Adaptive Clutter Suppression via Convex Optimization

TL;DR

This paper presents a convex optimization-based method for adaptive clutter suppression in passive radar, effectively reducing clutter and improving target detection without distorting the core signal features.

Contribution

It introduces a novel convex optimization framework that adaptively synthesizes delay-Doppler filters, eliminating the need for separate cancellation stages and maintaining the canonical cross-ambiguity function.

Findings

Strong clutter suppression demonstrated in simulations

Accurate CFAR calibration achieved

Significant detection-rate improvements over classical methods

Abstract

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.