A Three-Dimensional Hough Transform-Based Track-Before-Detect Technique for Detecting Extended Targets in Strong Clutter Backgrounds

Abstract

Hough Transform (HT), which has a low sensitivity to local faults and good ability in suppressing noise and clutters, usually applies to trajectory detection in a cluttered environment. This paper describes its application for detecting the trajectories of extended targets in three-dimensional measurements, i.e., a two-dimensional positional information and its measuring time. For taking the full merits of a multi-scan, the measuring time is regarded as a variable for the time axis. This correspondence extends the HT to 3-dimensional data. Meanwhile, a three-dimensional accumulator matrix is built for the purpose of voting. The voting process is done in an iterative way by selecting the 3D-line with the most votes and removing the corresponding measurements in each step. The three dimensional Hough Transform-based extended target track-before-detect technique (3DHT-ET-TBD), proposed here, is suitable to track the extended target and non-extended target simultaneously and few false alarm trajectories arise. Both the real data and simulated data are exploited to evaluate its performance. Compared with the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter based methods and a 4DHT-TBD algorithm, the 3DHT-ET-TBD is a more promising approach for multi-extended target tracking problems due to its high efficiency and low computation, especially in situations where the noise and false alarms are considerably high but few measurements are generated by the extended targets.

A Grey Wolf Optimization-based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking

“…The extended target state ξ k at k -th scan is defined as the triplet ξ k = ( γ , x k , X k ) in [10]. Firstly, the random variable γ > 0 is the measurement rate that describes how many measurements the target, on average, generates per time scan.…”

“…Various algorithms have been developed for extended target detection and tracking. The algorithms are mainly fall into two categories: extended target probability hypothesis density (ET-PHD) filters [1,2,3,4,5] and track-before-detect algorithms [6,7,8,9,10].…”

“…For the presence of clutter measurements, extended target which only generates a few measurements is hard to be detected. Track-before-detect algorithms [6,7,8,9,10] are superior in detecting and tracking weak targets for taking full merits of multiscan. Track-before-detect algorithms mainly have three implementations: particle filter based track-before-detect (PF-TBD) [6], dynamic programming based track-before-detect (DP-TBD) algorithms [7,8] and Hough transformation based track-before-detect (HT-TBD) [9,10].…”

“…Enumeration of hypotheses is impractical for the real-time application as the number of hypotheses exponentially increases with a linear increases in the depth of the hypotheses. Above discussion implies that existing methods [1,2,3,4,5,6,7,8,9,10,11,12] are insufficient to detect and track the maneuvering weak extended targets.…”

A Novel Smooth Variable Structure Smoother for Robust Estimation

“…Unlike using the SVSF gain, the proposed SVSS method is capable of getting a better state estimation by using the innovation sequence and projection theory [8]. Our former work focus on maneuvering target tracking [50][51][52][53], the SVSS is also applied in maneuvering target tracking. The rest of the paper is structured as follows.…”