Tracking Small Targets in Heavy-Tailed Clutter Using Amplitude Information

Brekke, Edmund; Hallingstad, Oddvar; Glattetre, John

doi:10.1109/joe.2010.2044670

Cited by 57 publications

(34 citation statements)

References 24 publications

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“…The measured amplitude PDF of K clutter is Rayleigh distribution whose mean square value is gamma distributed [10], i.e.,…”

Section: A Clutter Modelmentioning

confidence: 99%

“…However, for small grazing angle or high resolution radars, the amplitude of the envelope of the sea clutter returns can not be modelled by Gaussian/Rayleigh distribution any longer and in this case, the radar sea clutter is better to be modeled by the K-distribution [10].…”

Section: Introductionmentioning

confidence: 99%

“…, M} respectively. The approximation (20) is computationally expensive since the number of Bessel functions that (20) need to evaluate equals to N × M .Another way to obtain the PDF of clutterplus-target case should be considered using the compound interpretation of K distribution[10] …”

mentioning

confidence: 99%

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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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“…The measured amplitude PDF of K clutter is Rayleigh distribution whose mean square value is gamma distributed [10], i.e.,…”

Section: A Clutter Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tracking targets in K clutter via particle filter

Jiang

Cui

et al. 2015

2015 IEEE Radar Conference (RadarCon)

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“…Replace (19) of conventional recursion by (29), we obtain the new updated equation incorporated with amplitude information. Notice that the prediction equation of amplitude-aided CPHD filter is the same as standard CPHD filter.…”

Section: Amplitude-aided Likelihood and Detection Probability Estimationmentioning

confidence: 99%

“…For examples, Lerro and Bar-Shalom [18] proposed to use amplitude likelihood for probabilistic data association (PDA) and assign the measurements more similar to the target amplitude with larger association probability, so it can track single target more robust in dense clutter. Brekke et al [19,20] proposed a new conservative amplitude likelihood for the PDA filter with improved robustness, which attempted to detect and track moderately observable targets such as small boats or human divers in environments. Van Keuk [21] used amplitude information in MHT to improve the performance of MTT.…”

Section: Introductionmentioning

confidence: 99%

Amplitude-Aided CPHD Filter for Multitarget Tracking in Infrared Images

Qiu¹,

Zhang²,

Lü³

et al. 2014

PIER B

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Abstract-The cardinalized probability hypothesis density (CPHD) filter is a powerful tool for multitarget tracking (MTT). However, conventional CPHD filter discriminates targets from clutter only via the motion information, which is not reasonable in the situation of dense clutter. In the tracking, the amplitude of target returns is usually stronger than those coming from clutter, so the amplitude information can be used to enhance the discrimination between targets and clutter. Based on this idea, this paper proposes an amplitude-aided CPHD filter for the MTT in distant infrared (IR) surveillance. First, we model the amplitude of targets and clutter in IR scenarios respectively. For distant IR scenarios, the point spread function (PSF) is used to model the imaging of the point target. The center intensity of the PSF is unknown in practice, and the maximum likelihood estimation (MLE) method is adopted to estimate the target center intensity via the intensities of the latest target detections. Then a likelihood function for MTT is established, and using this likelihood function, a new CPHD recursion is derived, which can distinguish different targets and clutter by the correspondence weight. In the implementation, we adopt the Gaussian mixture (GM) approach to implement the amplitude-aided CPHD filter to achieve efficient performance. In numerical experiments, the results show that the proposed method attains a significant improvement in performance over that only using location measurements.

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Maritime Targets Tracking in Heavy-tailed Clutter With Unknown and Time-varying Density

Shi,

Kuang,

2024

Int. J. Control Autom. Syst.

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Tracking Small Targets in Heavy-Tailed Clutter Using Amplitude Information

Cited by 57 publications

References 24 publications

Tracking targets in K clutter via particle filter

Tracking targets in K clutter via particle filter

Amplitude-Aided CPHD Filter for Multitarget Tracking in Infrared Images

Maritime Targets Tracking in Heavy-tailed Clutter With Unknown and Time-varying Density

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