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Target Localization in Multi-static Passive Radar Systems with Artificial Neural Networks

We propose an artificial neural network (ANN)-based target localization method for a multi-static passive radar system consisting of multiple illuminators, a radar receiver, and a single target. Assuming a multi-frequency network, the time difference-of-arrival (TDOA) of signals coming from each illuminator via the target and direct paths is estimated from the cross-ambiguity function. The training data consists of the estimated TDOAs corresponding to all illuminators and the target locations in the training set. The performance of the ANN is then evaluated with the test data, where 10% of the total data is randomly chosen as the test data. Simulation results show that the performance of the proposed ANN-based method decreases with the increasing estimation error in TDOA. Moreover, the results show that the mean square error (MSE) performance achieved with a set of 1, 000 data points is close to that achieved with the perfect case even the signal-to-noise-ratio in the surveillance channel is − 23 dB.