自适应容积卡尔曼滤波在空间机动目标跟踪中的应用

Application of Adaptive Cubature Kalman Filter in Spatial Maneuvering Target Tracking

针对目标在线机动时,平方根容积卡尔曼滤波不具有良好的鲁棒性,不能够快速发生响应的问题,提出一种自适应容积卡尔曼滤波(CKF)算法,算法利用CKF的平方根形式进行迭代,即SCKF。将强跟踪滤波算法引入平方根容积卡尔曼滤波,引入渐消因子对滤波发散情况进行检测和抑制,有效克服了空间目标发生机动时标准滤波器无法快速准确对其进行跟踪的问题,提高了空间目标定位跟踪的数值稳定性。仿真表明:与标准SCKF相比,自适应SCKF有效地提高了机动目标被动定位跟踪的鲁棒性,具有较高的滤波精度和稳定性,同时具有良好的实时性,能更好地完成对空间机动目标的跟踪任务。

Since the square root Cubature Kalman Filter is not robust and unable to give a quick response during target online maneuvering, an algorithm of adaptive Cubature Kalman Filter （CKF） is proposed, which uses the square root form of CKF for iteration, that is, SCKF. This algorithm introduces a strong tracking filtering algorithm into SCKF, and introduces a fading factor for detecting and restraining the filtering divergence, which effectively overcomes the problem that a standard filter cannot quickly and accurately track the spatial target during its maneuvering, and improves the numerical stability of spatial target location and tracking. Simulation result shows that： Compared with standard SCKF, the adaptive SCKF can effectively improve the robustness of maneuvering target passive locating and tracking, has higher filtering accuracy and stability, and good real-time performance, which can better accomplish the tracking task of spatial maneuvering target.