…Then, in the weight update step, iterative re-weighting of the least square regression is used to determine the receiving node position with the measured distance information. The author in [ 7 ] identifies the NLOS propagation through distance and angular probability, and a distance and angle probability model is proposed based on the sampling information to identify the NLOS propagation, followed by the maximum likelihood estimation (MLE) method to reduce the distance error in NLOS propagation through the model established previously. The improved Monte Carlo method is used to compute the best location of the mobile node, which reduces the computational complexity.…”

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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A Localization and Tracking Approach in NLOS Environment Based on Distance and Angle Probability Model

Cited by 7 publications

References 28 publications

Image-tag-based indoor localization using end-to-end learning

Image-tag-based indoor localization using end-to-end learning

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

An Indoor Robust Localization Algorithm Based on Data Association Technique

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