Decision-Level Fusion Performance Improvement From Enhanced HRR Radar Clutter Suppression

Abstract

Airborne radar tracking in moving ground vehicle scenarios is impacted by sensor, target, and environmental dynamics. Moving targets can be characterized by 1-D High Range Resolution (HRR) Radar profiles with sufficient Signal-to-Noise Ratio (SNR). The amplitude feature information for each range bin of the HRR profile is used to discern one target from another to help maintain track or to identify a vehicle. Typical radar clutter suppression algorithms developed for processing moving ground target data not only remove the surrounding clutter, but a portion of the target signature. Enhanced clutter suppression can be achieved using a Multi-channel Signal Subspace (MSS) algorithm, which preserves target features. In this paper, we (1) exploit extra feature information from enhanced clutter suppression for Automatic Target Recognition (ATR), (2) present a Decision-Level Fusion (DLF) gain comparison using Displaced Phase Center Antenna (DPCA) and MSS clutter suppressed HRR data; and (3) develop a confusion-matrix identity fusion result for Simultaneous Tracking and Identification (STID). The results show that more channels for MSS increase identification over DPCA, result in a slightly noisier clutter suppressed image, and preserve more target features after clutter cancellation. The paper contributions include extending a two-channel MSS clutter cancellation technique to three channels, verifying the MSS is superior to the DPCA technique for target identification, and a comparison of these techniques in a novel multi-look confusion matrix decision-level fusion process.