基于转换量测高斯化的三坐标雷达跟踪算法

3D Radar Tracking Algorithm with Gaussianized Converted Measurements

最佳线性无偏估计(BLUE,Best Linear Unbiased Estimation)滤波用于雷达目标跟踪时,有计算量小,置信度高等优点.但是当互斜距测量误差较大时,BLUE滤波会产生非高斯转换量测,导致跟踪精度降低.为解决此问题,对其量测转换模型进行修正:通过引入方位预测,减小方位误差三角函数的非线性影响,得到准高斯分布的转换量测.分析视线坐标系下BLUE滤波的性能,推导引入方位预测的条件,给出改进算法工作流程.推导三坐标雷达下的滤波模型参数,提出转换量测高斯化水平的评估指标并仿真证明:改进算法的转换量测更逼近高斯分布,因此跟踪性能更好,而计算量只有轻微增加.本算法思想同样适用于其他非线性误差较大的场合,对解决类似问题有借鉴意义.

Best linear unbiased estimation(BLUE)filter is widely used in radar target tracking for its efficiency and robustness.Its estimation performance is deteriorated by the non-Gaussian converted measurement noises under the condition of large cross-range errors.To mitigate the problem,a modified converted measurement model with predicted bearing is proposed.The predicted bearing can help to alleviate the nonlinearity caused by bearing error trigonometric function and obtain pseudo-Gaussian converted measurements.Theoretic analysis of the proposed BLUE filter in the line-of-sight coordinates is derived.The constraints to use the predicted bearing is derived.The filtering diagram of the improved algorithm is given.The filtering model parameters for 3D radar tracking system are derived.The Gaussian level indexes of the converted measurements are provided.Simulation results show the converted measurements of the proposed BLUE are more closer to the Gaussian distribution,so its accuracy improves significantly with minor computation burden,which means it can provide real-time and accurate estimation for radar target tracking.The fundamental idea also applies to other occasions with relatively large nonlinear errors,thus providing some references for similar applications.