Detection of the Weak Targets by Using Multi Dimensional Hough Transform Based Track Before Detect Algorithm

Turhan, Hasan Ihsan
Demirekler, Mübeccel
This study proposes a multi-dimensional Hough transform algorithm that is improved from [1] by detecting weak targets in the high clutter. In the proposed algorithm execution time is reduced by eliminating the measurements considering speed and SNR values before the Hough transform. The skor-based track confirmation algorithm proposed in [1] is improved and tracks that belong to same target are eliminated. The proposed algorithm is tested with real data and results are presented.


A Multi-Dimensional Hough Transform Algorithm Based on Unscented Transform as a Track-Before-Detect Method
Sahin, Gozde; Demirekler, Mübeccel (2014-07-10)
In this study, a new Multi-Dimensional Hough Transform technique is proposed for the detection of dim targets in radar data. Multi-Dimensional Hough Transform is a Track-Before-Detect method that fuses Hough Transform results obtained on (x-t), (y-t) and (x-y) domains. The proposed study models Hough Transform results in (x-t) and (y-t) domains by Gaussians and transforms these Gaussians to (x-y) domain using Unscented Transform. This improves the computational efficiency significantly without degrading per...
Improved Multi-Dimensional Hough Transform as a Track-Before-Detect Method
Sahin, Gozde; Demirekler, Mübeccel (2014-04-25)
This study proposes an improved Multi-Dimensional Hough Transform technique for the detection of low SNR targets (dim targets) in radar data. The proposed Track-Before-Detect technique improves the Multi-Dimensional Hough Transform by limiting the target's maximum velocity and incorporating the SNR values of the targets in the algorithm. In addition, the performance is enhanced by confirming the Hough Transform results with a score-based confirmation algorithm.
Efficient Bayesian track-before-detect
Tekinalp, Serhat; Alatan, Abdullah Aydın (2006-10-11)
This paper presents a novel Bayesian recursive track-before-detect (TBD) algorithm for detection and tracking of dim targets in optical image sequences. The algorithm eliminates the need for storing past observations by recursively incorporating new data acquired through sensor to the existing information. It calculates the likelihood ratio for optimal detection and estimates target state simultaneously. The technique does not require velocity-matched filtering and hence, it is capable of detecting any targ...
Okman, O. Erman; Akar, Gözde (2013-05-31)
In this paper a novel fast circle detection algorithm is proposed which depends on the spatial properties of the connected components on the image. Two 1-D transforms of each connected component is obtained by taking the Radon Transform of the image for two different directions, which are in fact the integrations of the image through horizontal and vertical directions. Circles are detected using the similarities of detected peaks on the transformed functions and the characteristics of the values in between ...
An interactive ranking-based multi-criteria choice algorithm with filtering: Applications to university selection
Karakaya, Gülşah (Orta Doğu Teknik Üniversitesi (Ankara, Turkey), 2019-6)
In this study, we develop an interactive algorithm to converge to the most preferred alternative of a decision maker (DM) among a set of discrete alternatives. The algorithm presents a limited number of alternatives to the DM and collects preference ranking of them iteratively. The preferences are modeled by a flexible and realistic preference function. To improve the performance, the alternatives presented are determined by a filtering method. We compare our algorithm with benchmark algorithms on nume...
Citation Formats
H. I. Turhan and M. Demirekler, “Detection of the Weak Targets by Using Multi Dimensional Hough Transform Based Track Before Detect Algorithm,” 2016, Accessed: 00, 2020. [Online]. Available: