&lt;title&gt;Improved multi-target tracking using probability hypothesis density smoothing&lt;/title&gt;

Nandakumaran, N.; Punithakumar, Kumaradevan; Kirubarajan, T.

doi:10.1117/12.734656

Cited by 15 publications

(22 citation statements)

References 12 publications

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“…This best estimate at a given time can be improved significantly by smoothing or retrodiction, which uses more measurements beyond the time in question [1,8]. To improve multitarget tracking the backward PHD smoothing algorithm, which is developed in [13,14], is applied to outputs of the GMPHD filter. The continuous backward PHD smoothing recursion is given by, [13,14],…”

Section: Phd Smoothingmentioning

confidence: 99%

“…To improve multitarget tracking the backward PHD smoothing algorithm, which is developed in [13,14], is applied to outputs of the GMPHD filter. The continuous backward PHD smoothing recursion is given by, [13,14],…”

Section: Phd Smoothingmentioning

confidence: 99%

“…This best estimate at a given time can be improved significantly by smoothing or retrodiction, which uses more measurements beyond the time in question [1,8]. To improve the above filter outputs, the backward PHD smoothing algorithm from [13,14] is extended to GMPHD. The resulting algorithm provides delayed, but better, estimates of the target states.…”

Section: Introductionmentioning

confidence: 99%

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Gaussian mixture probability hypothesis density smoothing with multistatic sonar

Nandakumaran

Kirubarajan

Lang

et al. 2008

Signal Processing, Sensor Fusion, and Target Recognition XVII

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Passive sonar is widely used in practice to covertly detect maritime vessels. However, the detection of stealthy vessels often requires active sonar. The risk of the overt nature of active sonar operation can be reduced by using multistatic sonar techniques. Cheap sonar sensors that do not require any beamforming technique can be exploited in a multistatic system for spacial diversity. In this paper, Gaussian mixture probability hypothesis density (GMPHD) filter, which is a computationally cheap multitarget tracking algorithm, is used to track multiple targets using the multistatic sonar system that provides only bistatic range and Doppler measurements. The filtering results are further improved by extending the recently developed PHD smoothing algorithm for GMPHD. This new backward smoothing algorithm provides delayed, but better, estimates for the target state. Simulations are performed with the proposed method on a 2-D scenario. Simulation results present the benefits of the proposed algorithm.

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Section: Phd Smoothingmentioning

confidence: 99%

Section: Phd Smoothingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gaussian mixture probability hypothesis density smoothing with multistatic sonar

Nandakumaran

Kirubarajan

Lang

et al. 2008

Signal Processing, Sensor Fusion, and Target Recognition XVII

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“…This best estimate at a given time can be improved significantly by smoothing or retrodiction, which uses more measurements beyond the current estimation time [4,7]. In this paper, a smoothing method is proposed to improve the capability of the MMPHD based state estimator that is a natural extension of PHD smoothing method [12] for maneuvering target tracking. By performing smoothing, which gives delayed but better estimates.…”

Section: Introductionmentioning

confidence: 97%

Maneuvering target tracking using probability hypothesis density smoothing

Nadarajah

Kirubarajan

2009

SPIE Proceedings

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The Probability Hypothesis Density (PHD) filter is a computationally tractable alternative to the optimal nonlinear filter. The PHD filter propagates the first moment instead of the full posterior density. Evaluation of the PHD enables one to extract the number of targets as well as their individual states from noisy data with data association uncertainties. Recently, a smoothing algorithm was proposed by the authors to improve the capability of PHD based tracking. Smoothing produces delayed estimates, which yield better estimates not only for the target states but also for the unknown number of targets. However, in the case of the maneuvering target tracking problem, this single model method may not provide accurate estimates. In this paper, a multiple model PHD smoothing method is proposed to improve the tracking of multiple maneuvering targets. A fast sequential Monte Carlo implementation for a special case is also provided. Simulations are performed with the proposed method consisting of multiple maneuvering targets. Simulation results confirm the improved performance of the proposed algorithm.

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“…For tracking multiple maneuvering targets, similar results have been extended to handle jump Markov models [7][8][9][10]. To derive PHD smoothers, the particle and Gaussian mixture techniques have also been used [11][12][13][14][15][16][17][18]. In [19], the GM-PHD filter is extended to multi-sensor tracking system and the target state estimates are obtained sequentially at each sensor.…”

Section: Introductionmentioning

confidence: 99%

Promotion of GM-PHD Filtering Approach for Single-Target Tracking in Raw Data of Synthetic Aperture Radar in Spotlight Imaging Mode

Sadeghzadeh¹,

Daryasafar²,

Naser‐Moghadasi³

2017

RADIOENGINEERING

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<title>Improved multi-target tracking using probability hypothesis density smoothing</title>

Cited by 15 publications

References 12 publications

Gaussian mixture probability hypothesis density smoothing with multistatic sonar

Gaussian mixture probability hypothesis density smoothing with multistatic sonar

Maneuvering target tracking using probability hypothesis density smoothing

Promotion of GM-PHD Filtering Approach for Single-Target Tracking in Raw Data of Synthetic Aperture Radar in Spotlight Imaging Mode

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