Monostatic Waveform-Domain Passive Radar for Detection and Localization Using a Sparse Circular Array with Deterministic Frequency Dither

This paper presents the RaDICAL monostatic passive radar framework for target detection and localization using a sparse uniform circular array (SUCA), multifrequency dither, and dictionary-based waveform processing. Rather than forming conventional spatial images or relying on explicit Doppler/TDOA/FDOA estimation, the proposed method encodes target geometry directly into a composite receiver waveform and performs localization through hypothesis testing using a library of predicted waveform responses. A SUCA-based signal model is developed for both point and extended targets, and detection/localization formulated as a waveform-domain dictionary matching problem using normalized complex correlation and QR-domain processing. A reproducible MATLAB-based Monte Carlo study evaluates waveform separability, probability of detection versus input SNR, receiver operating characteristic (ROC) behavior, localization performance, and receiver power balance. The results demonstrate that multifrequency dither produces distinctive composite waveforms with strong hypothesis separability and stable waveform domain recognition performance. ROC analysis and detection simulations showed reliable target detection at input SNR levels on the order of −10 to 0 dB, consistent with the coherent processing gain achieved through waveform-domain correlation processing. The corresponding power-balance analysis indicates that reliable detection and localization are feasible using modest illuminator EIRP and compact receiver dimensions. These results support the feasibility of compact reference-free waveform domain passive sensing for joint target detection and localization.