001 /* 002 * Licensed to the Apache Software Foundation (ASF) under one or more 003 * contributor license agreements. See the NOTICE file distributed with 004 * this work for additional information regarding copyright ownership. 005 * The ASF licenses this file to You under the Apache License, Version 2.0 006 * (the "License"); you may not use this file except in compliance with 007 * the License. You may obtain a copy of the License at 008 * 009 * http://www.apache.org/licenses/LICENSE-2.0 010 * 011 * Unless required by applicable law or agreed to in writing, software 012 * distributed under the License is distributed on an "AS IS" BASIS, 013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 014 * See the License for the specific language governing permissions and 015 * limitations under the License. 016 */ 017 package org.apache.commons.math3.filter; 018 019 import org.apache.commons.math3.linear.RealMatrix; 020 import org.apache.commons.math3.linear.RealVector; 021 022 /** 023 * Defines the process dynamics model for the use with a {@link KalmanFilter}. 024 * 025 * @since 3.0 026 * @version $Id: ProcessModel.java 1416643 2012-12-03 19:37:14Z tn $ 027 */ 028 public interface ProcessModel { 029 /** 030 * Returns the state transition matrix. 031 * 032 * @return the state transition matrix 033 */ 034 RealMatrix getStateTransitionMatrix(); 035 036 /** 037 * Returns the control matrix. 038 * 039 * @return the control matrix 040 */ 041 RealMatrix getControlMatrix(); 042 043 /** 044 * Returns the process noise matrix. This method is called by the {@link KalmanFilter} every 045 * prediction step, so implementations of this interface may return a modified process noise 046 * depending on the current iteration step. 047 * 048 * @return the process noise matrix 049 * @see KalmanFilter#predict() 050 * @see KalmanFilter#predict(double[]) 051 * @see KalmanFilter#predict(RealVector) 052 */ 053 RealMatrix getProcessNoise(); 054 055 /** 056 * Returns the initial state estimation vector. 057 * <p> 058 * <b>Note:</b> if the return value is zero, the Kalman filter will initialize the 059 * state estimation with a zero vector. 060 * 061 * @return the initial state estimation vector 062 */ 063 RealVector getInitialStateEstimate(); 064 065 /** 066 * Returns the initial error covariance matrix. 067 * <p> 068 * <b>Note:</b> if the return value is zero, the Kalman filter will initialize the 069 * error covariance with the process noise matrix. 070 * 071 * @return the initial error covariance matrix 072 */ 073 RealMatrix getInitialErrorCovariance(); 074 }