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在主从式UUVs(Unmanned Underwater Vehicles)系统中,从UUV仅携带被动传感器测得杂波环境下的主UUV方位角信息,通过自主优化其运动轨迹以达到尽快实现与主UUV汇合的目的。分析对比不同的观测载体机动轨迹对仅方位角系统TMA(Target Motion Analysis)精度的影响后,充分考虑到本课题从UUV速度小于主UUV速度这一特殊情况,提出了一种适用于工程应用的基于动态距离最短的轨迹优化方法并且借鉴其它载体航路跟踪模式设计从UUV跟踪轨迹的整体过程,使用扩展卡尔曼滤波算法进行主UUV运动要素估计,采取边估计边优化的方法实时更新从UUV运动状态。仿真实验中与基于方位角变化率最大轨迹优化方法及基于最小化均方误差阵的迹的轨迹优化方法进行对比,结果表明该方法能够实现主从UUVs的汇合,验证了该方法的合理性。
In the UUV system, the UUV only carries the passive sensor to measure the azimuth information of the main UUV in the clutter environment, and achieves the goal of merging with the main UUV as soon as possible by optimizing the trajectory of the UUV. After analyzing and comparing the influence of different trajectories of observing carrier on the accuracy of TMA (Target Motion Analysis) system, considering the special situation that the velocity of UUV is less than that of the main UUV, we put forward a new method which is suitable for engineering application Based on the trajectory optimization method with the shortest dynamic distance and using other carrier route tracking model to design the whole process of tracking trajectory from UUV, the extended Kalman filter algorithm is used to estimate the main UUV motion elements. The edge estimation method is used to update the motion state . Compared with the trajectory optimization method based on the maximum azimuthal rate of change trajectory and the trajectory optimization method based on the minimum mean square error matrix, the simulation results show that this method can achieve the convergence of master-slave UUVs and verify the rationality of this method.