基于RBF神经网络的被动声定位算法研究

Research on Passive Acoustic Localization Algorithm Base on RBF Neural Network

提出了基于RBF神经网络的被动声定位算法.该算法根据TDOA定位原理,以四元十字阵作为定位模型,利用RBF神经网络较快的学习特性和逼近任意非线性映像的能力,实现对声源的快速准确定位,并与WLS算法、Chan算法、Taylor算法作对比分析.仿真结果表明,该算法定位精度高,鲁棒性好,性能优于其他算法.

A passive acoustic localization algorithm based on RBF neural network was proposed.According to the principle of TDOA,four-cross array was chosen.And the fast study and non-linear approach capacity of the RBF neural network was made use of to realize rapid and precise localization.Furthermore,relative analysis has been done among RBF neural network,BP neural network,WLS algorithm,Chan algorithm,and Taylor algorithm.The performance of RBF neural network was simulated.The simulation results indicate that algorithm can improve the localization accuracy and robustness,and its performance is better than other algorithms.