Bearing estimation with time-delay neural networks

In this paper we present a neuromimetic approach to bearing estimation issue. The proposed method is based on time-delay neural networks. This kind of network is well suited to take into account constraints encountered in signal processing: it deals with the dynamic nature of signal and discovers acoustic and temporal features. According to the propagation model of plane waves, the network has to relate the delays between sensors to enable source localisation. The time-delay neural network approach is encompassed in a successive-reenement method. Thus, accuracy is increased while the number of networks to look at the whole horizon is reduced.