trackingFilterTuner
Description
The trackingFilterTuner
object creates a tracking filter tuner that tunes
the tunable properties of a tracking filter object, such as the trackingEKF
object. Use the FilterInitializationFcn
property to initialize a tracking filter for
tuning. Use the tune
object
function to tune the filter.
Creation
Description
creates a
tracking filter tuner with default property values.tuner
= trackingFilterTuner
specifies properties using one or more name-value arguments. For example,
tuner
= trackingFilterTuner(Name=Value)trackingFilterTuner(Solver="patternsearch")
specifies the pattern
search algorithm as the solver algorithm.
Properties
FilterInitializationFcn
— Filter initialization function
"initcvekf"
(default) | string scalar | charter array
Filter initialization function, specified as a string scalar or a character vector representing the name of a valid filter initialization function. For this property, a valid filter initialization function is a function that returns a tunable tracking filter object. These tracking filter objects are tunable:
You can use any of these built-in filter initialization functions.
To write a custom initialization function, refer to the code for any of
the initialization functions. For example, type the following command in the command
window to see the implementation for the initcvekf
function.
edit initcvekf
Note
If you specify the UseMex
property to
true
, changing the value of the
FilterInitializationFcn
property triggers code
generation.
Data Types: char
| string
TunablePropertiesSource
— Source of filter tunable properties
"Default"
(default) | "Custom"
Source of filter tunable properties, specified as:
"Default"
— The filter object specified in theFilterInitializationFcn
property defines the properties to be tuned, the tuned elements, and the tuning bounds. To get the tunable properties of the filter, use thetunableProperties
object function of the tunable filter object."Custom"
— Specify the tunable properties as atunableFilterProperties
object in theCustomTunableProperties
property.
Note
If you set the UseMex
property to true
,
changing the value of the TunablePropertiesSource
property
triggers code generation.
Data Types: char
| string
CustomTunableProperties
— Custom tunable properties
tunableFilterProperties
object
Custom tunable properties, specified as a tunableFilterProperties
object. You can use the tunableProperties
function of the filter object to obtain the default
tunableFilterProperties
object and modify it to obtain a custom tunableFilterProperties
object.
Note
If you set the UseMex
property to true
,
changing the value of the CustomTunableProperties
property
triggers code generation.
Dependencies
To enable this property, set the TunablePropertiesSource
property to "Custom"
.
Data Types: object
Cost
— Tuning cost
"RMSE"
(default) | "NEES"
| "NLL"
| "Custom"
Tuning cost, specified as one of these options:
"RMSE"
— The tuner minimizes the root mean square of the error between the ground truth and the filter estimates over all the time steps and Monte-Carlo runs. Use this option if the absolute distance between the truth and estimate is the most important aspect for your tuning. See Objective Function for RMSE for more algorithm details."NEES"
— The tuner minimizes the normalized estimate error squared between the ground truth and the filter estimates over all the time steps and Monte-Carlo runs. Use this option if you want to optimize the state estimate error covariance for a consistent filter. See Objective Function for NEES for more algorithm details."NLL"
— The tuner minimizes the negative log likelihood between the ground truth and the filter estimates over all the time steps and Monte-Carlo runs. Use this option if you want to optimize the filter for a tracker that uses this cost for data association. See Objective Function for NLL for more algorithm details."Custom"
— Specify the cost function in theCustomCostFcn
property. Use a custom cost function if any of these conditions is true:You want to tune the filter using a custom cost.
The truth table used in the
tune
function contains data other than target position, velocity, or state.
Data Types: char
| string
CustomCostFcn
— Custom cost function
string scalar | character vector | function handle
Custom cost function, specified as a string scalar, a character vector, or a function handle. The custom cost function must use the following syntax:
cost = myCostFun(trackHistory,truth)
trackHistory
— N-by-M array of track structures, where N is the number of the rows in thetruth
time table and M is the number of Monte-Carlo runs specified in the detection log input of thetune
object function. Each structure has these fields:UpdateTime
— The time at which the filter was updated, specified as a scalar.State
— The state estimate at the update time.StateCovariance
— The state estimate error covariance at the update time.
truth
— A truth time table that is the same as the truth time table input for thetune
object function.cost
— A scalar value representing the cost.
Dependencies
To enable this property, set the Cost
property to
"Custom"
.
Data Types: char
| string
| function_handle
Solver
— Optimization solver
"active-set"
(default) | "fmincon"
| "patternsearch"
| "particleswarm"
Optimization solver, specified as one of these options:
"active-set"
— Tune the filter by using the active-set optimization algorithm.Note
When using this solver, the tuner can possibly violate the bounds of tunable properties during the tuning process and cause an error. In this case, either reduce the bounds of the tunable property or use a different solver.
"fmincon"
— This option requires Optimization Toolbox™. Seefmincon
(Optimization Toolbox) for details."patternsearch"
— This option requires Global Optimization Toolbox. Seepatternsearch
(Global Optimization Toolbox) for details."particleswarm"
— This option requires Global Optimization Toolbox. Seeparticleswarm
(Global Optimization Toolbox) for details.
Data Types: char
| string
UseMex
— Enable using code generation
false
or 0
(default) | true
or 1
Enable using code generation, specified as a logical 0
(false
) or 1
(true
).
Specifying true
requires MATLAB®
Coder™ license.
If you set the property to true
, the object first generates mex
code and then runs the tuning based on the generated code. For large data sets, using
generated code can expedite the tuning process.
Data Types: logical
UseParallel
— Enable using parallel processing
false
or 0
(default) | true
or 1
Enable using parallel processing, specified as a logical 0
(false
) or 1
(true
).
Specifying true
requires Parallel Computing Toolbox™. For large data sets, using parallel processing can expedite the tuning
process.
Note
If you set the UseMex
property to true
,
changing the value of the useParallel
property triggers code
generation.
Data Types: logical
Display
— Tuner progress display option
"Text"
(default) | "None"
| "Plot"
| "SolverOptions"
Tuner progress display options, specified as one of these:
"Text"
— The tuner displays the progress using an iterative textual display in the command window."None"
— The tuner does not display any information while tuning."Plot"
— The tuner displays the tuning progress as a plot of tuning cost versus number of iterations."SolverOptions"
— The tuner displays the tuning information based on the configuration specified in theSolverOptions
property. To use this option, set theSolver
property as"fmincon"
,"patternsearch"
, or"particleswarm"
.
Data Types: char
| string
SolverOptions
— Options to control solver
[]
(default) | optimoptions
object
Options to control the solver in the Solver
property, specified
as an optimoptions
(Optimization Toolbox) object. The default value of
[]
represents the default setup for the solver.
Object Functions
tune | Tune tracking filter |
exportToFunction | Export filter initialization function |
plotFilterErrors | Plot tracking filter errors |
tuningData | Generate detection log and truth table for tuning |
parameterCost | Get cost for tuning parameters |
tunedParameters | Get tuned parameters |
Examples
Tune trackingEKF
Using Tracking Filter Tuner
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 and set the Solver
property to "fmincon"
.
tuner = trackingFilterTuner(Solver="fmincon");
By default, the FilterInitialization
property of the tuner returns an initialization function that initializes a trackingEKF
object.
initFcn = tuner.FilterInitializationFcn
initFcn = "initcvekf"
Initialize the trackingEKF
object and display the untuned process noise.
filter = feval(tuner.FilterInitializationFcn,detlog{1}); disp(filter.ProcessNoise);
1 0 0 0 1 0 0 0 1
Specify the SolverOptions
property so that the tuner displays the tuning progress for every iteration and set the maximum number of iterations to 30.
tuner.SolverOptions = optimoptions("fmincon",MaxIterations=30);
Using the tune
object function, tune the filter with the detection log and the truth data. Return the tuned properties.
tunedProps = tune(tuner,detlog,truth);
Iter RMSE Step Size 0 9.2941 0.0000 1 9.2749 0.1313 2 9.1827 0.6852 3 9.0777 1.1658 4 9.0734 0.0937 5 9.0548 0.2909 6 9.0472 0.2754 7 9.0477 0.0348 8 9.0474 0.0501 9 9.0474 0.0148 10 9.0473 0.0141 11 9.0473 0.0072 12 9.0459 0.0621 13 9.0455 0.0237 14 9.0455 0.0054 15 9.0455 0.0021 16 9.0455 0.0019 17 9.0452 0.0250 18 9.0451 0.0177 19 9.0451 0.0024 20 9.0451 0.0005 21 9.0451 0.0010 22 9.0451 0.0007 23 9.0451 0.0012 24 9.0451 0.0007 25 9.0451 0.0006 26 9.0451 0.0003 27 9.0451 0.0002 28 9.0450 0.0153 29 9.0450 0.0073 30 9.0450 0.0013 Solver stopped prematurely. fmincon stopped because it exceeded the iteration limit, options.MaxIterations = 3.000000e+01.
Set the filter tunable properties by using the setTunedProperty
object function of the filter. Display the tuned process noise.
setTunedProperties(filter,tunedProps); disp(filter.ProcessNoise);
0.0000 0.0001 0.0000 0.0001 0.0149 0.0007 0.0000 0.0007 0.0002
Plot the filter estimation error after tuning by using the plotFilterErrors
object function.
plotFilterErrors(tuner)
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
Tune Tracking Filter Using Tuning Data Generated from Tracking Scenario
Create a trackingFilterTuner
object and set its Display
property to "Plot"
.
tuner = trackingFilterTuner(Display="Plot")
tuner = trackingFilterTuner with properties: FilterInitializationFcn: "initcvekf" TunablePropertiesSource: "Default" Cost: "RMSE" UseMex: 0 UseParallel: 0 Solver: "active-set"
Load the air traffic control scenario. Use the moterCarloRun
and tuningData
functions to generate the tuning data.
load("ATCScenario.mat")
recordings = monteCarloRun(scenario,1);
[detlog,truthTable] = tuner.tuningData(recordings);
Tune the tracking filter based on the tuning data.
tune(tuner,detlog,truthTable)
ans = struct with fields:
ProcessNoise: [3×3 double]
StateCovariance: [6×6 double]
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}{1})
tunedFilter = trackingEKF with properties: State: [6×1 double] StateCovariance: [6×6 double] StateTransitionFcn: @constvel StateTransitionJacobianFcn: @constveljac ProcessNoise: [3×3 double] HasAdditiveProcessNoise: 0 MeasurementFcn: @cvmeas MeasurementJacobianFcn: @cvmeasjac HasMeasurementWrapping: 1 MeasurementNoise: [3×3 double] HasAdditiveMeasurementNoise: 1 MaxNumOOSMSteps: 0 EnableSmoothing: 0
tunedFilter.ProcessNoise
ans = 3×3
100.0029 0 100.0000
0 100.0000 0
100.0000 0 100.0000
Tune trackingIMM
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 "initekfIMM"
, which corresponds to a trackingIMM
filter object containing three trackingEKF
filter objects.
tuner = trackingFilterTuner(FilterInitializationFcn ="initekfimm")
tuner = trackingFilterTuner with properties: FilterInitializationFcn: "initekfimm" TunablePropertiesSource: "Default" Cost: "RMSE" UseMex: 0 UseParallel: 0 Solver: "active-set" Display: "Text"
Obtain the filter by evaluating the initialization function on an object detection.
filter = feval(tuner.FilterInitializationFcn,detlog{1}); tps = tunableProperties(filter)
tps = Tunable properties for object of type: trackingIMM Property: TransitionProbabilities PropertyValue: [0.9 0.05 0.05;0.05 0.9 0.05;0.05 0.05 0.9] TunedQuantity: Rows sum to one IsTuned: true TunedQuantityValue: [0.9 0.05 0.05;0.05 0.9 0.05;0.05 0.05 0.9] TunableElements: [1 2 3 4 5 6 7 8 9] LowerBound: [0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001] UpperBound: [1 1 1 1 1 1 1 1 1] Property: ModelProbabilities PropertyValue: [0.333333333333333;0.333333333333333;0.333333333333333] TunedQuantity: Columns sum to one IsTuned: true TunedQuantityValue: [0.333333333333333;0.333333333333333;0.333333333333333] TunableElements: [1 2 3] LowerBound: [0.001 0.001 0.001] UpperBound: [1 1 1] The filter contains 3 tracking filters Show tunable properties for filter 1 Show tunable properties for filter 2 Show tunable properties for filter 3
Disable the tuning of the ProcessNoise
properties in the three tracking filters by using the setProperTuanbility
function. With this setup, the tuner tunes only the TransitionProbabilities
and ModelProbabilities
properties.
setPropertyTunability(tps,"ProcessNoise",FilterIndex=1,IsTuned=false); setPropertyTunability(tps,"ProcessNoise",FilterIndex=2,IsTuned=false); setPropertyTunability(tps,"ProcessNoise",FilterIndex=3,IsTuned=false);
To enable custom tunable properties, set the TunablePropertiesSource
and CustomTunableProperties
properties to "Custom"
and tps
, respectively.
tuner.TunablePropertiesSource = "Custom";
tuner.CustomTunableProperties = tps;
Tune the filter and obtain the tuned properties.
tunedProps = tune(tuner,detlog,truth)
Iter RMSE Step Size 0 11.4984 1 10.5860 0.6467 2 10.3850 0.2469 3 10.2415 0.0553 4 10.2505 0.0338 5 10.2278 0.0658 6 10.1940 0.1806 7 10.2251 0.1738 8 10.2225 0.0066 9 10.2042 0.1454 10 10.1976 0.1352 11 10.3699 0.4188 12 10.2894 0.2968 13 10.2965 0.2498 14 10.2886 0.0362 15 10.1763 0.5250 16 10.2446 0.3159 17 10.2597 0.0263 18 10.1836 0.3079 19 10.1794 0.0172 20 10.1890 0.1725 21 10.1870 0.0145 22 10.1484 0.1162 23 10.1435 0.0033 24 10.1384 0.0082 25 10.1354 0.0099 26 10.1322 0.0158 27 10.1331 0.0838 28 10.1316 0.0188 29 10.1145 0.6438 30 10.1338 0.2801 31 10.1312 0.3709 32 10.1153 0.6495 33 10.1118 0.1515 34 10.1340 0.4673 35 10.1164 0.6180 36 10.1344 0.3019 37 10.1244 0.1909 38 10.1162 0.4880 39 10.1141 0.1283 40 10.1127 0.0911 41 10.1116 0.0588 42 10.1112 0.0473 43 10.1110 0.0308 44 10.1109 0.0363 45 10.1107 0.0433 46 10.1104 0.0436 47 10.1096 0.0569 48 10.1041 0.0228 49 10.1041 0.0009 50 10.0953 0.3454 51 10.0953 0.0000 52 10.0949 0.0169 53 10.0798 0.6475 54 10.0452 0.6132 55 10.0008 0.5961 56 10.0007 0.0000 57 10.0007 0.0002 58 9.9981 0.0283 59 9.9977 0.0122 60 9.9976 0.0084 61 9.9976 0.0023
tunedProps = struct with fields:
TransitionProbabilities: [3×3 double]
ModelProbabilities: [3×1 double]
TrackingFilters: {3×1 cell}
Set the properties of the filter using the tuned properties.
setTunedProperties(filter,tunedProps);
Show the tuned properties.
filter.TransitionProbabilities
ans = 3×3
0.9980 0.0010 0.0010
0.1733 0.4133 0.4133
0.4995 0.0010 0.4995
filter.ModelProbabilities
ans = 3×1
0.9980
0.0010
0.0010
Tune Angle-Parameterized trackingGSF Filter
Generate a log of range-only detections and the truth data by using the helperGenerateDetectionAndTruth
function.
rng(2023) % For repeatable results
[detlog,truth] = helperGenerateDetectionAndTruth;
Create an angle-parameterized trackingGSF
object that has ten tracking filters by using the initapekf
function. Display the untuned process noise.
filter = initapekf(detlog{1}); disp(filter.TrackingFilters{1}.ProcessNoise)
1 0 0 0 1 0 0 0 1
Create a trackingFilterTuner
object. Specify the FilterInitializationFcn
property as "initapekf"
.
tuner = trackingFilterTuner(FilterInitializationFcn="initapekf");
Tune the filter and obtain the tuned properties.
tunedProps = tune(tuner,detlog,truth)
Iter RMSE Step Size 0 178.8318 1 177.5988 19.5448
tunedProps = struct with fields:
ProcessNoise: [3x3 double]
Set the properties of the filter using the tuned properties.
setTunedProperties(filter,tunedProps);
Show the tuned process noise.
disp(filter.TrackingFilters{10}.ProcessNoise)
100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 100.0000 201.0000
Helper Functions
function [detlog,truth] = helperGenerateDetectionAndTruth % Function to generate a detection log and the corresponding truth. % Define initial conditions. Assume a constant velcoity truth model. s0 = [10 10 0]'; v = [2 2 0]'; dt = 5; tFinal = 100; tspan = (0:dt:tFinal)'; numSteps = numel(tspan); % Specify measurement structure for range-only detection. mp = struct(Frame="Spherical", ... HasAzimuth=false, ... HasElevation=false, ... HasRange=true,... HasVelocity=false); detlog = cell(numSteps,1); sigma = 5; % Standar deviation of range measurements detlog{1} = objectDetection(tspan(1),norm(s0), ... MeasurementNoise=sigma^2,MeasurementParameters=mp); s = s0; Position = NaN(numSteps,3); Position(1,:) = s; Velocity = repmat(v',numSteps,1); % Iterate to obtain the truth and detections for ii = 2:numSteps s = s + v *dt; Position(ii,:) = s; measure = norm(s) + sqrt(sigma^2)*randn; detlog{ii} = objectDetection(tspan(ii),measure, ... MeasurementNoise=sigma^2,MeasurementParameters=mp); end truth = timetable(seconds(tspan),Position,Velocity); end
Algorithms
Objective Function for RMSE
The state estimate error between the truth state xk and the estimated state at time step k is defined as:
The square error between the truth state and the corresponding estimated state is defined as:
Furthermore, for measurement data from multiple Monte Carlo runs, define the averaged square error at time k as:
where M is the number of Monte Carlo runs. Then the objective function for RMSE-based filter tuning is:
where T is the number of time steps. If multiple truth tables are provided, the object function is averaged as:
where Nt is the number of truth tables and JjRMSE is the RMSE object function for the j-th truth table.
Objective Function for NEES
The state estimate error between the truth state xk and the estimated state at time step k is defined as:
The normalized estimate error squared (NEES) between the truth state and the corresponding estimated state is defined as:
where Pk is the state estimate error covariance matrix at time step k.
Furthermore, for measurement data from multiple Monte Carlo runs, define the averaged NEES at time k as:
where M is the number of Monte Carlo runs. Then the objective function for NEES-based filter tuning is:
where T is the number of time steps and nx is the dimension of the state vector x.
If multiple truth tables are provided, the object function is averaged as:
where Nt is the number of truth tables and JjNEES is the NEES object function for the j-th truth table.
For a consistent filter, a filter that is neither overconfident or underconfident, the overall NEES value should be equal or close to nx. As a result, the value of JNEES should be close to 0 and the optimization algorithm penalizes any JNEES value that is larger than 0.
Objective Function for NLL
The state estimate error between the truth state xk and the estimated state at time step k is defined as:
The negative log likelihood (NLL) for the estimate error after neglecting constant terms is:
where Pk is the state estimate error covariance matrix at time step k and |•| represents matrix determinant.
Furthermore, for measurement data from multiple Monte Carlo runs, define the averaged NLL at time k as:
where M is the number of Monte Carlo runs. Then the objective function for NLL-based filter tuning is:
where T is the number of time steps.
If multiple truth tables are provided, the object function is averaged as:
where Nt is the number of truth tables and JjNLL is the NLL object function for the j-th truth table.
References
[1] Chen, Zhaozhong, et al. “Time Dependence in Kalman Filter Tuning.” IEEE 24th International Conference on Information Fusion , IEEE, 2021, pp. 1–8.
Version History
Introduced in R2022bR2023b: Tune trackingGSF
object
You can tune a trackingGSF
object by specifying the FilterInitialization
property to initialize a
trackingGSF
object.
R2023b: Use negative log likelihood tuning cost
You can use the negative log likelihood (NLL) of the state estimate error as the tuning
cost by specifying the Cost
property of the trackingFilterTuner
object as "NLL"
. Use the NLL cost if you
want to optimize the filter for a tracker that uses this cost for data association.
R2023b: Use RMSE, NEES, and NLL tuning cost for tuning filter of custom motion model
When tuning a tracking filter using the trackingFilterTuner
object, you can use the built-in root mean square of the
error (RMSE), normalized estimate error squared (NEES), and negative log likelihood (NLL)
tuning costs even if the filter uses a custom motion model. Previously, you had to define a
custom cost when tuning a filter with a custom motion model.
R2023a: Tune trackingIMM
object
You can tune a trackingIMM
object by specifying the FilterInitialization
property to initialize a
trackingIMM
object.
R2023a: Tune tracking filter using new solver and display
You can specify the solver as "active-set"
, which does not require
additional license.
Additionally, you can control the display of tuning iteration by specifying the new
Display
property.
R2023a: Evaluate untuned cost and retrieve tuned parameters
Use the parameterCost
object function to obtain the cost before tuning. You can optionally specify tuned
parameters, detection log, and truth log for evaluating the tuning cost.
Use the tunedParameters
object function to retrieve the latest tuned parameters after the tuning process
terminates.
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