setTunedProperties

Set properties to tuned values

Since R2022b

Syntax

setTunedProperties(filter,tunedProps)

Description

setTunedProperties(filter,tunedProps) sets the tunable properties to the tuned values in the tunedProps input argument.

Examples

Set Tuned Properties of trackingEKF

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

filter — Tracking filter

trackingKF object | trackingEKF object | trackingUKF object | trackingCKF object

Tracking filter, specified as one of these objects:

• trackingKF

• trackingEKF

• trackingUKF

• trackingCKF

• trackingIMM

• trackingGSF

tunedProps — Tuned values

structure

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