Esempio n. 1
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        public void KalmanFilter()
        {
            mousePoints   = new List <PointF>();
            kalmanPoints  = new List <PointF>();
            kal           = new KalmanFilter(4, 2, 0);
            syntheticData = new SyntheticData();
            Matrix <float> state = new Matrix <float>(new float[]
            {
                0.0f, 0.0f, 0.0f, 0.0f
            });

            /*
             * kal.CorrectedState = state;
             * kal.TransitionMatrix = syntheticData.transitionMatrix;
             * kal.MeasurementNoiseCovariance = syntheticData.measurementNoise;
             * kal.ProcessNoiseCovariance = syntheticData.processNoise;
             * kal.ErrorCovariancePost = syntheticData.errorCovariancePost;
             * kal.MeasurementMatrix = syntheticData.measurementMatrix;
             */
            state.Mat.CopyTo(kal.StatePre);
            syntheticData.transitionMatrix.Mat.CopyTo(kal.TransitionMatrix);    // kal.TransitionMatrix = syntheticData.transitionMatrix;
            syntheticData.measurementNoise.Mat.CopyTo(kal.MeasurementNoiseCov); // kal.MeasurementNoiseCovariance = syntheticData.measurementNoise;
            syntheticData.processNoise.Mat.CopyTo(kal.ProcessNoiseCov);         // kal.ProcessNoiseCovariance = syntheticData.processNoise;
            syntheticData.errorCovariancePost.Mat.CopyTo(kal.ErrorCovPost);     // kal.ErrorCovariancePost = syntheticData.errorCovariancePost;
            syntheticData.measurementMatrix.Mat.CopyTo(kal.MeasurementMatrix);  // kal.MeasurementMatrix = syntheticData.measurementMatrix;
        }
Esempio n. 2
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 public void KalmanFilter()
 {
     mousePoints = new List<PointF>();
         kalmanPoints = new List<PointF>();
         kal = new Kalman(4, 2, 0);
         syntheticData = new SyntheticData();
         Matrix<float> state = new Matrix<float>(new float[]
         {
             0.0f, 0.0f, 0.0f, 0.0f
         });
         kal.CorrectedState = state;
         kal.TransitionMatrix = syntheticData.transitionMatrix;
         kal.MeasurementNoiseCovariance = syntheticData.measurementNoise;
         kal.ProcessNoiseCovariance = syntheticData.processNoise;
         kal.ErrorCovariancePost = syntheticData.errorCovariancePost;
         kal.MeasurementMatrix = syntheticData.measurementMatrix;
 }
Esempio n. 3
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    public KalmanTracker()
    {
        kalmanPoints  = new List <PointF>();
        kal           = new Kalman(4, 2, 0);
        syntheticData = new Kalman_Filter.SyntheticData();
        Matrix <float> state = new Matrix <float>(new float[]
        {
            0.0f, 0.0f, 0.0f, 0.0f
        });

        kal.CorrectedState             = state;
        kal.TransitionMatrix           = syntheticData.transitionMatrix;
        kal.MeasurementNoiseCovariance = syntheticData.measurementNoise;
        kal.ProcessNoiseCovariance     = syntheticData.processNoise;
        kal.ErrorCovariancePost        = syntheticData.errorCovariancePost;
        kal.MeasurementMatrix          = syntheticData.measurementMatrix;
    }