一种面向临近空间高超声速再入滑翔目标跟踪算法

针对临近空间高超声速再入滑翔目标的跟踪问题，提出了一种基于回顾成本输入估计的无偏转换量测卡尔曼滤波(Retrospective cost input estimation-unbiased converted measurements Kalman filter, RCIE-UCMKF)。首先，根据再入滑翔目标的飞行特性，将加速度看成是未知的确定输入构建运动学跟踪模型；然后，对目标的非线性量测信息进行无偏转换，并将得到的噪声协方差矩阵进行解耦，降低算法的复杂度；最后，利用回顾成本的输入估计对未知加速度进行重构，采用递推最小二乘法更新输入估计器的参数矩阵，同时将估计的加速度引入到卡尔曼滤波框架下，实现对高超声速再入滑翔目标状态的准确估计。仿真结果表明了该算法的有效性和可行性。

A Tracking Algorithm for Near Space Hypersonic Reentry Glide Target#br#

An algorithm of unbiased converted measurement Kalman filter based on retrospective cost input estimation (RCIE-UCMKF) is proposed for near space hypersonic reentry glide target tracking. Firstly, according to the characteristics of the reentry glide target, the acceleration is regarded as an unknown deterministic input to construct the kinematics tracking model. Then, the target measurements are unbiasedly converted, and the noise covariance matrix is decoupled to reduce the computational complexity of the algorithm. Finally, the unknown acceleration is reconstructed by using the retrospective cost input estimation, the parameter matrix of the input estimator is updated by the recursive least squares method, and the estimated acceleration is introduced into the Kalman filter framework to effectively estimate the state of the hypersonic reentry glide target. The simulation results demonstrate the validness and feasibility of the proposed algorithm.