风场环境中声速修正的分布式声源定位算法

为减小声速误差对定位精度的影响,提出了一种基于声速修正的分布式声源定位方法。首先,将声速表示为未知声源位置的函数,逼近风场中的声速场分布,然后将其代入TDOA (Time Differences of Arrival)算法中,构建非线性超定方程组,最后采用粒子群优化算法求解声源位置。对不同风速、不同声源位置及不同测试区域进行仿真,结果表明:修正后的定位精度比修正前有明显提高,尤其对于大范围并且声源靠近测试区域边缘位置的定位系统,改善更加明显;4个节点的定位系统实验结果表明,修正后的定位误差可降至修正前的4l%,该方法能更好的应用于风场中的定位系统。 

Distributed sound source localization algorithm for sound velocity calibration in windy environment

In order to reduce the influence of sound speed error on method based on sound velocity calibration is proposed. Firstly, the the localization accuracy, a distributed localization acoustic velocity is derived as functions of unknown source location, to approximate the acoustic velocity field distribution in the wind field. Then, they are introduced into the TDOA (Time Differences of Arrival) algorithm, to construct nonlinear equations. Finally, the particle swarm optimization algorithm is used to get the source location. The simulated results show that the proposed algorithm can significantly improve the localization accuracy for different wind velocity, source location and sizes of test area. The experimental results show that the localization error of proposed method can be reduced to 41% of original in the four nodes' localization system.