Target tracking based on improved square root cubature particle filter via underwater wireless sensor networks

Feng, Hailin; Cai, Zhiwei

doi:10.1049/iet-com.2018.5097

Cited by 22 publications

(8 citation statements)

References 19 publications

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“…When the measurement model is strong non-Gaussian, the accuracy of tracking would drop down. The particle filter can effectively tackle this problem [ 27 ]. The core idea of PF is to estimate the probability density function (PDF) of states using random samples in the state space but it will bring about the problem of particle degradation.…”

Section: Related Workmentioning

confidence: 99%

A Grey Model and Mixture Gaussian Residual Analysis-Based Position Estimator in an Indoor Environment

Wang

Ren

Cheng

et al. 2020

Sensors

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As the progress of electronics and information processing technology continues, indoor localization has become a research hotspot in wireless sensor networks (WSN). The adverse non-line of sight (NLOS) propagation usually causes large measurement errors in complex indoor environments. It could decrease the localization accuracy seriously. A traditional grey model considers the motion characteristics but does not take the NLOS propagation into account. A robust interacting multiple model (R-IMM) could effectively mitigate NLOS errors but the clipping point is hard to choose. In order to easily cope with NLOS errors, we present a novel filter framework: mixture Gaussian fitting-based grey Kalman filter structure (MGF-GKFS). Firstly, grey Kalman filter (GKF) is proposed to pre-process the measured distance, which can mitigate the process noise and alleviate NLOS errors. Secondly, we calculate the residual which is the difference between the filtered distance of GKF and the measured distance. Thirdly, a soft decision method based on mixture Gaussian fitting (MGF) is proposed to identify the propagation condition through residual value and give the degree of membership. Fourthly, weak NLOS noise is further processed by unscented Kalman filter (UKF). The filtered results of GKF and UKF are weighted using the degree of membership. Finally, a maximum likelihood (ML) algorithm is applied to get the coordinate of the target. MGF-GKFS is not supported by any of the priori knowledge. Full-scale simulations and an experiment are conducted to compare the localization accuracy and robustness with the state-of-the-art algorithms, including robust interacting multiple model (R-IMM), unscented Kalman filter (UKF) and interacting multiple model (IMM). The results show that MGF-GKFS could achieve significant improvement compared to R-IMM, UKF and IMM algorithms.

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Section: Related Workmentioning

confidence: 99%

A Grey Model and Mixture Gaussian Residual Analysis-Based Position Estimator in an Indoor Environment

Wang

Ren

Cheng

et al. 2020

Sensors

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“…Particle filter is the most preferred to approximate such non‐linear Bayesian problems. As recursive inference procedure, it provides a tribalistic for dynamic state estimations [49, 50]. It operates based on three important steps: Dealing with sampling whereabouts the generation of the states hypotheses for the states to be generated on behalf of the candidate probability distribution. Based on the dynamical modelling. The phase based on the observation in which the selection of the hypotheses is based on the agreement of the best observation model. …”

Section: System Modelmentioning

confidence: 99%

Efficient‐spectrum management based on localisation of primary user position towards 5G

2020

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“…By deploying several passive underwater buoys in an interested sea area to different depth, an uncooperative target can be tracked with full 3D observability if combined angle-only measurements are utilized. Therefore, developing passive tracking techniques which depend on the distributed underwater buoys has attracted attention from various researchers and rich research outcomes have been obtained [ 12 , 13 , 14 ]. Nevertheless, there are also several crucial issues to be further studied.…”

Section: Introductionmentioning

confidence: 99%

“…However, for the underwater target tracking scenario, the unknown underwater environment will probably influence the kinematics of the target and the measurements made by every underwater buoy so that the noise can be time-varying and non-Gaussian. Considering this assumption, only a limited number of particle filter (PF)-based researches that utilize the Bayesian posterior estimation method to overcome the Gaussian white noise limitation are designed [ 23 , 24 ], and very few researchers pay attention to the time-varying characteristic of the underwater noise.…”

Section: Introductionmentioning

confidence: 99%

3D Underwater Uncooperative Target Tracking for a Time-Varying Non-Gaussian Environment by Distributed Passive Underwater Buoys

Hou

Zhou

Yang

et al. 2021

Entropy

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Accurate 3D passive tracking of an underwater uncooperative target is of great significance to make use of the sea resources as well as to ensure the safety of our maritime areas. In this paper, a 3D passive underwater uncooperative target tracking problem for a time-varying non-Gaussian environment is studied. Aiming to overcome the low observability drawback inherent in the passive target tracking problem, a distributed passive underwater buoys observing system is considered and the optimal topology of the distributed measurement system is designed based on the nonlinear system observability analysis theory and the Cramer–Rao lower bound (CRLB) analysis method. Then, considering the unknown underwater environment will lead to time-varying non-Gaussian disturbances for both the target’s dynamics and the measurements, the robust optimal nonlinear estimator, namely the adaptive particle filter (APF), is proposed. Based on the Bayesian posterior probability and Monte Carlo techniques, the proposed algorithm utilizes the real-time optimal estimation technique to calculate the complex noise online and tackle the underwater uncooperative target tracking problem. Finally, the proposed algorithm is tested by simulated data and comprehensive comparisons along with detailed discussions that are made to demonstrate the effectiveness of the proposed APF.

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Target tracking based on improved square root cubature particle filter via underwater wireless sensor networks

Cited by 22 publications

References 19 publications

A Grey Model and Mixture Gaussian Residual Analysis-Based Position Estimator in an Indoor Environment

A Grey Model and Mixture Gaussian Residual Analysis-Based Position Estimator in an Indoor Environment

Efficient‐spectrum management based on localisation of primary user position towards 5G

3D Underwater Uncooperative Target Tracking for a Time-Varying Non-Gaussian Environment by Distributed Passive Underwater Buoys

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