A novel variable structure multi-model approach based on error-ambiguity decomposition |
| |
Institution: | 1. Institution of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China;2. Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China |
| |
Abstract: | Model Set Adaptation (MSA) plays a key role in the Variable Structure Multi-Model tracking approach (VSMM). In this paper, the Error-Ambiguity Decomposition (EAD) principle is adopted to derive the EAD-MSA criterion that is optimal in the sense of minimizing the square error between the estimate and the truth. Consequently, the EAD Variable Structure first-order General Pseudo Bayesian (EAD-VSGPB1) algorithm and the EAD Variable Structure Interacting Multiple Model (EAD-VSIMM) algorithm are constructed. The proposed algorithms are tested in two groups of maneuvering target tracking scenarios under different modes and observation error conditions. The simulation results demonstrate the effectiveness of the EAD-VSMM approach and show that, compared to some existing multi-model algorithms, the proposed EAD-VSMM algorithms achieve more robust and accurate tracking results. |
| |
Keywords: | Error-ambiguity decomposition Maneuvering target tracking Model sequence set adaptation Multi-model estimation Variable structure |
本文献已被 ScienceDirect 等数据库收录! |
|