Tracking in clutter with strongest neighbor measurements. I. Theoretical analysis

Li, X.R.

doi:10.1109/9.728872

Cited by 89 publications

(41 citation statements)

References 12 publications

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“…These range from Kalman filters, nearest-neighbour standard filter [1,2], joint probability data association [3] to probability hypothesis density filtering [4][5][6][7] and so forth, which mainly focused on data association problem.…”

Section: Introductionmentioning

confidence: 99%

Improved weak space object tracking assisted by strong target

Zeng

Liu

et al. 2015

IET Radar, Sonar & Navigation

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Radar sensor is a promising candidate for space surveillance, providing timely assessment of space objects and events. Weak space target tracking is a challenging conundrum mainly because of the low detection probability and poor measurement accuracy. In this study, a novel method of weak space target tracking assisted by strong space target is proposed. By introducing the relative motion tracking, the steady-state estimates of strong target are utilised to improve the state estimation of weak target. Compared with weak target tracking without strong target assistance, the proposed tracking method can provide higher state estimation accuracy, faster convergence rate and better track validity probability for weak target. The results of numerical simulations have validated the effectiveness of the proposed technique.

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Section: Introductionmentioning

confidence: 99%

Improved weak space object tracking assisted by strong target

Zeng

Liu

et al. 2015

IET Radar, Sonar & Navigation

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“…The applicable data association methods in the environment with this clutter include the Nearest Neighbor (NN) method series [1,2], the Strongest Neighbor (SN) method series [3,4], and the Probabilistic Data Association (PDA) method series [5,6], etc. The Integrated PDA (IPDA) method [7] and the IPDA with the Amplitude Information (IPDA-AI) method [8] have been widely used as the track initiation methods that judge the existence of the track.…”

Section: Introductionmentioning

confidence: 99%

A study of a new data association and track initiation method with normalized distance squared ordering

Kim

Lim

Song

2011

Int. J. Control Autom. Syst.

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In an underwater environment, measurements regarding true targets and false targets (clutter) can be made. Therefore, a suitable data association method to exactly detect and track a target and an efficient track initiation method for judging tracks formed by the target should be selected in this environment. This paper attempts to propose a new data association method and track initiation method to detect and track targets more effectively. Also, the performance of the new method is tested in a series of Monte Carlo simulation runs and is compared with the existing data association and track initiation methods in a cluttered environment.

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“…PDA utilizes a weighted average of all the measurements to update the track state. Nearest neighbor association selects a measurement which is located with the smallest normalized distance squared [13] while strongest neighbor association selects a measurement with the strongest signal amplitude [14]. Highest probability data association (HPDA) proposed in this paper selects a measurement with the highest probability that it is targetoriginated and uses it to update the target state so that it makes one-to-one assignments of measurement to track.…”

Section: Introductionmentioning

confidence: 99%

Highest Probability Data Association for Active Sonar Tracking

Song

Kim

2006

2006 9th International Conference on Information Fusion

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We propose a new method of data association called highest probability data association (HPDA) combined with particle filtering and applied to active sonar tracking in clutter. The proposed HPDA method is a unification of probabilistic nearest neighbor and probabilistic strongest neighbor approaches. It evaluates the probabilities of one-to-one assignments of measurement-to-track. All of the measurements at the present sampling instance are lined up in the order of signal strength. The measurement with the highest probability is selected to be target-originated and the measurement is usedfor probabilistic weight update of particle filtering. The HPDA algorithm can be used in Automatic target detection for track confirmation and estimation of the number of the targets. The proposed HPDA algorithm is easily extended to multi-target tracking problems. It can be used to avoid track coalescence phenomenon that prevails when several tracks move very close.

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Tracking in clutter with strongest neighbor measurements. I. Theoretical analysis

Cited by 89 publications

References 12 publications

Improved weak space object tracking assisted by strong target

Improved weak space object tracking assisted by strong target

A study of a new data association and track initiation method with normalized distance squared ordering

Highest Probability Data Association for Active Sonar Tracking

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