Current Statistic Model and Adaptive Tracking Algorithm Based on Strong Tracking Filter

FAN Xiao-jun; LIU Feng; QIN Yong; ZHANG Jun

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Current Statistic Model and Adaptive Tracking Algorithm Based on Strong Tracking Filter

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- Current Statistic Model and Adaptive Tracking Algorithm Based on Strong Tracking Filter
- Acta Electronica Sinica Vol. 34, Issue 6, Pages: 981-984(2006)
- 作者机构：
  
  北京航空航天大学电子信息工程学院,北京,100083
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN953
- Published：2006
- 稿件说明：
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FAN Xiao-jun, LIU Feng, QIN Yong, et al. Current Statistic Model and Adaptive Tracking Algorithm Based on Strong Tracking Filter[J]. Acta Electronica Sinica, 2006, 34(6): 981-984.
DOI：

FAN Xiao-jun, LIU Feng, QIN Yong, et al. Current Statistic Model and Adaptive Tracking Algorithm Based on Strong Tracking Filter[J]. Acta Electronica Sinica, 2006, 34(6): 981-984. DOI：

摘要

在"当前"统计模型(CS)的基础上

提出了一种新的机动目标自适应跟踪算法STF-CS.该算法通过引入强跟踪滤波器(STF)的渐消因子

实时调节滤波器增益

增强了系统对突发机动的自适应跟踪能力

同时保留了"当前"统计模型跟踪算法对一般机动目标跟踪精度高的特点.仿真结果表明

在跟踪一般机动目标时

其误差和"当前"统计模型算法相当;在跟踪突发机动目标时

本文算法的误差明显小于"当前"统计模型及自适应算法.

Abstract

A new adaptive maneuvering target tracking algorithm STF-CS is presented based on the "current" statistical model (CS).By introducing a fading factor of Strong Tracking Filter (STF)

this algorithm improves the adaptive tracking performance greatly when there is a sudden maneuver

and has the same high precision to track common maneuvering targets as the "current" statistical model and adaptive tracking algorithm.Simulation results show that

when there is only common maneuver the performance of the two algorithms is the same

when there is a sudden maneuver

the performance of STF-CS is much better than the CS.

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