引入斜距和高程预测的BLUE跟踪方法

最佳线性无偏估计(BLUE:Best Linear Unbiased Estimation)算法用于目标跟踪时,受斜距、高程参量间的"共线"效应影响,对近程目标估计误差会增大甚至发散。针对此问题,在量测转换模型中引入斜距、高程预测,构建斜距、高程参量有偏估计,抑制"共线"效应。基于非线性参数误差最小准则推导斜距、高程估计的权值和偏置,建立基于非线性观测和状态预测融合估计的量测转换模型。基于该模型的BLUE算法能更精确的捕捉转换量测误差特性,以较小计算代价获得性能提升,数值仿真鲁棒性好,有很好应用前景。

BLUE Tracking Algorithm with Predicted Slant Range and Altitude

BLUE(Best linear unbiased estimation) filter can be used for target tracking. Influenced by the colinearity between the slant range measurement and the altitude measurement, BLUE filter's estimation may degrade or diverge for close-range target tracking. To solve this problem, the biased weighted estimates of slant range and altitude were employed in the converted measurements to alleviate the colinearity. The bias and weighing of the slant range and altitude parameters were derived based on the minimum mean square error criteria. The converted measurement model with fusion of nonlinear measurement and state prediction was built. The improved BLUE algorithm was able to estimate the statistics of the converted measurements more accurately, hence the filtering accuracy was improved. Simulation results verified this model can greatly improve the performances with minor computational burden. It was also shown to have excellent robustness in numerical examples, which proved it to be a practical approach.