基于修正观测值的水下移动节点定位算法

A localization algorithm based on modified observations for underwater mobile nodes

针对由声线弯曲和节点移动性造成的水声定位系统定位误差偏大的问题,提出了基于修正观测值的粒子滤波算法(MOPF)。首先,根据声速剖面,使用迭代逼近的方式初步修正观测距离,补偿由声线弯曲造成的测距误差。然后,利用移动节点在信标节点方向上的分速度与定位信号到达时间差的乘积,进一步修正观测距离。最后,使用二次修正的观测值更新粒子滤波产生随机的粒子权重,得出最佳位置估计。仿真实验表明,MOPF算法平均定位误差为0.72 m,定位精度较扩展卡尔曼滤波算法提升了50%。MOPF算法解决了节点运动特性及所处方位对定位精度造成的影响,提高了定位系统的稳定性。

In the underwater acoustic positioning system,the bending of the acoustic line and the mobility of the nodes are the main factors that cause the large positioning error.To solve this problem,a modified observations particle filter(MOPF)is proposed.First,according to the sound velocity profile,the observation distance is modified by iterative approach to compensate the ranging error caused by the bending of the sound line.Then,the observation distance is further modified by the product of the velocity of the mobile node in the direction of the beacon node and the arrival time difference of the positioning signal.Finally,the weight of the random particles generated by the particle filter is updated by the twice modified observation value,and the best position estimation is obtained.The simulation results show that the average positioning error of MOPF algorithm is 0.72 m,and the positioning accuracy is improved by 50%compared with the extended Kalman filter algorithm.The MOPF algorithm solves the influence of node motion characteristics and position on positioning accuracy,and improves the stability of positioning system.