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多站测角的最小二乘交互多模型跟踪算法

宋骊平;姬红兵
  

  1. (西安电子科技大学 电子工程学院,陕西 西安 710071)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2008-04-20 发布日期:2008-03-28
  • 通讯作者: 宋骊平

Least squares interacting multiple model algorithm for passive multi-sensor maneuvering target tracking

SONG Li-ping;JI Hong-bing
  

  1. (School of Electronic Engineering, Xidian Univ., Xi′an 710071, China)
  • Received:1900-01-01 Revised:1900-01-01 Online:2008-04-20 Published:2008-03-28
  • Contact: SONG Li-ping

摘要: 在多站测角的被动目标跟踪中,目标的状态与角度量测值之间存在非线性关系,现有的方法主要是对其进行线性化,但线性化过程会带来滤波精度的下降,甚至会产生滤波发散而丢失目标.针对这一问题提出一种新方法,采用最小二乘法对多个观测站测得的目标角度信息进行融合,估计出目标的状态,将状态估计作为卡尔曼滤波的伪量测,然后采用交互多模型算法跟踪机动目标.仿真结果表明该方法可实现多站测角机动目标的跟踪,其跟踪误差远小于现有的跟踪方法.

关键词: 最小二乘, 交互多模型, 卡尔曼滤波, 被动跟踪

Abstract: In multi-sensor bearings-only passive target tracking, the state of the target has a nonlinear relation with the bearings measurements. Existing methods focus mainly on the process of linearization. However, in this process, a precision decrease is obviously unavoidable and even filter divergence will occur so as to lose the target. Therefore a new algorithm is proposed. The state of the target is approximately estimated by least squares first which is taken as pseudo measurements for the Kalman filter, and then the IMM algorithm is employed for maneuvering target tracking. Simulation results demonstrate that the algorithm for multi-sensor maneuvering target tracking is realizable and that the tracking error is far lower than that by the existing methods.

Key words: least squares, interacting multiple model, Kalman filter, passive tracking

中图分类号: 

  • TN957.51