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首先在雷达直角坐标系下建立了再入目标的动力学模型,针对其运动的非线性,采用了三种经典的非线性滤波,即扩展卡尔曼滤波,不敏卡尔曼滤波以及粒子滤波进行弹道参数估计,给出了估计性能(误差均值和标准偏差),并与理论后验Cramer-Rao下限进行了对比。计算结果表明,在弹道参数采取指数建模的情况下,三种滤波的性能大致相同,从计算量、滤波性能和滤波的稳健性上综合考虑,不敏卡尔曼滤波更胜一筹。
First of all, a kinetic model of reentry target was set up in Cartesian coordinate system of radar. According to its nonlinearity of motion, three kinds of classical nonlinear filtering, namely Extended Kalman Filter, Unstirred Kalman Filter and Particle Filter The parameter estimation gives the estimated performance (error mean and standard deviation) and is compared with the theoretical posterior Cramer-Rao lower bound. The calculation results show that the performance of the three kinds of filtering is roughly the same when the ballistic parameters are modeled exponentially. The unstirred Kalman filtering is superior to the computational complexity, the filtering performance and the robustness of the filtering.