System and method for fft-based radar range-doppler map learning

Abstract

Described is a system for FFT-based radar range-doppler map learning. The system includes a physics enhanced dynamic reservoir core operable for receiving as an input signal a time delayed copy of a transmitted complex in-phase and quadrature (I/Q) radar waveform and generating an FFT transformed input signal. The FFT transformed input signal is mapped onto a neural network to determine complex-valued output weights of neural states of the neural network. An online learning layer is included that adapts the complex-valued output weights of the neural states to predict a most likely next FFT value of the FFT transformed input signal. A neural combiner is used to combine a set of delayed neural state vectors with the complex-valued output weights of the online learning layer to compute a denoised and decluttered output signal.