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Multi-dimensional hough transform based on unscented transform as a method of track-before-detect /

Şahin, Gözde
Track-Before-Detect (TBD) is the problem where target state estimation and detection occur simultaneously, and is a suitable method for the detection of low-SNR targets in unthresholded sensor data. In this thesis, a new Multi-Dimensional Hough Transform (MHT) technique based on Unscented Transform is proposed for the detection of dim targets in radar data. MHT is a TBD method that fuses Hough Transform results obtained on (x-t), (y-t) and (x-y) domains in order to detect a constant velocity target. The proposed study models Hough Transform results in (x-t) and (y-t) domains by Gaussians and transforms these Gaussians to (x-y) domain by using Unscented Transform for this purpose. Moreover, the algorithm is modified to make use of the intensity values of the radar data and the prior knowledge of target’s maximum speed. Lastly, a score-based track confirmation algorithm is proposed to increase the performance under heavy clutter and eliminate possible false trajectories. The study examines the performance of the proposed algorithm using target existence and estimation accuracy. The algorithm’s susceptibility to clutter and varying SNR levels is also tested in the simulations.