Compressive sensing based high resolution radar stretch processing

Stretch Processing(SP) is a pulse compression technique that processes large bandwidth signals with narrowband receivers; using lower rate Analog to Digital Converters(ADC) while obtaining high-range resolution. The range resolution of the large bandwidth signal is obtained through looking into a limited range window, and by deramping in the analogue domain. Stretch processing offer moderate data rate for signal processing for high bandwidth waveforms. Besides frequency shift occurs in case of insufficient information about velocities of targets in the scene even though stretch processing provides high range resolution and this may cause ambiguity in the observed ranges. Therefore, it is proposed to use pulse-Doppler technique with Stretch processing in order to estimate velocity of targets in additional to range information by protecting the range resolution level. While stretch processing decreaces the sampling rate, it causes the increment of data collection period. Thus time-bandwidth product of demodulated chirp signal is almost fixed and stretch processing could not be considered as a compression method. Besides if the target space in the observed range window is sparse, Compressive sensing(CS) is an important tool to further decrease the number of measurements and sparsely reconstruct the target space for sparse scenes with a known basis which is the Fourier basis in the general application of both pulse Doppler and stretch processing. Although classical compressive sensed techniques might be directly applied to this problem, compressive sensing based reconstructions are highly effected by model mismatches such as targets that are offgrid. In order to overcome model mismatch problem, a sparsity based iterative parameter perturbation technique for stretch processing that is robust to targets off-the-grid in range or Doppler is proposed. Earth Mover's Distance(EMD) metric is used to measure the error between actual and reconstructed scenes. Performance analyses of proposed technique is compared with classical stretch processing and compressive sensing techniques in terms of data rate, sparsity levels, signal-noise ratio(SNR), and signal-clutter ratio(SCR). It is shown through simulation scenes consisting of off-grid targets that the proposed technique offer robust and high resolution reconstructions for the same data rate compared to classical stretch processing and compressive sensing techniques.