GREY PREDICTION BASED PARTICLE FILTER FOR MANEUVERING TARGET TRACKING

The multiple maneuvering target tracking algorithm based on a particle filter is addressed. The equivalent-noise approach is adopted, which uses a simple dynamic model consisting of target state and equivalent noise which accounts for the combined effects of the process noise and maneuvers. The equivalent-noise approach converts the problem of maneuvering target tracking to that of state estimation in the presence of nonstationary process noise with unknown statistics. A novel method for identifying the nonstationary process noise is proposed in the particle filter framework. Furthermore, a particle filter based multiscan Joint Probability Data Association (JPDA) filter is proposed to deal with the data association problem in a multiple maneuvering target tracking. In the proposed multiscan JPDA algorithm, the distributions of interest are the marginal filtering distributions for each of the targets, and these distributions are approximated with particles. The multiscan JPDA algorithm examines the joint association events in a multiscan sliding window and calculates the marginal posterior probability based on the multiscan joint association events. The proposed algorithm is illustrated via an example involving the tracking of two highly maneuvering, at times closely spaced and crossed, targets, based on resolved measurements.

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Fuzzy Quadrature Particle Filter for Maneuvering Target Tracking

International Journal of Fuzzy Systems ◽

10.1007/s40815-015-0105-5 ◽

2015 ◽

Vol 18 (4) ◽

pp. 647-658 ◽

Cited By ~ 4

Author(s):

Liang-qun Li ◽

Chun-lan Li ◽

Wen-ming Cao ◽

Zong-Xiang Liu

Keyword(s):

Particle Filter ◽

Target Tracking ◽

Maneuvering Target Tracking ◽

Maneuvering Target

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Hummingbirds optimization algorithm-based particle filter for maneuvering target tracking

Nonlinear Dynamics ◽

10.1007/s11071-019-05043-0 ◽

2019 ◽

Vol 97 (2) ◽

pp. 1227-1243 ◽

Cited By ~ 3

Author(s):

Zhuoran Zhang ◽

Changqiang Huang ◽

Dali Ding ◽

Shangqin Tang ◽

Bo Han ◽

...

Keyword(s):

Particle Filter ◽

Target Tracking ◽

Optimization Algorithm ◽

Maneuvering Target Tracking ◽

Maneuvering Target

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Maneuvering Target Tracking Algorithm Based on Particle PHD Filtering

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.2212 ◽

2014 ◽

Vol 989-994 ◽

pp. 2212-2215

Author(s):

Song Gao ◽

Chao Bo Chen ◽

Qian Gong

Keyword(s):

Particle Filter ◽

Target Tracking ◽

Tracking Accuracy ◽

Maneuvering Target Tracking ◽

Probability Hypothesis Density ◽

Filtering Algorithm ◽

Maneuvering Target ◽

Maneuvering Targets ◽

The Status ◽

Simulation Results

As for the problem of maneuvering target tracking in the clutter environment, this paper combines IMM with PHD and realizes it through approach of particle filter. This algorithm avoids the troublesome problem of data association, and takes advantage of probability hypothesis density (PHD) filter in tracking maneuvering targets and interacting multi-model (IMM) algorithm in the field of model switching effectively, in the clutter environment, the status of the targets can be estimated precisely and steadily. This paper compares the proposed filtering algorithm with the classical IMM algorithm in performance, and the simulation results show that, the improved filtering algorithm has good tracking performance and tracking accuracy.

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