Iterative enhancement of noncoherent radar data

In this paper, the application of a constrained iterative deconvolution algorithm to the problem of enhancing noncoherent radar data is described. It is shown that noncoherent reconstruction is theoretically not unique. Despite this, an approximate convolutional model of the observed data is established and it is seen that reasonable results are observed. A key feature of the algorithm is the use of a median filter and threshold to segment the data into two classes, potential point targets and background, which are deconvolved separately subject to different nonlinear constraints. The performance of the algorithm is demonstrated using real air-to-ground millimeter-wave noncoherent radar data.