A Neural Network Based Approach for ESM/Radar Track

In this paper, a neural network based ESM/radar track association algorithm is presented. The algorithm consists of a feed-forward neural network and a probability combiner. The neural network classifier is trained using the radar bearing measurements as well as their time stamps to approximate the a posteriori probabilities. The ESM bearing measurements along with their time stamps are fed to the trained network to provide a sequence of a posteriori probabilities. The probability combiner combines the local a posteriori probabilities to provide the global a posteriori probabilities that an ESM track belongs to each individual radar track. The track association logic associates the ESM track with the radar track that has the maximum a posteriori probability. The approach is able to eliminate the complex track time alignment process that is required by other techniques. It also alleviates the requirement for the Gaussian assumption about the measurements. Computer simulations are used to demonstrate the performance and effectiveness of the proposed algorithm