Real-time PRF Selection of an Airborne MPRF Radar for Target Detection and Tracking Under Sea Clutter Environments

This paper proposes a novel pulse repetition frequency (PRF) selection algorithm for airborne radars that utilizes prior knowledge of the target trajectory to link the detection and tracking stages. Conventional methods suffer from limited detection performance and heavy computational loads, since they formulate the two modes independently as a combinatorial optimization over the entire range–Doppler plane. To this end, a range–Doppler region of interest (ROI) is defined from the predicted trajectory, which restricts the search domain while serving as a common basis linking the two modes. In detection mode, restricting the visibility evaluation to the ROI shrinks the solution space, and the resulting computational margin is allocated to a wider beam search within a greedy algorithm. This deterministically yields a sub-optimal PRF set that remains valid throughout the tracking phase, thereby mitigating the PRF exhaustion inherent in conventional approaches. In tracking mode, the separation between the target signal and the blind region is defined as a single clutter avoidance index, reducing the selection to a scalar discrete optimization problem solvable in real time. Simulation results for typical airborne radar engagement scenarios confirmed that the proposed algorithm provided superior target detection performance and computational efficiency compared to conventional methods.