Merge lp:~oontvoo/kalmanfilterimpl/runnable_class into lp:kalmanfilterimpl

[Vy Nguyen]

- Refactor KalmanFilter into an interface.
- Add a runnable class that could generate/read data from text file which can then be used (by Excel) to graph the data

Probably needs A LOT of fixings. The graph doesn't look that good.

Unmerged revisions

2. By vnguyen (<email address hidden>) on 2012-07-07

create a runnable class

=== added file '.bzrignore'
--- .bzrignore	1970-01-01 00:00:00 +0000
+++ .bzrignore	2012-07-07 07:30:27 +0000
@@ -0,0 +1,2 @@
+nbproject/**
+build/**

=== modified file 'src/kalmanfilter/RunFilter.java'
--- src/kalmanfilter/RunFilter.java	2012-07-03 18:46:12 +0000
+++ src/kalmanfilter/RunFilter.java	2012-07-07 07:30:27 +0000
@@ -26,6 +26,8 @@
 import java.util.Arrays;
 import java.util.Collections;
 import java.util.List;
+import kalmanfilter.impl.KalmanFilter1;
+import kalmanfilter.impl.KalmanFilter2;
 
 /**
  *
@@ -67,6 +69,16 @@
             this.ay = ay;
         }
         
+        public String toString()
+        {
+            return "(" + x + ", "
+                       + y + ", "
+                       + vx + ", "
+                       + vy + ", "
+                       + ax + ", "
+                       + ay + ", "
+                       + t + ")";   
+        }
         @Override
         public int compareTo(Point o)
         {
@@ -88,7 +100,7 @@
         List<Point> output = new ArrayList<Point>(inputs.size());
         
         //(Matrix X, Matrix P, Matrix Q, Matrix H, Matrix A, Matrix R)
-        KalmanFilter filter = new KalmanFilter(createX(inputs, 0),
+        KalmanFilter2 filter = new KalmanFilter2(createX(inputs, 0),
                                                getP0(),
                                                getQ(),
                                                getH(),

=== added file 'src/kalmanfilter/RunFilter2.java'
--- src/kalmanfilter/RunFilter2.java	1970-01-01 00:00:00 +0000
+++ src/kalmanfilter/RunFilter2.java	2012-07-07 07:30:27 +0000
@@ -0,0 +1,355 @@
+/**
+ * Implementation of the Kalman Filter
+ * Copyright (C) 2012  Vy Nguyen
+ *
+ * This library is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Library General Public
+ * License as published by the Free Software Foundation; either
+ * version 2 of the License, or (at your option) any later version.
+ * 
+ * This library is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ * Library General Public License for more details.
+ * 
+ * You should have received a copy of the GNU Library General Public
+ * License along with this library; if not, write to the
+ * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
+ * Boston, MA  02110-1301, USA.
+ */
+
+package kalmanfilter;
+
+import Jama.Matrix;
+import java.io.*;
+import java.util.*;
+import kalmanfilter.RunFilter.Point;
+import kalmanfilter.impl.KalmanFilter;
+import kalmanfilter.impl.KalmanFilter1;
+
+/**
+ *
+ * @author Vy Thao Nguyen
+ */
+public class RunFilter2
+{
+    private static List<Point> createIdealValues(int count, double dt,
+                                                 double ax0, double ay0,
+                                                 double vx0, double vy0,
+                                                 double x0, double y0)
+    {
+        // x  = x0 + v * dt
+        // v = v0 + a * dt
+        
+        List<Point> ret = new ArrayList<Point>();
+        ret.add(new Point(x0, y0, 0, vx0, vy0, ax0, ay0)); // first point
+        --count;
+        
+        double t = dt;
+        for (int n = 0; n < count; ++n)
+        {
+            double vx = vx0 + ax0 * t;
+            double x = x0 + vx0 * t + 0.5 * ay0 * t * t;
+            
+            double vy = vy0 + ay0 * t;
+            double y = y0 + vy0 * t + 0.5 * ay0 * t * t;
+            
+            ret.add(new Point(x, y, t, vx, vy, ax0, ay0));
+            t += dt;
+        }
+        return ret;
+    }
+    
+    private static void writeToFile(String fileName, List<Point> toWrite) throws IOException
+    {
+        StringBuilder bd = new StringBuilder();
+        
+        for (Point p : toWrite)
+            bd.append(p.x).append("     ").append(p.y).append(System.getProperty("line.separator"));
+        
+        FileWriter fstream = new FileWriter(fileName);
+        BufferedWriter out = new BufferedWriter(fstream);
+        out.write(bd.toString());
+        out.close();
+    }
+    
+    private static double parseArg(String args[])
+    {
+        if (args.length != 1)
+        {
+            System.out.println("default noise: " + DEFAULT_NOISE);
+            return DEFAULT_NOISE;
+        }
+        return Double.parseDouble(args[0]);
+    }
+    
+    private static List<Point> readSensorData(double dt, double ax)
+    {
+        try
+        {
+            Scanner input = new Scanner(new File(SENSOR));
+            double t = 0;
+            List<Point> ret = new ArrayList<Point>();
+            
+            while(input.hasNext())
+            {
+                double x = Double.parseDouble(input.next());
+                double y = Double.parseDouble(input.next());
+                ret.add(new Point(x, y, t, 0, 0, ax, ax));
+                t += dt;
+            }
+            return ret;
+        }
+        catch (FileNotFoundException ex)
+        {
+            System.out.println("File not found: " + SENSOR);
+            System.exit(2);
+        }
+        return null;
+    }
+    
+    public static void main (String args[]) throws IOException
+    {
+        double ax = 2.5;
+        double ay = 1.256;
+        double dt = 0.8;
+
+        System.out.println("computing the ideal values");
+        List<Point> idealData = createIdealValues(9, dt,
+                                          ax, ay ,
+                                          0, 0,
+                                          0, 0);
+        writeToFile(IDEAL, idealData);
+
+        System.out.println("reading sensor data");
+        List<Point> sensor = readSensorData(dt, ax);
+        double noise = parseArg(args);
+
+        System.out.println("filtering sensor data");
+        writeToFile(CORRECTED, filter(sensor, noise));
+    }
+     /**
+     * 
+     * @param inputs
+     * @param sensorNoise
+     * @return
+     */
+    public static List<Point> filter(List<Point> inputs, 
+                                               double sensorNoise)
+    {
+        // sort the points in time order
+        Collections.sort(inputs);
+
+        // compute the velocity
+        List<Point> updated = computeVAndA(inputs);
+  
+        // get the standard deviation of acceleration
+        double sd = getSd(updated);
+
+        List<Point> output = new ArrayList<Point>(inputs.size());
+        output.add(inputs.get(0)); // first point is the same
+
+        Matrix P0 = getP0();
+        Matrix H = getH();
+        Matrix R = createR(sensorNoise);
+ 
+        KalmanFilter filterX = new KalmanFilter1(createXx(updated.get(0)),
+                                                 P0,
+                                                 H,
+                                                 R
+                                                 );
+             
+        KalmanFilter filterY = new KalmanFilter1(createXy(updated.get(0)),
+                                                 P0,
+                                                 H,
+                                                 R
+                                                 );
+        for (int n = 1; n < updated.size(); ++n)
+        {
+            Point cur = updated.get(n);
+            Matrix A = createA(cur);
+            Matrix G = createG(cur);
+            Matrix Gx = G.times(cur.ax);
+            Matrix Gy = G.times(cur.ay);
+            Matrix Q = getQ(G, sd);
+            
+            Matrix correctedX = filterX.timeUpdate(A, Q, Gx).messurementUpdate(createXx(cur));
+            Matrix correctedY = filterY.timeUpdate(A, Q, Gy).messurementUpdate(createXy(cur));
+
+            output.add(decodeXY(correctedX, correctedY, inputs.get(n)));
+        }
+
+        return output;
+    }
+    private static double getSd(List<Point> p)
+    {
+        // for now, the accelerations are const
+        return 0;
+    }
+
+    /**
+     * TODO: correctly decode the state matrix into a point.
+     * (that is, retrieve a and v)
+     * 
+     * @param X the corrected state matrix
+     * @return the corrected value of (x,y,t)
+     */
+    private static Point decodeXY(Matrix X, Matrix Y, Point input)
+    {
+        return new Point(X.get(0, 0) / 10, // x
+                         Y.get(0, 0) / 10, // y
+                         X.get(1, 0) / 10, // vx
+                         Y.get(1, 0) / 10, // vy
+                         input.ax, // unchanged
+                         input.ay, // unchanged
+                         input.t // unchanged
+                );
+    }
+    
+    /**
+     * TODO: correctly encode the point (velocity, acceleration, etc.) into a matrix.
+     * 
+     * Vector x = {x, vx , ax, ay}
+     * 
+     * @param points 
+     * @param currentIndex index of the point the filter is currently looking at
+     * @return the input vector X
+     */
+    private static Matrix createXx(Point current)
+    {
+        Matrix x = new Matrix(2, 1);        
+
+        x.set(0, 0, current.x);
+        x.set(1, 0, current.vx);
+
+        return x;
+    }
+
+    private static Matrix createXy(Point cur)
+    {
+        Matrix y = new Matrix(2, 1);
+        
+        y.set(0, 0, cur.y);
+        y.set(1, 0, cur.vy);
+        
+        return y;
+    }
+    /**
+     * Compute the instant velocity and acceleration of each point
+     * <b>This method makes the following assumptions:</b>
+     *      1. Acceleration of a Point object is constant and was properly set in the constructor.
+     *      2. The velocity of the first point is either 0 or has already been correctly calculated.
+     *    
+     * TODO: take variable acceleration into account.
+     * 
+     * @param points
+     * @return list of point with correct v and a 
+     */
+    private static List<Point> computeVAndA(List<Point> points)
+    {
+        int size = points.size();
+        double vx_0 = points.get(0).vx;
+        double vy_0 = points.get(0).vy;
+        double ax_0 = points.get(0).ax;
+        double ay_0 = points.get(0).ay;   
+        
+        List<Point> result = new ArrayList<Point> (size);
+        result.add(points.get(0));
+        
+        for (int i = 1; i < size; ++i)
+        {
+            double dt = points.get(i).t - points.get(0).t;
+            double vx = vx_0 + ax_0 * dt;
+            double vy = vy_0 + ay_0 * dt;
+            
+            result.add(new Point(points.get(i).x,
+                                 points.get(i).y,
+                                 dt,
+                                 vx,
+                                 vy,
+                                 points.get(i).ax,
+                                 points.get(i).ay
+                        ));
+        }
+        
+        return result;
+    }
+    
+    /**
+     * TODO:
+     * Same as createX(...)'s
+     * 
+     * @param inputs
+     * @param currentIndex
+     * @return the state matrix A based on the the current and past position
+     */
+    private static Matrix createA(Point current)
+    {
+        Matrix A = Matrix.identity(2, 2);
+        A.set(0, 1, current.t);
+        return A;
+    }
+    
+    /**
+     * 
+     * @param error the sensor's error
+     * @return the measurement noise covariance matrix
+     */
+    private static Matrix createR (double error)
+    {
+        Matrix R = new Matrix(1,1);
+        R.set(0, 0, error);
+        return R;
+    }
+    /**
+     * 
+     * @return the initial P matrix, which contains all zeroes 
+     */
+    private static Matrix getP0()
+    {
+        double m2x2[][] = new double[VARS_COUNT][VARS_COUNT];
+        
+        for (int n = 0; n < VARS_COUNT; ++n)
+            Arrays.fill(m2x2[n], 0);
+
+        return Matrix.constructWithCopy(m2x2);
+    }
+
+    /**
+     * TODO:
+     * correctly determine this
+     * 
+     * @return the process noise covariance matrix
+     */
+    private static Matrix getQ(Matrix G, double sd)
+    {
+        return G.times(G.transpose()).times(sd * sd);
+    }
+
+    /**
+     * 
+     * @return the [1 by n] observation matrix: [1 0 0 0 .... 0 ] 
+     */
+    private static Matrix getH()
+    {
+        Matrix H = new Matrix(1,2);
+        H.set(0, 0, 1);
+        return H;
+    }
+    
+    private static Matrix createG(Point p)
+    {
+        Matrix ret = new Matrix(2, 1);
+        ret.set(0, 0, p.t * p.t / 2);
+        ret.set(1, 0, p.t);
+        
+        return ret;
+    }
+
+    private static final String IDEAL = "C:\\Users\\NhatPhan\\Desktop\\Choe\\Sensor Research\\data\\ideal.txt";
+    private static final String SENSOR = "C:\\Users\\NhatPhan\\Desktop\\Choe\\Sensor Research\\data\\sensor.txt";
+    private static final String CORRECTED = "C:\\Users\\NhatPhan\\Desktop\\Choe\\Sensor Research\\data\\corrected.txt";
+
+    private static final double DEFAULT_NOISE = 0.5;
+    private static final int VARS_COUNT = 2;
+}

=== modified file 'src/kalmanfilter/impl/KalmanFilter.java'
--- src/kalmanfilter/impl/KalmanFilter.java	2012-07-03 18:46:12 +0000
+++ src/kalmanfilter/impl/KalmanFilter.java	2012-07-07 07:30:27 +0000
@@ -1,85 +1,33 @@
-/**
- * Implementation of the Kalman Filter
- * Copyright (C) 2012  Vy Nguyen
- *
- * This library is free software; you can redistribute it and/or
- * modify it under the terms of the GNU Library General Public
- * License as published by the Free Software Foundation; either
- * version 2 of the License, or (at your option) any later version.
- * 
- * This library is distributed in the hope that it will be useful,
- * but WITHOUT ANY WARRANTY; without even the implied warranty of
- * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
- * Library General Public License for more details.
- * 
- * You should have received a copy of the GNU Library General Public
- * License along with this library; if not, write to the
- * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
- * Boston, MA  02110-1301, USA.
- */
-package kalmanfilter.impl;
-  
-import Jama.Matrix;
-
-/**
- * Implements the Kalman Filter using Jama's Matrix API
- * 
- * @author Vy Thao Nguyen
- */
-public class KalmanFilter 
-{
-    // variable states
-    private Matrix X; // input vector
-    private Matrix P; // state covariance matrix
-    private Matrix Q; // process noise covariance matrix
-
-    // const states
-    private final Matrix H; // observation matrix
-    private final Matrix I; // the identity matrix 
-    private final Matrix A; // state matrix
-    private final Matrix R; // measurement noise covariance matrix
-    private final Matrix B; // control input matrix
-    private final Matrix u; 
-    
-    public KalmanFilter(Matrix X, Matrix P, Matrix Q, Matrix H, Matrix A, Matrix R, Matrix B, Matrix u)
-    {
-        this.X = X;
-        this.P = P;
-        this.Q = Q;
-        this.H = H;
-        this.A = A;
-        this.R = R;
-        this.B = B;
-        this.u = u;
-        
-        I = Matrix.identity(P.getRowDimension(), P.getColumnDimension());
-    }
-
-    public KalmanFilter timeUpdate()
-    {
-        X = A.times(X).plus(B.times(u));
-        P = A.times(P).times(A.transpose()).plus(Q);
-        return this; // for easy chaining
-    }
-
-    /**
-     * 
-     * @param Xk: the new measurement (sensor reading)
-     * @return 
-     */
-    public Matrix messurementUpdate(Matrix Xk)
-    {
-        // compute kalman gain
-        //Kk+1 = PkHT(H PkHT + R)-1
-        Matrix K = P.times(H.transpose()).times((H.times(P).times(H.trace()).plus(R)).inverse());
-        
-        // update x
-        // xk+1 = xk + K(zk+1 − Hxk )
-        X = X.plus(K.times((Xk.times(H)).minus(H.times(X))));
-
-        // update P
-        P = (I.minus(K.times(H))).times(P);
-        
-        return X;
-    }
-}
+/**
+ * Implementation of the Kalman Filter
+ * Copyright (C) 2012  Vy Nguyen
+ *
+ * This library is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Library General Public
+ * License as published by the Free Software Foundation; either
+ * version 2 of the License, or (at your option) any later version.
+ * 
+ * This library is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ * Library General Public License for more details.
+ * 
+ * You should have received a copy of the GNU Library General Public
+ * License along with this library; if not, write to the
+ * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
+ * Boston, MA  02110-1301, USA.
+ */
+
+package kalmanfilter.impl;
+
+import Jama.Matrix;
+
+/**
+ *
+ * @author Vy Thao Nguyen
+ */
+public interface KalmanFilter
+{
+    public KalmanFilter timeUpdate(Matrix A, Matrix Q, Matrix Ga);
+    public Matrix messurementUpdate(Matrix Xk);
+}

=== added file 'src/kalmanfilter/impl/KalmanFilter1.java'
--- src/kalmanfilter/impl/KalmanFilter1.java	1970-01-01 00:00:00 +0000
+++ src/kalmanfilter/impl/KalmanFilter1.java	2012-07-07 07:30:27 +0000
@@ -0,0 +1,84 @@
+/**
+ * Implementation of the Kalman Filter
+ * Copyright (C) 2012  Vy Nguyen
+ *
+ * This library is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Library General Public
+ * License as published by the Free Software Foundation; either
+ * version 2 of the License, or (at your option) any later version.
+ * 
+ * This library is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ * Library General Public License for more details.
+ * 
+ * You should have received a copy of the GNU Library General Public
+ * License along with this library; if not, write to the
+ * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
+ * Boston, MA  02110-1301, USA.
+ */
+package kalmanfilter.impl;
+  
+import Jama.Matrix;
+
+/**
+ * Implements the original Kalman Filter using Jama's Matrix API
+ * 
+ * *original* being all states are encoded into one state matrix.
+ * If you have difficulty encoding all states into one, you might want to consider
+ * using KalmanFilter2.java
+ * 
+ * @author Vy Thao Nguyen
+ */
+public class KalmanFilter1 implements KalmanFilter
+{
+    // variable states
+    private Matrix X; // input vector
+    private Matrix P; // state covariance matrix
+
+    // const states
+    private final Matrix H; // observation matrix
+    private final Matrix I; // the identity matrix 
+    private final Matrix R; // measurement noise covariance matrix 
+    
+    public KalmanFilter1(Matrix X, Matrix P, Matrix H, Matrix R)
+    {
+        this.X = X;
+        this.P = P;
+        this.H = H;
+        this.R = R;
+
+        I = Matrix.identity(P.getRowDimension(), P.getColumnDimension());
+    }
+
+    @Override
+    public KalmanFilter1 timeUpdate(Matrix A, Matrix Q, Matrix Ga)
+    {
+      //  Matrix Ax = A.times(X);
+        X = (A.times(X)).plus(Ga);
+        P = A.times(P).times(A.transpose()).plus(Q);
+        return this; // for easy chaining
+    }
+
+    /**
+     * 
+     * @param Xk: the new measurement (sensor reading)
+     * @return 
+     */
+    @Override
+    public Matrix messurementUpdate(Matrix Xk)
+    {
+        // compute kalman gain
+        //Kk+1 = PkHT(H PkHT + R)-1
+        Matrix K = (P.times(H.transpose())).times((H.times(P).times(H.transpose()).plus(R)).inverse());
+        
+        // update x
+        // xk+1 = xk + K(zk+1 − Hxk )
+        X = X.plus(K.times((H.times(Xk)).minus(H.times(X))));
+
+        // update P
+        P = (I.minus(K.times(H))).times(P);
+        
+        return X;
+    }
+}

=== added file 'src/kalmanfilter/impl/KalmanFilter2.java'
--- src/kalmanfilter/impl/KalmanFilter2.java	1970-01-01 00:00:00 +0000
+++ src/kalmanfilter/impl/KalmanFilter2.java	2012-07-07 07:30:27 +0000
@@ -0,0 +1,101 @@
+/**
+ * Implementation of the Kalman Filter
+ * Copyright (C) 2012  Vy Nguyen
+ *
+ * This library is free software; you can redistribute it and/or
+ * modify it under the terms of the GNU Library General Public
+ * License as published by the Free Software Foundation; either
+ * version 2 of the License, or (at your option) any later version.
+ * 
+ * This library is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
+ * Library General Public License for more details.
+ * 
+ * You should have received a copy of the GNU Library General Public
+ * License along with this library; if not, write to the
+ * Free Software Foundation, Inc., 51 Franklin St, Fifth Floor,
+ * Boston, MA  02110-1301, USA.
+ */
+
+package kalmanfilter.impl;
+
+import Jama.Matrix;
+
+/**
+ * Implements the filter with different 'groups' of states encoded in different
+ * matrices.
+ * 
+ * For eg., 
+ * For a system with the following states:
+ *      x: horizontal position
+ *      y: vertical position
+ *      u: horizontal velocity
+ *      v: vertical velocity
+ *      
+ * divided into two groups: X1 = {x, u}, X2 = {y, v}
+ * 
+ * TODO: make the number of 'groups' variable. Currently, this is only
+ * capable of handling a fixed 2 groups
+ * 
+ * @author Vy Thao Nguyen
+ */
+public class KalmanFilter2 //implements KalmanFilter
+{
+    // variable states
+    private Matrix X; // input vector
+    private Matrix P; // state covariance matrix
+    private Matrix Q; // process noise covariance matrix
+
+    // const states
+    private final Matrix H; // observation matrix
+    private final Matrix I; // the identity matrix 
+    private final Matrix A; // state matrix
+    private final Matrix R; // measurement noise covariance matrix
+    private final Matrix B; // control input matrix
+    private final Matrix u; 
+    
+    public KalmanFilter2(Matrix X, Matrix P, Matrix Q, Matrix H, Matrix A, Matrix R, Matrix B, Matrix u)
+    {
+        this.X = X;
+        this.P = P;
+        this.Q = Q;
+        this.H = H;
+        this.A = A;
+        this.R = R;
+        this.B = B;
+        this.u = u;
+        
+        I = Matrix.identity(P.getRowDimension(), P.getColumnDimension());
+    }
+
+   // @Override
+    public KalmanFilter2 timeUpdate()
+    {
+        X = A.times(X).plus(B.times(u));
+        P = A.times(P).times(A.transpose()).plus(Q);
+        return this; // for easy chaining
+    }
+
+    /**
+     * 
+     * @param Xk: the new measurement (sensor reading)
+     * @return 
+     */
+   // @Override
+    public Matrix messurementUpdate(Matrix Xk)
+    {
+        // compute kalman gain
+        //Kk+1 = PkHT(H PkHT + R)-1
+        Matrix K = P.times(H.transpose()).times((H.times(P).times(H.trace()).plus(R)).inverse());
+        
+        // update x
+        // xk+1 = xk + K(zk+1 − Hxk )
+        X = X.plus(K.times((Xk.times(H)).minus(H.times(X))));
+
+        // update P
+        P = (I.minus(K.times(H))).times(P);
+        
+        return X;
+    }
+}