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correctjpda

Correct state and state estimation error covariance using tracking filter and JPDA

Since R2021a

Description

[xcorr,Pcorr] = correctjpda(filter,zmeas,jpdacoeffs) returns the corrected state, xcorr, and the corrected state estimation error covariance, Pcorr, for the next time step of the input tracking filter. The corrected values are based on a set of measurements, zmeas, and their joint probabilistic data association coefficients, jpdacoeffs. These values overwrite the internal state and state estimation error covariance of filter.

[xcorr,Pcorr] = correctjpda(filter,zmeas,jpdacoeffs,measparams) specifies additional parameters used by the measurement function that is defined in the MeasurementFcn property of the tracking filter object.

If filter is a trackingKF or trackingABF object, then you cannot use this syntax.

[xcorr,Pcorr] = correctjpda(filter,zmeas,jpdacoeffs,zcov) specifies additional measurement covariance, zcov, used in the MeasurementNoise property of filter.

You can use this syntax only when filter is a trackingKF object.

[xcorr,Pcorr,zcorr] = correctjpda(filter,zmeas,jpdacoeffs) also returns the correction of measurements, zcorr.

You can use this syntax only when filter is a trackingABF object.

[xcorr,Pcorr,zcorr] = correctjpda(filter,zmeas,jpdacoeffs,zcov) returns the correction of measurements, zcorr, and also specifies additional measurement covariance, zcov, used in the MeasurementNoise property of filter.

You can use this syntax only when filter is a trackingABF object.

Input Arguments

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Filter for object tracking, specified as one of these objects:

Measurements, specified as an M-by-N matrix, where M is the dimension of a single measurement, and N is the number of measurements.

Data Types: single | double

Joint probabilistic data association coefficients, specified as an (N+1)-element vector. The ith (i = 1, …, N) element of jpdacoeffs is the joint probability that the ith measurement in zmeas is associated with the filter. The last element of jpdacoeffs corresponds to the probability that no measurement is associated with the filter. The sum of all elements of jpdacoeffs must equal 1.

Data Types: single | double

Measurement covariance, specified as an M-by-M matrix, where M is the dimension of the measurement. The same measurement covariance matrix is assumed for all measurements in zmeas.

Data Types: single | double

Measurement function arguments, specified as a comma-separated list of arguments. These arguments are the same ones that are passed into the measurement function specified by the MeasurementFcn property of the tracking filter. If filter is a trackingKF or trackingABF object, then you cannot specify measparams.

Suppose you set MeasurementFcn to @cameas, and then call correctjpda:

[xcorr,Pcorr] = correctjpda(filter,frame,sensorpos,sensorvel)
The correctjpda function internally calls the following:
meas = cameas(state,frame,sensorpos,sensorvel)

Output Arguments

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Corrected state, returned as a P-element vector, where P is the dimension of the estimated state. The corrected state represents the a posteriori estimate of the state vector, taking into account the current measurements and their associated probabilities.

Corrected state error covariance, returned as a positive-definite P-by-P matrix, where P is the dimension of the state estimate. The corrected state covariance matrix represents the a posteriori estimate of the state covariance matrix, taking into account the current measurements and their associated probabilities.

Corrected measurements, returned as an M-by-N matrix, where M is the dimension of a single measurement, and N is the number of measurements. You can return zcorr only when filter is a trackingABF object.

More About

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References

[1] Fortmann, T., Y. Bar-Shalom, and M. Scheffe. "Sonar Tracking of Multiple Targets Using Joint Probabilistic Data Association." IEEE Journal of Ocean Engineering. Vol. 8, Number 3, 1983, pp. 173–184.

Extended Capabilities

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Version History

Introduced in R2021a