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

Moyer, Lee R.; Spak, Jeffrey; Lamanna, P.

doi:10.1109/taes.2011.6034689

Cited by 117 publications

(51 citation statements)

References 12 publications

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“…But the drawback is that it can only track target with determinate trajectory. The multi-dimensional Hough transform technique presented in [162] provides a computationally-efficient approach to address random clutter backgrounds. Also, when the clutter is dense or the target movement between two frames is significant, the detection performance deteriorates.…”

Section: ) Projective Transformsmentioning

confidence: 99%

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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Abstract-Multitarget tracking (MTT) in surveillance system is extremely challenging, due to uncertain data association, maneuverable target motion, dense clutter disturbance, and real-time processing requirements. A good many methods have been proposed to cope with these challenges. However, no up-to-date survey is available in the literature that can help to select suitable tracking algorithm for practical problem. This paper provides a comprehensive review of the state-of-the-art motion-based MTT techniques, classifies existing methods into two groups, i.e., the detect-beforetrack (DBT) scheme and the track-before-detect (TBD) scheme. The DBT scheme is employed to achieve robust and tractable tracking performance when the signal-noise-ratio (SNR) is strong. The TBD scheme is used in the scenarios of low SNR, and it aims to cumulate target energy by multiple sensor frames. Furthermore, depending on the data association mechanism, the DBT methods can be classified into two categories, data association based approaches and finite set statistics (FISST) based approaches. And the TBD methods can be classified into non-Bayesian approaches and Bayesian approaches depending on the basis theory used for tracking. For each category, this paper provides the detailed descriptions of the representative algorithms, and examines their pros and cons. Finally, new trends for future research directions are offered.

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Section: ) Projective Transformsmentioning

confidence: 99%

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

View full text Add to dashboard Cite

show abstract

“…Moyer et al's [7] multi-dimensional reduction method allows us to focus on processing 2D data X = {Xd�l' where each Xi = [ti, mil T comprises of a sensor measurement mi (e.g., range, bearing), and the time ti at which the measurement was recorded (here, we always take ti as time to remind the reader that the points may not lie in general position. In practice, ti can be another measurement, and this does not invalidate our method below).…”

Section: Tbd With Plane Sweepmentioning

confidence: 99%

“…To conduct multi-dimensional TBD, Moyer et al [7] pro posed to take two dimensions of the multi-dimensional data at a time, and apply standard HT to detect lines in each 2D sub problem. A gating process aggregates the multiple 2D line detections to yield an overall result.…”

Section: Introductionmentioning

confidence: 99%

Plane sweep method for optimal line fitting in track-before-detect

Guo

White

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The Hough transfonn line detection method has been estab lished as a viable technique for track-before-detect (TBD). However, its basic operation of binning and accumulating votes in the parameter space is computationally expensive. A more critical weakness of Hough transform is its dependence on parameter tuning (e.g., bin size and various thresholds), which can be non-intuitive and data-dependent. This leads to low detection rates in data with low signal-to-noise ratio and significant clutter. In this paper we propose a line detection algorithm with guaranteed global optimality for TBD. Our algorithm is based on the plane sweep algorithm for robust linear regression, with novel modifications to ensure its appli cability under the TBD setting. Unlike the Hough transform, our algorithm has only one parameter to set (essentially the sensor false alarm rate) and can deterministically find the best solution according to a well-defined criterion. Simula tion results on multi-dimensional TBD problems validate the accuracy and efficiency of our method.

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“…Unlike traditional tracking algorithms, track-before-detect (TBD) [2], [3], [4] algorithms remove the threshold and supply the tracker with all of the radar data. By processing several consecutive scans jointly, TBD algorithms can be used to track targets at low SNR.…”

Section: Introductionmentioning

confidence: 99%

Tracking targets in K clutter via particle filter

Jiang

Cui

et al. 2015

2015 IEEE Radar Conference (RadarCon)

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In this paper the radar detection and tracking of targets in sea clutter is examined through the use of particle filter (PF). The clutter is modeled by the K-distribution, which is usually used to model radar sea clutter returns received from small grazing angle or high resolution radars. Two target models are considered, i.e. non-fluctuating target model and Swerling type 1 target model, to capture the effect of radar crosssection changes that a target would present to the radar over time. For the above two target models, the probability density functions of clutter-only and clutter-plus-target case are given, and then the likelihood ratios (LLRs) are derived and used in the filtering process of PF. The performance is examined using various simulations.

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

Cited by 117 publications

References 12 publications

A Survey of Motion-Based Multitarget Tracking Methods

A Survey of Motion-Based Multitarget Tracking Methods

Plane sweep method for optimal line fitting in track-before-detect

Tracking targets in K clutter via particle filter

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