基于外定界椭球集员估计的纯方位目标跟踪

针对在未知但有界噪声假设下的双基阵纯方位目标跟踪问题,本文提出了一种基于外定界椭球的集员估计(EOB-SME)跟踪算法。该算法具有类似于Kalman滤波的预测-校正递推更新结构,并且在时间更新和量测更新递推阶段分别有一个加权参数。通过最小化估计误差的Lyapunov函数的上界来求取量测更新递推阶段的加权参数,减少了算法的计算量;同时将非线性系统线性化后所产生的误差用椭球进行外包,与量测噪声椭球组成新的噪声椭球。仿真结果表明:在有界噪声假设下,本文所提出算法对纯方位机动目标的跟踪精度更高。

Bearing-only target tracking based on ellipsoidal outer-bounding set-membership estimation

For the problem of bearing-only maneuvering target tracking under the unknown-but-bounded noises,this paper proposes an ellipsoidal outer-bounding set-membership estimation (EOB-SME) algorithm.The EOB-SME algorithm has a prediction-correction structure in time update and observation update,which is similar to Kalman filter.For each update,there is a data-depending weighting factor.The observation weighting factor is computed by minimizing the upper bound on a Lyapunov function of the estimation error and the computation load is decreased.The linearization errors of nonlinear observation equation are bounded by an ellipsoid which is combined with the ellipsoid of observation noise to form a new ellipsoid of observation noise.The simulation results show that the proposed algorithm has higher tracking accuracy for bearing-only maneuvering target under the assumption of unknown-but-bounded noise.