基于卡尔曼滤波预测的无偏量测转换方法

针对在目标跟踪系统中通常使用量测转换方法将球面坐标系下的量测量转换到笛卡尔坐标系下，传统量测转换方法在互距离测量误差增大时跟踪性能有所下降的问题，提出一种基于卡尔曼滤波预测的无偏量测转换方法。采用无偏量测转换方法变换量测量，基于卡尔曼滤波预测值，结合无迹变换算法估计转换量测方差。仿真结果表明，所提出的转换量测卡尔曼滤波算法较现有方法具有更高的跟踪精度和可信度。

Tracking with prediction-conditioned unbiased converted measurements

In target tracking system, the converted measurement method is usually used to convert spherical measurements to a Cartesian frame of reference. The performance of the conventional converted measurement degrades for large crossrange errors. A prediction-conditioned unbiased conversion is presented. The unbiased conversion is used and the covariance of the converted measurements is approximated by the Kalman prediction and unscented transform. Simulation results show that the converted measurement Kalman filter proposed is more accurate and credible.