中文摘要：

为了进一步提高自主水下机器人（AUV）纯方位目标跟踪能力,从AUV轨迹优化方面进行了研究.采用基于距离的分段轨迹优化方法：在跟踪目标的初始阶段以定位的位置误差GDOP（geometrical dilution of precision）作为优化对象,以期在定位跟踪的各个时刻能得到最优的定位精度;针对目标运动要素（位置、速度、航向等）估计趋于收敛的情况,提出了一种基于短期预测的轨迹优化方法,AUV根据物理条件限制预测双方短期状态,计算能够反映跟踪态势特征的收益函数,根据收益函数对自身某状态进行评估,估算出自身各个预测状态的综合收益后,选出综合收益最大的那个状态作为短期目标,执行能到达该状态的行为.目标运动要素估计中使用扩展卡尔曼滤波（EKF）.最后,将该轨迹优化方法与基于GDOP的轨迹优化进行仿真对比,结果表明该方法能够实现AUV与目标较快汇合.

英文摘要：

In order to enhance the AUV （autonomous underwater vehicle） capability in bearings-only target tracking, the AUV trajectory optimization needs to be studied. A piecewise trajectory optimization method based on distance is proposed. In the initial phase of target tracking, GDOP （geometrical dilution of precision） matrix of positioning errors is taken as the objective function in optimization, in order to achieve optimal positioning precision at each time. Then, an AUV trajectory optimization method based on short-term prediction is proposed for the cases that the estimation of target navigation parameters （position, velocity and heading） converges. AUV predicts its own and the target＇s possible future states according to physical limits, and calculates its every state income according to the characteristics of the tracking trend. Based on the income, one of its own states is evaluated, and the consolidated income of every prediction state is estimated. At last, a proper state with the maximum consolidated income is chosen as its shot-term target, and the action leading to the target is executed. The extended Kalman filter algorithm is used to estimate the navigation parameters of the target. Finally, the proposed method and the GDOP based trajectory optimization method are compared through simulation, and the result shows that the AUV using the proposed method can capture the target as soon as possible.