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计算机工程 ›› 2013, Vol. 39 ›› Issue (4): 52-57. doi: 10.3969/j.issn.1000-3428.2013.04.013

• 先进计算与数据处理 • 上一篇    下一篇

基于分布式BLUE的多雷达数据融合方法

付 莹1a,1b,2,孙永健1b,3,汤子跃1b   

  1. (1. 空军预警学院 a. 研管大队;b. 空天基预警监视装备系,武汉 2. 中国人民解放军95333部队,长沙 410114;3. 北京无线电测量研究所,北京 100039)
  • 收稿日期:2012-05-21 出版日期:2013-04-15 发布日期:2013-04-12
  • 作者简介:付 莹(1982-),女,博士研究生,主研方向:雷达信号与数据处理,多传感器数据融合;孙永健,讲师、博士研究生;汤子跃,教授、博士后

Multi-radar Data Fusion Method Based on Distributed BLUE

FU Ying 1a,1b,2, SUN Yong-jian 1b,3, TANG Zi-yue 1b   

  1. (1a. Department of Graduate Management; 1b. Department of Air/Space-Based Early Warning Surveillance Equipment, Air Force Early Warning Academy, Wuhan 430019, China; 2. PLA 95333 Troops, Changsha 410114, China; 3. Beijing Institute of Radio Measurement, Beijing 100039, China)
  • Received:2012-05-21 Online:2013-04-15 Published:2013-04-12

摘要: 针对线性系统与非线性观测的混合跟踪融合问题,推导目标匀加速运动时的三维最佳线性无偏估计(BLUE)滤波器,给出基于分布式BLUE滤波的多雷达数据融合方法。由各单元雷达对直角坐标系下的目标状态进行BLUE估计,对多部雷达的目标状态在融合中心进行融合估计,采用位置、速度均方根误差和平均归一化估计误差平方作为融合性能评价标准。仿真结果表明,与基于NC的去偏转换状态融合方法和基于MMC的无偏转换状态融合方法相比,该方法对于过程噪声和测量噪声的变化不敏感,比基于NC和MMC的方法具有更小的位置和速度均方根误差,即使在大误差的情况下,仍然具有较高的融合精度和可靠性。

关键词: 数据融合, 分布式BLUE滤波, 多雷达, 去偏转换, 无偏转换, 非线性量测

Abstract: Aiming at the problem of the mixed tracking and fusion problem of linear system and nonlinear observation, a multi-radar data fusion method based on distributed Best Linear Unbiased Estimation(BLUE) filter is provided under the premise that three- dimensional BLUE filter of constant acceleration target motion model is derived. In which, the BLUE target states at cell radar are given in Cartesian coordinates. All the target states estimations are fused at fusion center. The advanced technique is compared with the traditional Nested Conditioning(NC) debiased conversion states fusion method and the Modified Measurement Conditioned(MMC) unbiased conversion states fusion method by taking the position Root-mean-square Error(RMSE), velocity RMSE and Average Normalized Estimation Error Squared(ANEES) as the fusion performance evaluation criterion. Simulation results show that the advanced technique is not sensitive with the variety of the process noise and measurement noise. Its position and velocity RMSE are smaller than the corresponding of NC and MMC based method. It has high fusion accuracy and reliability, even if the errors become larger.

Key words: data fusion, distributed Best Linear Unbiased Estimation(BLUE) filtering, multi-radar, debiased conversion, unbiased conversion, nonlinear measurement

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