Knowledge-Based Adaptive Stagger-Block-Lattice-Filter MTI Processor

This paper researches adaptive stagger-block-lattice-filter (SBLF) processor, which can be applied to an MTI radar. Our years’ experiments prove the necessity to incorporate AI into the processor. Only algorithmic operation largely restricts clutter suppression performance in complex environments. The high speed and small radar-cross-section of a target compel an MTI to be upgraded by stagger pulse repetition interval (PRI), pulse compression (PC) transmission, and intelligent operation. Five heuristic strategies for the adaptive SBLF processor are proposed: 1) non-clutter block decision and threshold set-up, which decides whether the Test block to be dominated by a clutter and uses the filtering-itself output to set up detection thresholds; 2) Target block identification and PC terrace-indication declaration, which identifies the Test block to be dominated by a target or clutter and determines the terrace-indication; 3) SBLF coefficient estimation, which calculates SBLF coefficients in the Guard I block in real-time; 4) Establishment of clutter-map, which stores the thresholds, target block index, terrace-indication index, SBLF coefficients, and notch parameters in the corresponding map cells; 5) Guidance of stagger PRI, which collects target block indices on adjacent bearing bins and notch parameters to maximize target acquisition and to suggest the available PRI. In order to verify effectiveness of the above intelligent operations, we have done a lot experiments with computer and selected a ship-borne radar as the background; the clutter returns feature the land, sea and weather environments, and the target returns feature weak high-speed aircrafts. In homogeneous and heterogeneous clutters, this processor demonstrates the individual ability to acquire the weak targets in the strong clutter.