基于分布式BLUE的多雷达数据融合方法

Multi-radar Data Fusion Method Based on Distributed BLUE

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。

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.