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exportToFunction

Export filter initialization function

Since R2022b

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Syntax

exportToFunction(tuner,funName)

Description

exportToFunction(tuner,funName) exports a filter initialization function specified in funName from the trackingFilterTuner object tuner and shows the function in the MATLAB editor.

Note

Use the tune object function before calling the exportToFunction function if you have changed any of these properties of the tuner:

  • FilterInitializationFcn

  • TunablePropertiesSource

  • CustomTunableProperties

example

Examples

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Open Live Script

Load the tuning data containing the truth and detection data. The truth data has the position and velocity of one target for a duration of 9.5 seconds. The detection data has object detections of ten Monte-Carlo runs for the same period.

load("filterTuningData.mat","truth","detlog");

Create a trackingFilterTuner object. Specify the FilterInitializationFcn property as "initcvkf" that corresponds to a trackingKF filter object with a constant velocity model.

tuner = trackingFilterTuner(FilterInitializationFcn ="initcvkf");

You can obtain the filter by evaluating the initialization function on an object detection.

filter = feval(tuner.FilterInitializationFcn,detlog{1})
filter = 
  trackingKF with properties:

               State: [6x1 double]
     StateCovariance: [6x6 double]

         MotionModel: '3D Constant Velocity'
        ProcessNoise: [3x3 double]

    MeasurementModel: [3x6 double]
    MeasurementNoise: [3x3 double]

     MaxNumOOSMSteps: 0

     EnableSmoothing: 0

To customize the tunable properties of the filter, first get the default tunable properties of the filter. 

tps = tunableProperties(filter)
tps = 
Tunable properties for object of type: trackingKF

Property:      ProcessNoise
   PropertyValue:   [1 0 0;0 1 0;0 0 1]
   TunedQuantity:   Square root
   IsTuned:         true
       TunedQuantityValue:  [1 0 0;0 1 0;0 0 1]
       TunableElements:     [1 4 5 7 8 9]
       LowerBound:          [0 0 0 0 0 0]
       UpperBound:          [10 10 10 10 10 10]
Property:      StateCovariance
   PropertyValue:   [3.53553390593274 0 0 0 0 0;0 100 0 0 0 0;0 0 3.53553390593274 0 0 0;0 0 0 100 0 0;0 0 0 0 3.53553390593274 0;0 0 0 0 0 100]
   TunedQuantity:   Square root of initial value
   IsTuned:         false

Based on the display, the tuner tunes only the ProcessNoise property, which is a 3-by-3 matrix. Change the tunable elements to be only the diagonal elements by using the setPropertyTunability object function. Set the lower and upper bounds for tuning the diagonal elements.

setPropertyTunability(tps,"ProcessNoise",TunableElements=[1 5 9], ...
    LowerBound=[0.01 0.01 0.01],UpperBound = [20 20 20])

To enable custom tunable properties, set the TunablePropertiesSource and CustomTunable properties to "Custom" and tps, respectively.

tuner.TunablePropertiesSource = "Custom";
tuner.CustomTunableProperties = tps;

Using the tune object function, tune the filter with the detection log and the truth data. 

tune(tuner,detlog,truth);
Iter        RMSE          Step Size
   0       9.2177                
   1       9.1951          0.1509
   2       9.0458          1.5108
   3       9.0456          0.0186
   4       9.0452          0.0705
   5       9.0452          0.0068
   6       9.0452          0.0016
   7       9.0451          0.1422
   8       9.0450          0.0600
   9       9.0450          0.0182
  10       9.0450          0.0105

Generate the filter initialization function after tuning by using the exportToFunction object function.

exportToFunction(tuner,"tunedInitFcn")

Obtain the tuned filter by evaluating the tuned initialization function on an object detection. Show the tuned process noise.

tunedFilter = tunedInitFcn(detlog{1})
tunedFilter = 
  trackingKF with properties:

               State: [6x1 double]
     StateCovariance: [6x6 double]

         MotionModel: '3D Constant Velocity'
        ProcessNoise: [3x3 double]

    MeasurementModel: [3x6 double]
    MeasurementNoise: [3x3 double]

     MaxNumOOSMSteps: 0

     EnableSmoothing: 0


tunedFilter.ProcessNoise
ans = 3×3

    0.0001         0         0
         0    0.0156         0
         0         0    0.0001

Input Arguments

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Tracking filter tuner, specified as a trackingFilterTuner object.

Function name, specified as a string scalar or a character vector.

Example: "myInitFun"

Data Types: char | string

Version History

Introduced in R2022b

See Also