Target Tracking in WSN using Time Delay Neural Network

Abstract

Energy efficient tracking is a challenging application of resource constrained wireless sensor network. Prediction based schemes play a vital role in energy saving by reducing an avoidable communication. Efficient tracking can be achieved only if state transition matrix used in filter closely resembles the target movement. Kalman filter has been widely used as prediction algorithm but fails in case of maneuvering target because of state transition matrix mismatch. To make tracking algorithm model free, Time delay neural network based prediction algorithm is proposed in this paper. Performance of Time Delay neural network (TDNN) is compared with Kalman filter and Interacting multiple model filter in terms of mean square error. Results shows that TDNN outperforms both the filters.