Abstract: A combination of processing techniques including ML-based techniques are used to process radar data to improve detection of targets from the radar data, especially for detecting valid targets in noisy environments and isolating valid targets from noise, clutter, and interference. By processing an input RD map using an ML model and then by performing denormalization on the output of the ML model, the values associated with the pixels in the input RD map are stretched such that the distance between the noise floor and valid target peaks in the input RD map is increased. This stretching accentuates the contrast between the noise and target signals in the RD map data, improving the detectability of weak targets in challenging environments by making weak and distant targets more distinguishable during the target detection phase.

Abstract: A method of processing radar data includes detecting a received signal including one or more targets signals and interference, converting the received signal to a matrix signal, and decomposing the matrix signal into a low-rank matrix and a sparse matrix using a group sparsity based low-rank and sparse decomposition method. The method also includes computing an inverse transform of the low-rank matrix and outputting the one or more target signals.