自然科学版 英文版
自然科学版 英文版
自然科学版 英文版

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中南大学学报(英文版)

Journal of Central South University

Vol. 22    No. 10    October 2015

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Maneuvering target track-before-detect viamultiple-model Bernoulli particle filter
ZHAN Rong-hui(占荣辉), LIU Sheng-qi(刘盛启), HU Jie-min(胡杰民), ZHANG Jun(张军)

Science and Technology on Automatic Target Recognition Laboratory,
National University of Defense Technology, Changsha 410073, China

Abstract:Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target’s maneuver makes it even more challenging. In this work, a multiple-model based method was proposed to tackle such issues. The method was developed in the framework of Bernoulli filter by integrating the model probability parameter and implemented via sequential Monte Carlo (particle) technique. Target detection was accomplished through the estimation of target’s existence probability, and the estimate of target state was obtained by combining the outputs of model- dependent filtering. The simulation results show that the proposed method performs better than the TBD method implemented by the conventional multiple-model particle filter.

 

Key words: Bernoulli filter; multiple model; target maneuver; track-before-detect (TBD); sequential Monte Carlo (SMC) technique

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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