电子学报2017，Vol.45Issue(9)：2113-2120,8.DOI:10.3969/j.issn.0372-2112.2017.09.009
自适应转移概率交互式多模型跟踪算法
Interacting Multiple Model Algorithm Based on Adaptive Transition Probability
许登荣 1程水英 1包守亮1
作者信息
- 1. 国防科技大学电子对抗学院,安徽合肥230037
- 折叠
摘要
Abstract
There are two shortcomings in the standard interacting multiple model (IMM) algorithm:one is that designing models is difficult,the other is that the application of constant transition probability matrices makes the model switching speed slow and tracking accuracy decreased.To overcome these shortcomings,an IMM algorithm with adaptive transition probability is proposed.Firstly,a new model-set design method is proposed,and the strong tracking modified input estimation (STMIE) model and constant velocity (CV) model are adopted as the model sets of the IMM algorithm.By using the capability of STMIE model to track high maneuvering targets and the precision of CV model to track non-maneuvering targets,this algorithm can be comprehensively adaptive in target tracking.Secondly,a new method is proposed to modify the Markov transition probability in real time based on the likelihood values of the models,which enhances the effect of the matching model,and weakens the influence of the mismatched model.Simulation results show that the new method improves model switching speed and tracking precision of IMM algorithm,and the tracking precision of IMM-STM1ECV algorithm is higher than that of IMM-CVCA,IMM-CVCACT and IMM-CVCS algorithms.关键词
机动目标跟踪/交互式多模型算法/Markov转移概率/修正的输入估计法/强跟踪Key words
maneuvering target tracking/interacting multiple model (IMM)/markov transition probability/modified input estimation (MIE)/strong tracking filter (STF)分类
信息技术与安全科学引用本文复制引用
许登荣,程水英,包守亮..自适应转移概率交互式多模型跟踪算法[J].电子学报,2017,45(9):2113-2120,8.
