关键词: 水下机动目标跟踪, 无迹卡尔曼滤波, 自适应滤波, 航速, 航向

Abstract: In order to meet the needs for tracking the underwater maneuvering target in real-time and predicting its location in the underwater confrontation environment, the higher requirements are put forward for accurate and fast estimation of target’s sailing speed and course. The traditional unscented Kalman filter(UKF) tracking algorithm is improved for maneuvering target tracking with range and azimuth. The improved tracking algorithm can also be used to estimate the system status noise online in real-time without determining the state equation and the state noise variance in advance, thus tracking the maneuvering target adaptively. A novel adaptive UKF method is proposed，which adaptively adjusts the target state noise with residual probability distribution according to the residuals of predicted and observed values of UKF algorithm, so that the UKF tracking algorithm can adjust the state according to the target state the equation reduces the reliance on the predicted value when the target is maneuvering and increases the reliance on the prediction when the target is maneuvering. Primary numerical simulation results show that the algorithm not only has good tracking performance in target maneuvering but also has accurate estimation in non-maneuvering. At last, the tracking performance of state adaptive UKF algorithm is illustrated by sonar simulation system. Key

Key words: underwatermaneuveringtargettracking, unscentedKalmanfilter, adaptivefilter, speed, course