Enhanced Small-Target Detection and Tracking Using a Mode-Adaptive Constant False Alarm Rate (CFAR) Detector

The propose effort will determine the feasibility of and continue development of innovative feature-based adaptive constant false alarm rate (FA-CFAR) techniques to detect slow moving and low radar cross section (RCS) maritime targets in challenging clutter environments. A first order approach employed by conventional techniques uses a whitener to notch out the clutter entirely and thus fails to detect endo-clutter targets moving too slowly relative to the radar sensor. Without a good model for sea clutter, CFAR estimates are unreliable in the maritime environment and as a result, saturation can occur. Our work has shown that sea clutter can be approximated by a piecewise linear compound Weibull distribution that independently accounts for Bragg scattering and sea spikes. Our analysis indicates that reliable estimation of the CFAR threshold based on the compound Weibull distribution shows up to 25 percent improvement in probability of detection (Pd) at a constant probability of false alarms (Pfa).