initcvkf

Create constant-velocity linear Kalman filter from detection report

Since R2021a

Syntax

``filter = initcvkf(detection)``

Description

example

````filter = initcvkf(detection)` creates and initializes a constant-velocity linear Kalman `filter` from information contained in a `detection` report. For more information about the linear Kalman filter, see `trackingKF`.The function initializes a constant velocity state with the same convention as `constvel` and `cvmeas`, [x vx y vy z vz].```

Examples

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Create and initialize a 2-D linear Kalman filter object from an initial detection report.

Create the detection report from an initial 2-D measurement, (10,20), of the object position.

```detection = objectDetection(0,[10;20],'MeasurementNoise',[1 0.2; 0.2 2], ... 'SensorIndex',1,'ObjectClassID',1,'ObjectAttributes',{'Yellow Car',5});```

Create the new track from the detection report.

`filter = initcvkf(detection)`
```filter = trackingKF with properties: State: [4x1 double] StateCovariance: [4x4 double] MotionModel: '2D Constant Velocity' ProcessNoise: [2x2 double] MeasurementModel: [2x4 double] MeasurementNoise: [2x2 double] MaxNumOOSMSteps: 0 EnableSmoothing: 0 ```

Show the state.

`filter.State`
```ans = 4×1 10 0 20 0 ```

Show the state transition model.

`filter.StateTransitionModel`
```ans = 4×4 1 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1 ```

Create and initialize a 3-D linear Kalman filter object from an initial detection report.

Create the detection report from an initial 3-D measurement, (10,20,−5), of the object position.

```detection = objectDetection(0,[10;20;-5],'MeasurementNoise',eye(3), ... 'SensorIndex', 1,'ObjectClassID',1,'ObjectAttributes',{'Green Car', 5});```

Create the new filter from the detection report and display its properties.

`filter = initcvkf(detection)`
```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 ```

Show the state.

`filter.State`
```ans = 6×1 10 0 20 0 -5 0 ```

Show the state transition model.

`filter.StateTransitionModel`
```ans = 6×6 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 ```

Input Arguments

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Detection report, specified as an `objectDetection` object.

Example: `detection = objectDetection(0,[1;4.5;3],'MeasurementNoise', [1.0 0 0; 0 2.0 0; 0 0 1.5])`

Output Arguments

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Linear Kalman filter, returned as a `trackingKF` object.

Algorithms

• The function computes the process noise matrix assuming a one-second time step and an acceleration standard deviation of 1 m/s2.

• You can use this function as the `FilterInitializationFcn` property of a `radarTracker` object.

Version History

Introduced in R2021a