Example usage for org.apache.commons.math3.filter KalmanFilter KalmanFilter

Introduction

In this page you can find the example usage for org.apache.commons.math3.filter KalmanFilter KalmanFilter.

Prototype

public KalmanFilter(final ProcessModel process, final MeasurementModel measurement)
        throws NullArgumentException, NonSquareMatrixException, DimensionMismatchException,
        MatrixDimensionMismatchException 

Source Link

Document

Creates a new Kalman filter with the given process and measurement models.

Usage

From source file:net.liuxuan.temp.filterTest.java

public static void main(String[] args) {
    double constantVoltage = 10d;
    double measurementNoise = 0.1d;
    double processNoise = 1e-5d;

    // A = [ 1 ]/* ww w . j ava2 s  .co  m*/
    RealMatrix A = new Array2DRowRealMatrix(new double[] { 1d });
    // B = null
    RealMatrix B = null;
    // H = [ 1 ]
    RealMatrix H = new Array2DRowRealMatrix(new double[] { 1d });
    // x = [ 10 ]
    RealVector x = new ArrayRealVector(new double[] { constantVoltage });
    // Q = [ 1e-5 ]
    RealMatrix Q = new Array2DRowRealMatrix(new double[] { processNoise });
    // P = [ 1 ]
    RealMatrix P0 = new Array2DRowRealMatrix(new double[] { 1d });
    // R = [ 0.1 ]
    RealMatrix R = new Array2DRowRealMatrix(new double[] { measurementNoise });

    ProcessModel pm = new DefaultProcessModel(A, B, Q, x, P0);
    MeasurementModel mm = new DefaultMeasurementModel(H, R);
    KalmanFilter filter = new KalmanFilter(pm, mm);

    // process and measurement noise vectors
    RealVector pNoise = new ArrayRealVector(1);
    RealVector mNoise = new ArrayRealVector(1);

    RandomGenerator rand = new JDKRandomGenerator();
    // iterate 60 steps
    for (int i = 0; i < 60; i++) {
        filter.predict();

        // simulate the process
        //            pNoise.setEntry(0, processNoise * rand.nextGaussian());
        pNoise.setEntry(0, Math.sin(Math.PI * 2 * i / 6));
        //            System.out.println("============");
        //            System.out.println(Math.sin(Math.PI*2*i/6));

        // x = A * x + p_noise
        x = A.operate(x).add(pNoise);
        // simulate the measurement
        //            mNoise.setEntry(0, measurementNoise * rand.nextGaussian());
        mNoise.setEntry(0, 0);

        // z = H * x + m_noise
        RealVector z = H.operate(x).add(mNoise);
        filter.correct(z);

        double voltage = filter.getStateEstimation()[0];
        System.out.println(voltage);

        // state estimate shouldn't be larger than the measurement noise
        double diff = Math.abs(x.getEntry(0) - filter.getStateEstimation()[0]);
        System.out.println("diff = " + diff);

    }
}

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From source file:net.liuxuan.device.w3330.KalmanFilterHolder.java

private void initfilter() {
    pm = new DefaultProcessModel(A, B, Q, x, P0);
    mm = new DefaultMeasurementModel(H, R);
    filter = new KalmanFilter(pm, mm);
    //        z = H.operate(x).add(mNoise);
}

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From source file:eu.qualimaster.monitoring.profiling.predictors.Kalman.java

/**
 * Update models and the Kalman-Filter after a change in the parameters / matrices.
 *//*from  w  w w  .  j a  v  a 2s .  c o m*/
private void reinitialize() {
    pm = new DefaultProcessModel(mA, mB, mQ, xVector, mP);
    mm = new DefaultMeasurementModel(mH, mR);
    filter = new KalmanFilter(pm, mm);
}

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