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M. S. Arulampalam, S. Maskell, N. Gordon, et al., “A Tutorial on Particle Filters for Online Nonlinear/Non- Gaussian Bayesian Tracking,” IEEE Transactions on Sig- nal Processing, Vol. 50, No. 2, 2002, pp. 174-188. doi:10.1109/78.978374

has been cited by the following article:

• TITLE: Maneuvering Multi-target Tracking Algorithm Based on Modified Generalized Probabilistic Data Association

  AUTHORS: Zhentao Hu, Chunling Fu, Xianxing Liu

  KEYWORDS: Multi-Target Tracking, Particle Filter, Generalized Probabilistic Data Association, Clutters

  JOURNAL NAME: Engineering, Vol.3 No.12, December 14, 2011

  ABSTRACT: Aiming at the problem of strong nonlinear and effective echo confirm of multi-target tracking system in clutters environment, a novel maneuvering multitarget tracking algorithm based on modified generalized probabilistic data association is proposed in this paper. In view of the advantage of particle filter which can deal with the nonlinear and non-Gaussian system, it is introduced into the framework of generalized probabilistic data association to calculate the residual and residual covariance matrices, and the interconnection probability is further optimized. On that basis, the dynamic combination of particle filter and generalized probabilistic data association method is realized in the new algorithm. The theoretical analysis and experimental results show the filtering precision is obviously improved with respect to the tradition method using suboptimal filter.