Square root Gaussian mixture PHD filter for multi-target bearings only tracking

Wong, Shanhung; Vo, Ba-Tuong

doi:10.1109/issnip.2011.6146607

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…These problems can be solved by considering the various data association techniques to identify the similar information pertaining to a particular target and fused using fusion techniques to obtain the optimized target sate. [105], [76] and [13].…”

Section: Underwater Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Bearing only Target Tracking using Single and Multisensor: A Review

Sindhu¹,

Valarmathi²,

Christopher³

et al. 2019

JESTR

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The brief review of methods used for estimating the target state in single and multi-sensor bearing only tracking (BOT) is presented in this paper. It deals with the target state estimation using bearing only measurements. BOT is difficult because of its poor observability in target state and nonlinearity in measurements. The complete survey is done on existing techniques, involved to overcome the difficulties caused by BOT. Here, the target tracking scenarios are divided into three different categories based on the nature of target motion and the number of target and sensors involved. The existing techniques involved are reviewed in detail. Finally the future trends for BOT are also discussed.

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Section: Underwater Applicationsmentioning

confidence: 99%

“…S.J. Wong et.al [13] has discussed about the multi-sensor multitarget tracking in BOT. The authors have presented the Gaussian mixture probability density hypothesis (GM-PHD) using EKF and UKF filter.…”

Section: Recursive Nonlinear Techniquesmentioning

confidence: 99%

Bearing only Target Tracking using Single and Multisensor: A Review

Sindhu¹,

Valarmathi²,

Christopher³

et al. 2019

JESTR

View full text Add to dashboard Cite

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“…It can be used to handle a variety of problems such as multiple on/off switching systems, detection imperfections, and inaccurate estimates [ 18 ]. Target tracking with bearings-only measurements by RFS-based filters has been studied from various aspects [ 19 , 20 , 21 , 22 ]. In Reference [ 23 ], Bernoulli filter tracking along with fusion is applied in an active multi-static acoustic sensor network.…”

Section: Introductionmentioning

confidence: 99%

Joint Passive Detection and Tracking of Underwater Acoustic Target by Beamforming-Based Bernoulli Filter with Multiple Arrays

Chen

2018

Sensors

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In this paper, improved Bernoulli filtering methods are developed to deal with the problem of joint passive detection and tracking of an underwater acoustic target with multiple arrays. Three different likelihood calculation methods based on local beamforming results are proposed for the Bernoulli filter updating. Firstly, multiple peaks, including both mainlobe and sidelobe peaks, are selected to form the direction-of-arrival (DOA) measurement set, and then the Bernoulli filter is used to extract the target track. Secondly, to make full use of the informations in the beamforming output, not only the DOAs but also their intensities, the beam powers are used as the input measurement sets of the filter, and an approach based on Pearson correlation coefficient (PCC) is developed for distinguishing between signal and noise. Lastly, a hybrid method of the former two is proposed in the case of fewer then three arrays. The tracking performances of the three methods are compared in simulations and experiment. The simulations with three distributed arrays show that, compared with the DOA-based method, the beam-based method and the hybrid method can both improve the target tracking accuracy. The processing results of the shallow water experimental data collected by two arrays show that the hybrid method can achieve a better tracking performance.

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“…The PHD-based tracking approaches have been successfully applied in many practical problems, such as bearings only tracking [12], extended target tracking [13], maneuvering target tracking [14][15], multiple target tracking in the presence of occlusion caused by terrain obscuration or Doppler blindness [16][17], joint multiple target tracking and spatial registration [18], target tracking with delayed and out-of-sequence measurements [19] and so on.…”

Section: Introductionmentioning

confidence: 99%

Fast Gaussian Mixture Probability Hypothesis Density Filter

Jiang

Liu

et al. 2014

AMM

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Although the Gaussian mixture probability hypothesis density (GMPHD) filter is a multi-target tracker that can alleviate the computational intractability of the optimal multi-target Bayes filter and its computational complex is lower than that of sequential Monte Carlo probability hypothesis density (SMCPHD), its computational burden can be reduced further. In the standard GMPHD filter, each observation should be matched with each component when the PHD is updated. In practice, time cost of evaluating many unlikely measurements-to-components parings is wasteful, because their contribution is very limited. As a result, a substantial reduction in complexity could be obtained by directly setting relative value associated with these parings. A fast GMPHD algorithm is proposed in the paper based on gating strategy. Simulation results show that the fast GMPHD can save computational time by 60%~70% without any degradation in performance compared with standard GMPHD.

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Square root Gaussian mixture PHD filter for multi-target bearings only tracking

Cited by 8 publications

References 13 publications

Bearing only Target Tracking using Single and Multisensor: A Review

Bearing only Target Tracking using Single and Multisensor: A Review

Joint Passive Detection and Tracking of Underwater Acoustic Target by Beamforming-Based Bernoulli Filter with Multiple Arrays

Fast Gaussian Mixture Probability Hypothesis Density Filter

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