机动目标单站无源定位中的测量更新CKF-IMM算法

A CKF-IMM Update Measurement Algorithm for Single Observer Passive Location of Maneuvering Target

机动目标单站无源定位是一个典型的非线性滤波问题,将一种新型的滤波算法)))容积卡尔曼滤波(CKF)应用于IMM算法之中。为进一步提高定位跟踪精度,提出了一种测量更新CKF-IMM算法。该算法利用马尔科夫过程控制子模型间的切换,并采用CKF算法对各模型进行滤波,然后将每个滤波器的输出状态进行概率加权求和,最后对融合状态再进行一次非线性测量更新。结合空频域单站无源定位模型进行仿真实验表明,与传统的EKF-IMM和UKF-IMM算法相比,CKF-IMM算法的估计误差更小、定位精度更高;而测量更新CKF-IMM算法较CKFIMM算法可进一步提高定位跟踪精度。

Single observer passive location of a maneuvering target is in a typical nonlinear filtering, a new filtering algorithm-cubature Kalman filter（ CKF） is applied to IMM. To improve the location and tracking precision, an update CKF-IMM measurement algorithm is proposed. This algorithm uses Markov process to control the switching among the sub-models, and uses CKF for filtering of each model. The outputs of all parallel CKF are weighted stun as an integrated estimation and the integrat- ed estimation is put through the nonlinear update measurement. Combining with the spatial-frequency domain model, simulation results show that CKF-IMM has lower estimation error and higher precision comparing with the EKF-IMM and UKF-IMM ; The CKF-IMM update measurement is of better location and tracking performance than CKF-IMM.