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研究了通过雷达观测跟踪重返大气层阶段的弹道目标问题。考虑了一种状态方程和量测方程都具有高度非线性的数学模型并推导出估计误差的理论 Cramer-Rao 低界。我们设计了三种次最优滤波器并将其滤波性能和Cramer-Rao 低界进行了比较。除了在非线性滤波中经常采用的 EKF 和 UKF 之外,提出了一种结合传统卡尔曼滤波和简化点 Unscented 变换的滤波器,仿真结果表明,新滤波器在精度和计算复杂性上均有良好表现。
The ballistic target problem of returning to the atmosphere through radar observations was studied. Consider a mathematical model with both state and measurement equations highly nonlinear and derive the theoretical Cramer-Rao low bound of the estimation error. We have designed three sub-optimal filters and compared their filtering performance with the Cramer-Rao low bound. In addition to the commonly used EKF and UKF in nonlinear filtering, a filter combining traditional Kalman filter and Unscented transform is proposed. The simulation results show that the new filter has good accuracy and computational complexity which performed.