Best choice of coordinate system for tracking coordinated turns

Gustafsson, Fredrik; Isaksson, Alf

doi:10.1109/cdc.1996.573612

Cited by 51 publications

(53 citation statements)

References 2 publications

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“…The following results are taken from Gustafsson and Isaksson (1996). For discretized linearization, the Frobenius norm of the Hessian for the state transition function f ( x ) is i=l for coordinates (dl), d 2 ) , d'), d 2 ) , u )~, and for coordinates (&I, d 2 ) , v, h, w )~.…”

Section: The Error In Linearizing State Variable I Ismentioning

confidence: 99%

Adaptive Filtering and Change Detection

Gustafsson¹

2001

595

699

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Section: The Error In Linearizing State Variable I Ismentioning

confidence: 99%

Adaptive Filtering and Change Detection

Gustafsson¹

2001

595

699

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“…As discussed in Gustafsson and Isaksson [1996], the former approach indeed tends to work better in the case of EKF, as it theoretically should, because in a sense, there are fewer Gaussian approximations involved. In linearized discretization the Gaussian approximations are only formed on the discretization steps, but in discretized linearization Gaussianity is assumed for all time instants on the sampling interval.…”

Section: Comparison Of Discretizationsmentioning

confidence: 86%

“…Gustafsson and Isaksson, 1996]. In that method we first discretize the continuous-time dynamic model and then apply discrete-time filter to the resulting discrete time system.…”

Section: Introductionmentioning

confidence: 99%

On Continuous-Discrete Cubature Kalman Filtering

Särkkä

Solin

2012

IFAC Proceedings Volumes

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“…A coordinated turn describes motion along a circular path with a constant turn rate. The particular coordinated turn model used is the polar velocity coordinated turn of Gustafsson and Isaksson [7], as they showed analytically that this implementation of the coordinated turn model has lower linearization error than a model based on Cartesian coordinate velocities. The polar velocity model is actually a target-oriented model, using the…”

Section: B Kalman Algorithmsmentioning

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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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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Best choice of coordinate system for tracking coordinated turns

Cited by 51 publications

References 2 publications

Adaptive Filtering and Change Detection

Adaptive Filtering and Change Detection

On Continuous-Discrete Cubature Kalman Filtering

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

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