A Gradient-based Method with Denoising for Target Shadow Profile Retrieval in Forward Scatter Radar

The existing nonlinear least-squares method for target shadow profile retrieval in forward scatter radar only considers noiseless samples of the forward scatter shadow ratio. Inspired by optical diffraction tomography, in this paper we propose to use a gradient-based method with denoising to tackle scenarios involving noisy samples. Simulation results demonstrate its superiority over the conventional least-squares method. More specifically, an essential prerequisite for the least-squares method, i.e., the number of samples being greater than or equal to the number of pixels, is no longer required by the proposed method. Moreover, the proposed method retrieves the target image with a higher peak signal-to-noise ratio than that using the least-squares method.