Examples of KalmanFilter

                {   0,    0,   0, 1e-3 }
        });


        final ProcessModel pm = new DefaultProcessModel(A, B, Q, initialState, initialErrorCovariance);
        final MeasurementModel mm = new DefaultMeasurementModel(H, R);
        final KalmanFilter filter = new KalmanFilter(pm, mm);


        final List<Number> realX = new ArrayList<Number>();
        final List<Number> realY = new ArrayList<Number>();
        final List<Number> measuredX = new ArrayList<Number>();
        final List<Number> measuredY = new ArrayList<Number>();
        final List<Number> kalmanX = new ArrayList<Number>();
        final List<Number> kalmanY = new ArrayList<Number>();
        
        for (int i = 0; i < iterations; i++) {


            // get real location
            realX.add(cannonball.getX());
            realY.add(cannonball.getY());


            // get measured location
            final double mx = cannonball.getMeasuredX();
            final double my = cannonball.getMeasuredY();


            measuredX.add(mx);
            measuredY.add(my);


            // iterate the cannon simulation to the next timeslice.
            cannonball.step();


            final double[] state = filter.getStateEstimation();
            kalmanX.add(state[0]);
            kalmanY.add(state[2]);


            // update the kalman filter with the measurements
            filter.predict(controlVector);
            filter.correct(new double[] { mx, 0, my, 0 } );
        }


        chart.setXAxisTitle("Distance (m)");
        chart.setYAxisTitle("Height (m)");

        // the measurement covariance matrix -> put the "real" variance
        RealMatrix R = new Array2DRowRealMatrix(new double[] { measurementNoise * measurementNoise });


        final ProcessModel pm = new DefaultProcessModel(A, B, Q, x0, P0);
        final MeasurementModel mm = new DefaultMeasurementModel(H, R);
        final KalmanFilter filter = new KalmanFilter(pm, mm);


        final List<Number> xAxis = new ArrayList<Number>();
        final List<Number> realVoltageSeries = new ArrayList<Number>();
        final List<Number> measuredVoltageSeries = new ArrayList<Number>();
        final List<Number> kalmanVoltageSeries = new ArrayList<Number>();


        final List<Number> covSeries = new ArrayList<Number>();
        
        for (int i = 0; i < 300; i++) {
            xAxis.add(i);


            voltMeter.step();


            realVoltageSeries.add(voltMeter.getVoltage());


            // get the measured voltage from the volt meter
            final double measuredVoltage = voltMeter.getMeasuredVoltage();
            measuredVoltageSeries.add(measuredVoltage);


            kalmanVoltageSeries.add(filter.getStateEstimation()[0]);
            covSeries.add(filter.getErrorCovariance()[0][0]);


            filter.predict();
            filter.correct(new double[] { measuredVoltage });
        }


        chart1.setYAxisTitle("Voltage");
        chart1.setXAxisTitle("Iteration");

Examples of KalmanFilter

Examples of org.apache.commons.math3.filter.KalmanFilter

Examples of org.apache.commons.math3.filter.KalmanFilter