setTunedProperties
Description
setTunedProperties(
sets the tunable properties to the tuned values in the filter
,tunedProps
)tunedProps
input
argument.
Examples
Load truth and detection data.
load("filterTuningData.mat","truth","detlog");
Create a default trackingFilterTuner
object. Obtain the trackingEKF
object using the FilterInitializationFcn
property.
tuner = trackingFilterTuner; filter = feval(tuner.FilterInitializationFcn,detlog{1});
Tune the filter to obtain tuned properties.
tunedProps = tune(tuner,detlog,truth)
Iter RMSE Step Size 0 9.2177 1 9.1951 0.1509 2 9.0458 1.5276 3 9.0456 0.0176 4 9.0452 0.0706 5 9.0452 0.0142 6 9.0452 0.0012 7 9.0452 0.0119 8 9.0452 0.0015 9 9.0451 0.0513 10 9.0450 0.0742 11 9.0450 0.0326 12 9.0450 0.0122
tunedProps = struct with fields:
ProcessNoise: [3×3 double]
StateCovariance: [6×6 double]
Set the tunable properties of the filter by using the setTuendProperty
object function of the filter. Display the tuned process noise.
setTunedProperties(filter,tunedProps); disp(filter.ProcessNoise);
0 0 0 0 0.0143 0 0 0 0.0000
Input Arguments
Tracking filter, specified as one of these objects:
Tuned values, specified as a structure. In most cases, you can generate this
structure from the tune
object
function of a trackingFilterTuner
object.
This structure can have different fields for different tracking filter objects since
the tunable properties of each tracking filter are different. For example, for the
trackingEKF
object, this structure has two
fields: ProcessNoise
and StateCovariance
.
Additionally, the value format of each field varies based on the setup of the tracking
filter object. To see the tunable properties and corresponding value formats, use the
tunableProperties
object function of the filter.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Version History
Introduced in R2022b
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