tune
Description
runs the tunedProperties
= tune(tuner
,detectionLog
,truth
)trackingFilterTuner
object, tunes the filter based on the
detection log and the truth data, and returns the tuned filter properties.
Examples
Customize Tunable Properties and Tune Tracking Filter
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
tuner
— Tracking filter tuner
trackingFilterTuner
object
Tracking filter tuner, specified as a trackingFilterTuner
object.
detectionLog
— Detection log
cell arrays of objectDetection
objects
Detection log, specified as cell arrays of objectDetection
objects.
Specify detectionLog
based on the number of truth targets that you specify in the truth
input argument.
One truth target — Specify
detectionLog
as a T-by-N cell array ofobjectDetection
objects. N is the number of Monte-Carlo runs. The number of time steps T does not have to be equal to the number of rows in the truth table. The timestamp of each detection does not have to match the time in the truth table.Multiple truth targets — Specify
detectionLog
as an M-element cells, where each cell contains a cell array ofobjectDetection
objects for the corresponding truth timetable. M is the number of truth tables. Across the M cells, the detection time, the number of detections, and the number of Monte-Carlo runs do not have to be the same.
truth
— Truth data
structure | table | timetable | cell array of structures | cell array of tables | cell array of timetables
Truth data, specified as a structure, table, timetable, cell array of structures, cell array of tables, cell array of timetables.
You can specify truth
for one or more truth targets.
One truth target — Specify
truth
as a truth structure, table, or timetable.When specified as a structure, the structure must contain a field
Time
and eitherPosition
andVelocity
fields or aState
field.tune
converts the structure to a timetable and uses it as defined below.When specified as a table, or timetable, the
truth
must have these variables as columns:Time
— Time of the truth information, specified as a single, double, orduration
in each table row.Position
— Position of the target at the time, specified as a 1-by-3 real-valued vector in each table row.Velocity
— Velocity of the target at the time, specified as a 1-by-3 real-valued vector in each table row. This variable is optional.State
— State of the target at the time, specified as 1-by-S real-valued vector in each table row, where S is dimension of the state.Note
When you specify the
State
column, the tuner ignores thePosition
andVelocity
columns if specified.The definition of the
State
column must be exactly the same as the state definition in the tuned filter. For example, if the filter defines its state as [x y v q], where x and y are positions in a 2-D rectangular frame, v is the speed, and q is the heading angle, you must specify the timetable with aState
column in the same [x y v q] definition.
Multiple truth targets — Specify
truth
as an M-element cell array of truth structures, tables, or timetables. Each truth structure, table, or timetable can have different initial and end times.
Output Arguments
tunedProperties
— Tuned properties
structure
Tuned properties, returned as a structure. For different tracking filter objects,
this structure can have different fields since the tunable properties of each tracking
filter can be different. For example, for the trackingEKF
object, this structure has two fields:
ProcessNoise
and StateCovariance
.
You can use the setTunedProperties
object function of each filter object to apply the
tuned properties.
Version History
Introduced in R2022bR2024a: Specify truth tuning data using tables or structure
When specifying the truth timetable input, you can use tables or structures. Previously, you could only use timetables.
R2023b: Specify truth tuning data using state variable
When specifying the truth timetable input of the tune
function,
you can use a column called State
to describe the truth target state.
Previously, you had to use the Position
and Velocity
columns to describe the truth target state. Using the State
column
provides more flexibility on defining target state.
Note that when the truth timetable contains the State
column, the
tuner ignores the Position
and Velocity
columns if
specified.
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