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

Abstract

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.

A Novel NLOS Suppression Algorithm for Indoor Location based on FCM and REKF

“…The ingenious combination of the processing methods can also greatly improve the positioning performance. The probability data association (PDA) are used as the NLOS processing method in the interactive multi-model (IMM) in paper [7] and achieves higher accuracy. Paper [8] combines the measurement methods TOA and RSSI which realize the mutual compensation of two measurements.…”

A Mobile Positioning Algorithm Based on Improved Robust Extended Kalman Filter with NLOS Identification and Mitigation

“…IMM can also use different filters to process measurements for different models. The REKF are used to deal with NLOS errors [14]; similarly, the PIMM mainly adopts probability data association filter (PDAF) to eliminate NLOS errors in IMM [19]. In general, the IMM performs better than the traditional techniques because it utilizes prior statistics about NLOS errors.…”

“…When 40 a X  , there is an inflection point of the trajectory. We mainly compared our algorithm with the conventional EKF [15], REKF [10], IMM-EKF [14] and PIMM [19]. The specific positioning performances of these algorithms in simulation are shown in the following section.…”

Special Issue on “Recent Advances in Indoor Localization Systems and Technologies”

“…Paper [32] proposes a method to ensure the accuracy and reliability of positioning results in indoor pseudolite systems: An adaptive fault-detection method is applied to find and exclude potentially faulty pseudolite measurements, influenced by multipath effects, clock drift, or noise. In papers [33][34][35], problems of line-of-sight (LOS) and non-line-of-sight (NLOS) situations are studied in radio-based systems, where accurate ranging is possible with LOS. These papers propose various NLOS identification methods, which help to eliminate the influence of NLOS errors on positioning accuracy.…”

“…These papers propose various NLOS identification methods, which help to eliminate the influence of NLOS errors on positioning accuracy. In [33], a NLOS tracer method is proposed based on the improved Modified Probabilistic Data Association and Interacting Multiple Model algorithms. In [34], a Gaussian model is proposed to identify NLOS signals, while [35] applies machine learning methods to identify not only LOS and NLOS, but also multipath situations.…”