Iterative Sidelobe Suppression for Range-Doppler Imaging via Progressive Model Expansion

In range- Doppler imaging radars, the sidelobes of strong scatterers may mask weak scatterers in the matched filter (MF) outputs. Adaptive pulse compression (APC) and iterative adaptive approach (IAA) pioneer a class of iterative filtering algorithms with remarkable sidelobe suppression performance. These algorithms formulate a multivariate linear model (MLM) by taking the complex scattering coefficients in all range- Doppler cells as its parameters, and solve for these parameters iteratively. As the MLM is potentially overparameterized, they incur significantly high computational costs. To address this issue, we propose a computationally efficient iterative filtering approach named iterative sidelobe suppression via progressive model expansion (PME-ISS) in this paper. It adaptively formulates a series of progressively expanding MLMs based on the estimated distribution of scatterer cells (DSC), which refers to range- Doppler cells occupied by scatterers, and estimates the complex scattering coefficients of these scatterers iteratively. To ensure the compactness of the MLMs, we propose a DSC estimation method composed of identifying potential scatterer cells and removing range- Doppler cells not occupied by scatterers. A specific implementation algorithm of PME-ISS is derived, and its computational cost analysis is provided. Simulations demonstrate a computational cost reduction of multiple orders of magnitude compared with existing iterative filtering algorithms without sacrificing sidelobe suppression performance.