MATH-485

New "filter" package. Initial implementation of Kalman filter provided
by Thomas Neidhart.


git-svn-id: https://svn.apache.org/repos/asf/commons/proper/math/trunk@1134779 13f79535-47bb-0310-9956-ffa450edef68

src/main/java/org/apache/commons/math/filter/DefaultMeasurementModel.java

@@ -0,0 +1,74 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.commons.math.filter;
+
+import org.apache.commons.math.linear.Array2DRowRealMatrix;
+import org.apache.commons.math.linear.RealMatrix;
+
+/**
+ * Default implementation of a {@link MeasurementModel} for the use with a
+ * {@link KalmanFilter}.
+ *
+ * @version $Id$
+ */
+public class DefaultMeasurementModel implements MeasurementModel {
+
+    private RealMatrix measurementMatrix;
+    private RealMatrix measurementNoise;
+
+    /**
+     * Create a new {@link MeasurementModel}, taking double arrays as input
+     * parameters for the respective measurement matrix and noise.
+     *
+     * @param measurementMatrix
+     *            the measurement matrix
+     * @param measurementNoise
+     *            the measurement noise matrix
+     */
+    public DefaultMeasurementModel(final double[][] measurementMatrix,
+            final double[][] measurementNoise) {
+        this(new Array2DRowRealMatrix(measurementMatrix),
+                new Array2DRowRealMatrix(measurementNoise));
+    }
+
+    /**
+     * Create a new {@link MeasurementModel}, taking {@link RealMatrix} objects
+     * as input parameters for the respective measurement matrix and noise.
+     *
+     * @param measurementMatrix
+     * @param measurementNoise
+     */
+    public DefaultMeasurementModel(final RealMatrix measurementMatrix,
+            final RealMatrix measurementNoise) {
+        this.measurementMatrix = measurementMatrix;
+        this.measurementNoise = measurementNoise;
+    }
+
+    /**
+     * {@inheritDoc}
+     */
+    public RealMatrix getMeasurementMatrix() {
+        return measurementMatrix;
+    }
+
+    /**
+     * {@inheritDoc}
+     */
+    public RealMatrix getMeasurementNoise() {
+        return measurementNoise;
+    }
+}

src/main/java/org/apache/commons/math/filter/DefaultProcessModel.java

@@ -0,0 +1,143 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.commons.math.filter;
+
+import org.apache.commons.math.linear.Array2DRowRealMatrix;
+import org.apache.commons.math.linear.ArrayRealVector;
+import org.apache.commons.math.linear.RealMatrix;
+import org.apache.commons.math.linear.RealVector;
+
+/**
+ * Default implementation of a {@link ProcessModel} for the use with a
+ * {@link KalmanFilter}.
+ *
+ * @version $Id$
+ */
+public class DefaultProcessModel implements ProcessModel {
+
+    private RealMatrix stateTransitionMatrix;
+    private RealMatrix controlMatrix;
+    private RealMatrix processNoise;
+    private RealVector initialStateEstimate;
+    private RealMatrix initialErrorCovariance;
+
+    /**
+     * Create a new {@link ProcessModel}, taking double arrays as input
+     * parameters.
+     *
+     * @param stateTransitionMatrix
+     *            the state transition matrix
+     * @param controlMatrix
+     *            the control matrix
+     * @param processNoise
+     *            the process noise matrix
+     * @param initialStateEstimate
+     *            the initial state estimate vector
+     * @param initialErrorCovariance
+     *            the initial error covariance matrix
+     */
+    public DefaultProcessModel(final double[][] stateTransitionMatrix,
+            final double[][] controlMatrix, final double[][] processNoise,
+            final double[] initialStateEstimate,
+            final double[][] initialErrorCovariance) {
+        this(new Array2DRowRealMatrix(stateTransitionMatrix),
+                new Array2DRowRealMatrix(controlMatrix),
+                new Array2DRowRealMatrix(processNoise), new ArrayRealVector(
+                        initialStateEstimate), new Array2DRowRealMatrix(
+                        initialErrorCovariance));
+    }
+
+    /**
+     * Create a new {@link ProcessModel}, taking double arrays as input
+     * parameters. The initial state estimate and error covariance are omitted
+     * and will be initialized by the {@link KalmanFilter} to default values.
+     *
+     * @param stateTransitionMatrix
+     *            the state transition matrix
+     * @param controlMatrix
+     *            the control matrix
+     * @param processNoise
+     *            the process noise matrix
+     */
+    public DefaultProcessModel(final double[][] stateTransitionMatrix,
+            final double[][] controlMatrix, final double[][] processNoise) {
+        this(new Array2DRowRealMatrix(stateTransitionMatrix),
+                new Array2DRowRealMatrix(controlMatrix),
+                new Array2DRowRealMatrix(processNoise), null, null);
+    }
+
+    /**
+     * Create a new {@link ProcessModel}, taking double arrays as input
+     * parameters.
+     *
+     * @param stateTransitionMatrix
+     *            the state transition matrix
+     * @param controlMatrix
+     *            the control matrix
+     * @param processNoise
+     *            the process noise matrix
+     * @param initialStateEstimate
+     *            the initial state estimate vector
+     * @param initialErrorCovariance
+     *            the initial error covariance matrix
+     */
+    public DefaultProcessModel(final RealMatrix stateTransitionMatrix,
+            final RealMatrix controlMatrix, final RealMatrix processNoise,
+            final RealVector initialStateEstimate,
+            final RealMatrix initialErrorCovariance) {
+        this.stateTransitionMatrix = stateTransitionMatrix;
+        this.controlMatrix = controlMatrix;
+        this.processNoise = processNoise;
+        this.initialStateEstimate = initialStateEstimate;
+        this.initialErrorCovariance = initialErrorCovariance;
+    }
+
+    /**
+     * {@inheritDoc}
+     */
+    public RealMatrix getStateTransitionMatrix() {
+        return stateTransitionMatrix;
+    }
+
+    /**
+     * {@inheritDoc}
+     */
+    public RealMatrix getControlMatrix() {
+        return controlMatrix;
+    }
+
+    /**
+     * {@inheritDoc}
+     */
+    public RealMatrix getProcessNoise() {
+        return processNoise;
+    }
+
+    /**
+     * {@inheritDoc}
+     */
+    public RealVector getInitialStateEstimate() {
+        return initialStateEstimate;
+    }
+
+    /**
+     * {@inheritDoc}
+     */
+    public RealMatrix getInitialErrorCovariance() {
+        return initialErrorCovariance;
+    }
+}