基于高阶累积量的目标机动检测新方法

机动检测是多模型目标跟踪中的一个关键问题.在卡尔曼滤波中,当目标机动被噪声淹没时,传统的机动检测算法将失效,多分辨方法虽然能够有效地抑制噪声,可靠检测机动,但由于计算复杂导致严重的检测延迟,从而限制了它的应用.本文提出一种基于三阶累积量的机动检测新算法,它有效地克服了上述二者的缺陷.由于高阶累积量能够抑制高斯噪声,因此在三阶累积量域易于检测机动.同时通过采用逐点更新法,可实时进行机动检测.仿真结果表明,该算法优于传统算法和多分辨方法,特别是在低信噪比的情况下.

A New Method of Maneuver Detection Based on Higher-Order Cumulants

Maneuver detection is a key issue in the multiple-model target tracking applications.In Kalman filter,the conventional maneuver detection algorithm fails to work well when the maneuver is almost buried in noise.The multiresolutional approach performs well for maneuver detection,but the time delay limits its application.In this paper,a new algorithm for maneuver detection is proposed,which is based on the third-order cumulant of the measurements.As the higher-order cumulants are blind to Gaussian noise (white or colored),the behavior of the maneuver becomes obvious and easy to be detected in the higher-order cumulant domains.Taking the sliding window processing,the maneuver detection algorithm is real-time without any time delay.Simulation results demonstrate the effectiveness of the algorithm over the conventional and multiresolutional one's,especially at low SNR.