Cognitive FDA-MIMO Radar Network’s Transmit Element Selection Algorithm for Target Tracking in a Complex Interference Scenario

In the future, radar will encounter a more intricate and ever-changing electromagnetic interference environment. Consequently, one crucial trajectory for radar system evolution is the incorporation of network and cognition capabilities to meet these emerging challenges. The traditional frequency diversity array multiple-input multiple-output (FDA-MIMO) radar is rendered ineffective due to occurrences of frequency spectrum interference and main-lobe deceptive interference with arbitrary time delays. Therefore, a cognitive FDA-MIMO radar network (CFDA-MIMORN) transmit element selection algorithm is introduced. At first, the target is discriminated from the false targets. The Kalman filter is used to track the target, then available information is used to infer the target’s position in the next time step. The finite transmit elements of the radar network are organized to enhance tracking performance, especially in the presence of frequency spectrum interferences. The numerical simulations demonstrate that the proposed CFDA-MIMORN can effectively discriminate the true target from false targets, and optimize the allocation of transmit elements to avoid interferences, resulting in improved tracking accuracy.