Tracking Highly Maneuverable Targets With Unknown Behavior

Schell, Chad; Linder, S.P.; Zeider, J.R.

doi:10.1109/jproc.2003.823151

Cited by 23 publications

(15 citation statements)

References 29 publications

(44 reference statements)

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“…Several data association algorithms exist that are designed to work with tracking algorithms to either simultaneously track multiple fish or to track a single fish in measurement clutter. Bar-Shalom and Li [3] provide several examples of such algorithms for use with Kalman filters, and a method of using the STI in these frameworks can be found in [ 18,19]. The STI algorithm has one additional possible advantage not previously mentioned.…”

Section: Discussionmentioning

confidence: 99%

“…The Segmenting Track Identifier (STI) [19] is a nonBayesian curve segmentation and fitting algorithm, originally presented in [14] that recursively develops a suboptimal, segmented, least-squares fit of a continuously acquired track to a parametric motion model. The goal of the algorithm is to partition the track into segments S n , n = 1...N, with the motion of each segment described by a single parameter vector x n , such that the total fitting cost for the entire track is minimized.…”

Section: Segmenting Track Identifiermentioning

confidence: 99%

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Experimental Evaluation of Tracking Algorithms Used for the Determination of Fish Behavioral Statistics

Schell

Linder

2006

IEEE J. Oceanic Eng.

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Abstract-The fine scale swimming behavior of fish can now be studied because of the development of sophisticated measurement devices such as multibeam sonar and stereo video systems. However, even with these sensors, improved methods are still required to generate quality estimates of swimming speeds and turn rates. Biologist have commonly relied on point-wise differentiation of noisy position measurements, while engineers have focused on Bayesian algorithms to track underwater vehicles. A comparative evaluation of the performance of these tracking algorithms for the analysis of fine scale behavior of fish was performed using a data set of 100 fish track tracks recorded simultaneously with a multibeam sonar and a stereo video camera system. The Segmenting Track Identifier, a non-Bayesian curve fitting and segmenting tracker, is shown to be most effective for tracking the unpredictable and complex horizontal motion of fish, while a Kalman smoother using a constant-velocity model is shown to be most effective for tracking the more predictable and piecewise linear vertical motion of fish. Both are shown to be more effective than point-wise differentiation. Criteria for selecting an appropriate algorithm for a given motion study are provided

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Section: Discussionmentioning

confidence: 99%

Section: Segmenting Track Identifiermentioning

confidence: 99%

Experimental Evaluation of Tracking Algorithms Used for the Determination of Fish Behavioral Statistics

Schell

Linder

2006

IEEE J. Oceanic Eng.

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“…The goal of in situ measurements is to estimate an average swimming pattern, as this can be expressed in backscattering. Either implemented in software (Ona & Hansen, 1991;Schell et al, 2004) or hardware (Hedgepeth et al, 2002), single fish tracking algorithms isolate single fish echoes and provide TS point estimates as well as further information concerning the swimming pattern (Furusawa & Amakusu, 2010).…”

Section: Target Strength Estimationmentioning

confidence: 99%

Accuracy of Acoustic Methods in Fish Stock Assessment Surveys

Georgakarakos¹,

Trygonis²,

Haralabous³

2011

Sonar Systems

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“…Moreover, it requires known process noises. A data-driven approach to tracking which has been presented in [13] uses the least squares fitting of a motion model to a segment of data. Considered process noises, the motion models fitted cannot exactly represent the real motion models of the target.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy probabilistic data association filter and its application to single maneuvering target

Fan

Shen

et al. 2016

EURASIP J. Adv. Signal Process.

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The fuzzy recursive least squares-probabilistic data association (FRLS-PDA) filter is presented for tracking single maneuvering target in cluttered situations with unknown process noises. In the proposed filter, the association probabilities of the current valid measurements belonging to a motion target are calculated by the probabilistic data association (PDA) algorithm. Then these probabilities are used to weight the valid measurements for generating a fused measurement, which is applied to determine the maneuvering characteristics of the moving target in real time including the current measurement residual and heading change. According to the above characteristics calculated, the fuzzy recursive least squares (FRLS) filter is used to estimate the current state of the target. The proposed filter can provide the advantage of the FRLS filter, which relaxes the restrictive assumptions of motion models of a maneuvering target. Moreover, it can realize single maneuvering target tracking in cluttered situations. The performance of the FRLS-PDA filter is evaluated by two experiments with the simulation data and real data, and it is found to be better than those of the PDA filter, IMM-PDA filter, fuzzy adaptive α-β filter, and FRLS filter in tracking accuracy.

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Tracking Highly Maneuverable Targets With Unknown Behavior

Cited by 23 publications

References 29 publications

Experimental Evaluation of Tracking Algorithms Used for the Determination of Fish Behavioral Statistics

Experimental Evaluation of Tracking Algorithms Used for the Determination of Fish Behavioral Statistics

Accuracy of Acoustic Methods in Fish Stock Assessment Surveys

Fuzzy probabilistic data association filter and its application to single maneuvering target

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