In this paper, the inherent problem of the Hough transform when applied to search radars is considered. This problem makes the detection probability of a target depend on the length of the target line in the data space in addition to the received SNR from it. It is shown that this problem results in a non-uniform distribution of noise power in the parameter space. In other words, noise power in some regions of the parameter space is greater than in others. Therefore, the detection probability of the targets covered by these regions will decrease. Our solution is to modify the Hough detector to remove the problem. This modification uses non-uniform quantization in the parameter space based on the Maximum Entropy Quantization method. The details of implementing the modified Hough detector in a search radar are presented according to this quantization method. Then, it is shown that by using this method the detection performance of the target will not depend on its length in the data space. The performance of the modified Hough detector is also compared with the standard Hough detector by considering their probability of detection and probability of false alarm. This comparison shows the performance improvement of the modified detector.

The Hough transform is known to be an effective technique for target detection and track initiation in search radars. However, most papers have focused on the simplistic applications of this technique which consider a 2-D data space for the Hough transform. In this paper, a new method based on xthe Hough transform is introduced for detecting targets in a 3-D data space. The data space is constructed from returned surveillance radar signal using the range and bearing information of several successive scans. This information is mapped into a 3-D x-y-t Cartesian data space. Targets are modeled with four parameters in this data space. The proposed 3-D Hough detector is then used to detect the existent targets in the 3-D surveillance space by mapping the returned signal of the radar from the data space to the parameter space. This detector, which is constructed of two detection stages, integrates the returned data of each target non-coherently along its 3-D trajectory in one parameter space cell related to this target. Hence, the detection performance will improve. The effectiveness of the new 3-D Hough detector is demonstrated through deriving the detection statistics analytically and comparing the results with those of several comprehensive simulations. The performance improvement of this detector is shown by comparing its detection range with the conventional detector. The proposed detector is also evaluated with real radar data and its efficiency is confirmed.