Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (4): 684-695.doi: 10.21629/JSEE.2019.04.06

• Electronics Technology • Previous Articles     Next Articles

Constrained auxiliary particle filtering for bearings-only maneuvering target tracking

Hongwei ZHANG*(), Weixin XIE()   

  • Received:2018-11-19 Online:2019-08-01 Published:2019-08-29
  • Contact: Hongwei ZHANG;
  • About author:ZHANG Hongwei was born in 1982. She is currently pursuing her Ph.D. degree in the College of Information Engineering at Shenzhen University. She received her B.S. degree in electronic engineering from Zhengzhou University in 2006 and M.S. degree from South China University of Technology in 2013. Her research interests are particle filtering and multiple target tracking.|XIE Weixin was born in 1941. He received his B.S. degree from Xidian University, Xi'an, and joined the faculty of Xidian University in 1965. From 1981 to 1983, he was a visiting scholar with the University of Pennsylvania, USA. In 1989, he was invited to University of Pennsylvania as a visiting professor. He is currently a professor with Shenzhen University. His research interests include intelligent information processing and pattern recognition.
  • Supported by:
    the National Natural Science Foundation of China(61773267);the Shenzhen Fundamental Research Project(JCYJ20170302145519524);the Shenzhen Fundamental Research Project(20170818102503604);This work was supported by the National Natural Science Foundation of China (61773267) and the Shenzhen Fundamental Research Project (JCYJ20170302145519524; 20170818102503604)


To track the nonlinear, non-Gaussian bearings-only maneuvering target accurately online, the constrained auxiliary particle filtering (CAPF) algorithm is presented. To restrict the samples into the feasible area, the soft measurement constraints are implemented into the update routine via the $\ell$1 regularization. Meanwhile, to enhance the sampling diversity and efficiency, the target kinetic features and the latest observations are involved into the evolution. To take advantage of the past and the current measurement information simultaneously, the sub-optimal importance distribution is constructed as a Gaussian mixture consisting of the original and modified priors with the fuzzy weighted factors. As a result, the corresponding weights are more evenly distributed, and the posterior distribution of interest is approximated well with a heavier tailor. Simulation results demonstrate the validity and superiority of the CAPF algorithm in terms of efficiency and robustness.

Key words: bearings-only maneuvering target tracking, soft measurement constraints, constrained auxiliary particle filtering (CAPF)