Employing frequency and antenna spatial diversity for improved, non-emi limited multi-radar target detection and tracking

Real-time fusion of data collected from a variety of radars that acquire information from multiple perspectives and/or different frequencies, is being shown to provide a more accurate picture of the adversary threat cloud than any single radar or group of radars operating independently. This paper describes a cooperative multi-sensor approach in which multiple radars operate together in a non-interference limited manner, and where intelligent decision algorithms are applied to optimize the acquisition, tracking, and discrimination of moving targets with low false alarm rate. The approach is three-fold: (i) apply multiobjective joint optimization algorithms to set limits on the operational parameters of the radars to preclude electromagnetic interference (EMI); (ii) measure and process radar returns in a shared manner for target feature extraction based on waveform diversity techniques; and (iii) employ feature-aided track/fusion algorithms to detect, discriminate, and track real targets from the adversary noise cloud. The results of computer simulations are provided that demonstrate the advantages of this approach.