RaDICAL: A Monostatic Passive Radar Framework for Geometry-Aware Detection and Image Recognition

This paper presents the RaDICAL sensing framework, a monostatic passive radar concept that combines a Sparse Uniform Circular Array (SUCA), deterministic multi-frequency dither, and dictionary-based waveform recognition for target detection and classification. Rather than forming conventional spatial images or relying primarily on Doppler processing, RaDICAL encodes target geometry directly into a composite receiver waveform and performs hypothesis testing by matching measured signals to a library of predicted responses. The paper develops the SUCA-based signal model for point and extended targets and formulates recognition as a waveform-domain dictio-nary matching problem using normalized complex correlation and QR-domain process-ing. A reproducible MATLAB-based study evaluates waveform separability, probability of detection versus dictionary SNR, physical power balance, receiver operating char-acteristic (ROC) behavior, and detection performance versus illuminator EIRP. The results show that deterministic frequency dither produces distinctive composite wave-forms with strong hypothesis separability. ROC simulations demonstrate reliable detec-tion at physical SNR levels below those typical of classical single-pulse matched-filter detection, while EIRP-based analysis indicates feasible detection for targets ranging from large aircraft to small drones and pedestrians. These results support the feasibil-ity of waveform-domain passive sensing using deterministic spatial–frequency encoding and dictionary-based recognition.