Abstract
We describe the design of a multiple maneuvering targets tracking algorithm under the framework of Gaussian mixture probability hypothesis density (PHD) filter. First, a variation of the generalized pseudo-Bayesian estimator of first order (VGPB1) is designed to adapt to the Gaussian mixture PHD filter for jump Markov system models (JMS-PHD). The probability of each kinematic model, which is used in the JMS-PHD filter, is updated with VGPB1. The weighted sum of state, associated covariance, and weights for Gaussian components are then calculated. Pruning and merging techniques are also adopted in this algorithm to increase efficiency. Performance of the proposed algorithm is compared with that of the JMS-PHD filter. Monte-Carlo simulation results demonstrate that the optimal subpattern assignment (OSPA) distances of the proposed algorithm are lower than those of the JMS-PHD filter for maneuvering targets tracking.
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Project supported by the National Natural Science Foundation of China (Nos. 61175008, 60935001, and 61104210), the Aviation Foundation (No. 20112057005), and the National Basic Research Program (973) of China (No. 2009CB824900)
A preliminary version was presented at the 7th IEEE Conference on Industrial Electronics and Applications, July 18–20, 2012, Singapore
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Zhang, Sc., Li, Jx., Wu, Lb. et al. Amultiplemaneuvering targets tracking algorithm based on a generalized pseudo-Bayesian estimator of first order. J. Zhejiang Univ. - Sci. C 14, 417–424 (2013). https://doi.org/10.1631/jzus.C1200310
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DOI: https://doi.org/10.1631/jzus.C1200310
Key words
- Gaussian mixture PHD filter
- Jump Markov system
- Generalized pseudo-Bayesian estimator of first order (GPB1)
- Multi-target tracking