Abstract— This paper presents a complete algorithmic framework for the automatic detection, parameter estimation, and recognition of Linear Frequency Modulated (LFM) synthetic aperture radar (SAR) signals within a compact radiomonitoring module. The proposed method integrates a parallel frequency-based search, an autocorrelation-based detection stage, time–frequency parameter extraction, and a Bayesian decision scheme for signal classification. A key feature of the approach is its implementation within an autocorrelation-type receiver, which enables precise estimation of LFM parameters at low signal-to-noise ratios while maintaining the small size, low weight, and low power consumption required for deployment on both stationary and mobile platforms. The simulation results indicate that the parameter estimation error does not exceed 8%, and the classification probability approaches unity for representative SAR signal types at signal-to-noise ratios above 0 dB. These results confirm the effectiveness of the proposed algorithm for compact radiomonitoring applications.