A Data Association Algorithm Based on Fuzzy Synthetic Closeness in the Passive Sensor Networks

LI Xue; LI Peng-fei; TIAN Jin-wen; HUANG Jing-xiong

doi:10.3969/j.issn.0372-2112.2014.09.023

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A Data Association Algorithm Based on Fuzzy Synthetic Closeness in the Passive Sensor Networks

更新时间：2025-07-16

- A Data Association Algorithm Based on Fuzzy Synthetic Closeness in the Passive Sensor Networks
- Acta Electronica Sinica Vol. 42, Issue 9, Pages: 1812-1817(2014)
- 作者机构：
  
  1. 华中科技大学,湖北,武汉,430000
  2. 防空兵学院,河南,郑州,450052
  3. 深圳大学光电工程学院,广东,深圳,518060
  4. 华中科技大学,湖北,武汉,430000
  5. 防空兵学院,河南,郑州,450052
  6. 深圳大学光电工程学院,广东,深圳,518060
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2014.09.023
  CLC： TP391TN953
- Published：2014
- 稿件说明：
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LI Xue, LI Peng-fei, TIAN Jin-wen, et al. A Data Association Algorithm Based on Fuzzy Synthetic Closeness in the Passive Sensor Networks[J]. Acta Electronica Sinica, 2014, 42(9): 1812-1817.
DOI：

LI Xue, LI Peng-fei, TIAN Jin-wen, et al. A Data Association Algorithm Based on Fuzzy Synthetic Closeness in the Passive Sensor Networks[J]. Acta Electronica Sinica, 2014, 42(9): 1812-1817. DOI： 10.3969/j.issn.0372-2112.2014.09.023.

摘要

针对被动传感器采样非周期且采样数据缺乏距离信息等特点，提出了一种用于解决目标航迹与传感器量测相关联的模糊综合贴近度的数据关联算法.由于被动传感器的量测没有距离信息且传感器探测范围小，本算法首先设置两个关联波门进行量测筛选；然后采用航向确定法得出航向角信息，并综合方位角、俯仰角信息，使用模糊综合的方法进行最终的关联，以解决关联错误率高的问题；最后使用扩展卡尔曼滤波进行目标状态与协方差的更新.实验结果证明了该算法方法的有效性.

Abstract

In the passive sensor networks

the sampling data are aperiodic and lack of distance information

a data association algorithm is proposed based on fuzzy synthetic closeness.Because the measurements of passive sensor were lack of distance information and the detection ranges of passive sensor were small

the algorithm used two thresholds to select the effective measurements;then the heading-angle was obtained by the heading-angle definition method;at last the fuzzy synthetic closeness algorithm was used for the final data association

which combined the information of the azimuths and the elevations to resolve the problem of high error rate of association.The extended Kalman filter is used to update the target state and covariance.The experimental results show the validity of this algorithm.

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