1 files changed, 73 insertions, 0 deletions
diff --git a/src/main/java/org/apache/commons/math3/filter/ProcessModel.java b/src/main/java/org/apache/commons/math3/filter/ProcessModel.java
new file mode 100644
index 0000000..5e0b427
--- /dev/null
+++ b/src/main/java/org/apache/commons/math3/filter/ProcessModel.java
@@ -0,0 +1,73 @@
+/*
+ * 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.math3.filter;
+
+import org.apache.commons.math3.linear.RealMatrix;
+import org.apache.commons.math3.linear.RealVector;
+
+/**
+ * Defines the process dynamics model for the use with a {@link KalmanFilter}.
+ *
+ * @since 3.0
+ */
+public interface ProcessModel {
+    /**
+     * Returns the state transition matrix.
+     *
+     * @return the state transition matrix
+     */
+    RealMatrix getStateTransitionMatrix();
+
+    /**
+     * Returns the control matrix.
+     *
+     * @return the control matrix
+     */
+    RealMatrix getControlMatrix();
+
+    /**
+     * Returns the process noise matrix. This method is called by the {@link KalmanFilter} every
+     * prediction step, so implementations of this interface may return a modified process noise
+     * depending on the current iteration step.
+     *
+     * @return the process noise matrix
+     * @see KalmanFilter#predict()
+     * @see KalmanFilter#predict(double[])
+     * @see KalmanFilter#predict(RealVector)
+     */
+    RealMatrix getProcessNoise();
+
+    /**
+     * Returns the initial state estimation vector.
+     *
+     * <p><b>Note:</b> if the return value is zero, the Kalman filter will initialize the state
+     * estimation with a zero vector.
+     *
+     * @return the initial state estimation vector
+     */
+    RealVector getInitialStateEstimate();
+
+    /**
+     * Returns the initial error covariance matrix.
+     *
+     * <p><b>Note:</b> if the return value is zero, the Kalman filter will initialize the error
+     * covariance with the process noise matrix.
+     *
+     * @return the initial error covariance matrix
+     */
+    RealMatrix getInitialErrorCovariance();
+}