北京航空航天大学学报 ›› 2014, Vol. 40 ›› Issue (10): 1392-1398.doi: 10.13700/j.bh.1001-5965.2013.0631

• 论文 • 上一篇    下一篇

同步三星目标运动状态快速检测方法

杨宇翔, 张汇川   

  1. 西南电子电信技术研究所, 成都 610041
  • 收稿日期:2013-11-05 出版日期:2014-10-20 发布日期:2014-10-29
  • 作者简介:杨宇翔(1984-),男,重庆人,博士生,yyxforever@163.com.
  • 基金资助:

    国家高技术研究发展计划(863计划)资助项目(2012AA7074012)

Fast algorithm of motion state detection based on tri geo-satellites

Yang Yuxiang, Zhang Huichuan   

  1. Southwest Electronics and Telecommunication Technology Research Institute, Chengdu 610041, China
  • Received:2013-11-05 Online:2014-10-20 Published:2014-10-29

摘要:

针对星载无源定位系统难以从定位结果及时准确地检测目标运动状态的问题,利用同步三星时差定位体制的定位误差特性,根据静止假设下同步双星时/频差与三星定位结果应相匹配的原理,提出了基于定位一致性的目标运动状态快速检测算法.先通过定位结果的误差分析掌握目标实际位置所在区域,然后在静止假设下统计该区域的频差范围,并将定位时刻的频差观测值与之比较,实现目标运动状态检测,并且推导了检测概率的理论表达式.仿真和试验结果验证了算法的有效性,时/频差估计误差标准差为10 μs/100 mHz时,对速度大于40 m/s的目标,检测概率高于99%.实测数据表明该算法显著提高了系统目标运动状态检测的准确性和时效性.

关键词: 频差, 时差, 无源定位, 误差椭圆, 运动状态检测

Abstract:

Aiming at the difficulty to detect the motion state of targets in the satellite-borne passive location system, a fast motion state detection algorithm, based on the principle that the location result of the dual-satellites geolocation using differential time offset (DTO) and differential frequency offset (DFO) should be consistent with that of the tri-satellites geolocation using dual DTOs under the stationary hypothesis, was proposed. Firstly the region of target was obtained by location error analysis. Then the DFO range of this region under the stationary hypothesis was obtained through the statistics. Finally the observed value of DFO at the location time was compared with the DFO range to detect the motion state of target. And the expression of theoretical detection probability was given. The algorithm was verified by the simulation and experiment results. For the targets with velocity above 40 m/s, the detection probabilities are more than 99% when the estimation precision of DTO/DFO is 10 μs/100 mHz. It is demonstrated by the practical data that the proposed algorithm helps the system improve the ability to detect motion state significantly.

Key words: differential frequency offset (DFO), differential time offset (DTO), passive location, error ellipse, motion state detection

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