Passive and bistatic radar systems are often limited by strong clutter and direct-path interference that mask weak moving targets. Conventional cancellation methods such as the extensive cancellation algorithm require careful tuning and can distort the delay-Doppler response. This paper introduces a convex optimization framework that adaptively synthesizes per-cell delay-Doppler filters to suppress clutter while preserving the canonical cross-ambiguity function (CAF). The approach formulates a quadratic program that minimizes distortion of the CAF surface subject to linear clutter-suppression constraints, eliminating the need for a separate cancellation stage. Monte Carlo simulations using common communication waveforms demonstrate strong clutter suppression, accurate CFAR calibration, and major detection-rate gains over the classical CAF. The results highlight a scalable, CAF-faithful method for adaptive clutter mitigation in passive radar.
@article{arxiv.2512.24889,
title = {Adaptive Clutter Suppression via Convex Optimization},
author = {Yifan He and Griffin Kearney and Makan Fardad},
journal= {arXiv preprint arXiv:2512.24889},
year = {2026}
}