scholarly journals A study on data association based on multiple model for improving target tracking performance in maneuvering interval in bistatic sonar environments

2017 ◽  
Vol 36 (3) ◽  
pp. 202-210
Author(s):  
Seung-Hyo Park ◽  
Taek-Lyul Song ◽  
Seung-Ho Lee
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Tracking Performance ◽  
Multiple Model

scholarly journals Variational Bayesian Based Adaptive Shifted Rayleigh Filter for Bearings-Only Tracking in Clutters

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1512 ◽  
Author(s):  
Jing Hou ◽  
Yan Yang ◽  
Tian Gao
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Tracking Performance ◽  
Computation Complexity ◽  
Variational Bayesian ◽  
Target State ◽  
Scenarios Simulation ◽  
Simulation Results

This paper considers bearings-only target tracking in clutters with uncertain clutter probability. The traditional shifted Rayleigh filter (SRF), which assumes known clutter probability, may have degraded performance in challenging scenarios. To improve the tracking performance, a variational Bayesian-based adaptive shifted Rayleigh filter (VB-SRF) is proposed in this paper. The target state and the clutter probability are jointly estimated to account for the uncertainty in clutter probability. Performance of the proposed filter is evaluated by comparing with SRF and the probability data association (PDA)-based filters in two scenarios. Simulation results show that the proposed VB-SRF algorithm outperforms the traditional SRF and PDA-based filters especially in complex adverse scenarios in terms of track continuity, track accuracy and robustness with a little higher computation complexity.

scholarly journals IMM Fifth-Degree Spherical Simplex-Radial Cubature Filter for Maneuvering Target Tracking

2017 ◽  
Author(s):  
Hua Liu ◽  
Wen Wu
Keyword(s):  
Target Tracking ◽  
Tracking Performance ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Scenario Simulation ◽  
Tracking Accuracy ◽  
Maneuvering Target Tracking ◽  
Switching Speed ◽  
Maneuvering Target ◽  
Spherical Simplex

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named interacting multiple model fifth-degree spherical simplex-radial cubature filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and fifth-degree spherical simplex-radial cubature filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with IMMUKF, IMMCKF and IMM5thCKF.

Multiple Target Tracking in the Adaptive Cruise Control Environment Using Multiple Models and Probabilistic Data Association

2001 ◽  
Author(s):  
Derek Caveney ◽  
J. Karl Hedrick
Keyword(s):  
Target Tracking ◽  
Adaptive Cruise Control ◽  
Data Association ◽  
Multiple Model ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Cruise Control ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Position Errors

Abstract The multiple target tracking (MTT) performance of a new combination of the fuzzy interacting multiple model (FIMM) algorithm and the probabilistic data association filter (PDAF) is investigated. The ability of a set of these FIMMPDAFs to maintain the tracks of multiple targets in a cluttered adaptive cruise control (ACC) environment is compared to that of the likelihood approach, the IMMPDAF. The differences between the two methods are highlighted and simulation results for a typical highway driving scenario demonstrate the performance of each approach. These results show that both the IMMPDAF and FIMMPDAF strategies are capable of tracking multiple vehicles with low RMS position errors, while the FIMMPDAF appears to detect the initiation of a target maneuver more rapidly by adjusting model probabilities more quickly.

scholarly journals A Novel Probabilistic Data Association for Target Tracking in a Cluttered Environment

Sensors ◽  
2016 ◽  
Vol 16 (12) ◽  
pp. 2180 ◽  
Author(s):  
Xiao Chen ◽  
Yaan Li ◽  
Yuxing Li ◽  
Jing Yu ◽  
Xiaohua Li
Keyword(s):  
Target Tracking ◽  
Data Association ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Cluttered Environment
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