Physics-Based Waveform Representation for Geometry-Aware Object Recognition

This paper presents the RaDICAL sensing framework, a monostatic passive radar concept that combines a Sparse Uniform Circular Array (SUCA), deterministic multifrequency 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. This study develops the SUCA-based signal model for point and extended targets and formulates recognition as a waveform-domain matching problem against the representation dictionary using normalized complex correlation and QR-domain processing. A reproducible MATLAB-based study evaluates waveform separability, probability of detection versus dictionary SNR, physical power balance, receiver operating characteristic (ROC) behavior, and detection performance versus illuminator EIRP. The results show that deterministic dither produces distinctive composite waveforms with strong hypothesis separability. The ROC simulations characterize binary detection of the structured waveform, while the recognition simulations quantify discrimination among competing physical dictionary hypotheses. Because the proposed receiver coherently processes multiple spatial and temporal waveform samples, no direct single-sample SNR advantage over a classical matched-filter detector is claimed. These results support the feasibility of waveform-domain passive sensing using deterministic spatial–frequency encoding and dictionary-based recognition. Unlike conventional representation-learning methods that derive embeddings from image or feature datasets, the proposed waveform representations are generated deterministically from physical electromagnetic models.