Improved radar object identification using wavelet transform and ART-2 neural network

Object identification plays a crucial role in military units and therefore accuracy and reliability is of utmost importance. In this paper, we apply the concepts of wavelet transform and ART-2 neural networks to propose a new system for radar object identification. We also propose an algorithm for constructing the feature vector, which serves as the input to the ART-2 neural network. The ART-2 neural network categorizes and thereby identifies the type of object based on the feature vector. Wavelet transform is the best tool to perform feature extraction because of its unique capability to distinguish noise from detectable signal and also the smaller resultant feature vector size. Even in the presence of low signal to noise ratio (SNR), the proposed system works with good accuracy rate.