1 files changed, 202 insertions, 0 deletions
diff --git a/src/AltEst/filters.cpp b/src/AltEst/filters.cpp
new file mode 100644
index 0000000..7902065
--- /dev/null
+++ b/src/AltEst/filters.cpp
@@ -0,0 +1,202 @@
+/*
+   filters.cpp: Filter class implementations
+ */
+
+//#include <cmath>
+#include <stdlib.h> // XXX eventually use fabs() instead of abs() ?
+
+#include "filters.h"
+
+void KalmanFilter::getPredictionCovariance(float covariance[3][3], float previousState[3], float deltat)
+{
+    // required matrices for the operations
+    float sigma[3][3];
+    float identity[3][3];
+    identityMatrix3x3(identity);
+    float skewMatrix[3][3];
+    skew(skewMatrix, previousState);
+    float tmp[3][3];
+    // Compute the prediction covariance matrix
+    scaleMatrix3x3(sigma, pow(sigmaGyro, 2), identity);
+    matrixProduct3x3(tmp, skewMatrix, sigma);
+    matrixProduct3x3(covariance, tmp, skewMatrix);
+    scaleMatrix3x3(covariance, -pow(deltat, 2), covariance);
+}
+
+void KalmanFilter::getMeasurementCovariance(float covariance[3][3])
+{
+    // required matrices for the operations
+    float sigma[3][3];
+    float identity[3][3];
+    identityMatrix3x3(identity);
+    float norm;
+    // Compute measurement covariance
+    scaleMatrix3x3(sigma, pow(sigmaAccel, 2), identity);
+    vectorLength(& norm, previousAccelSensor);
+    scaleAndAccumulateMatrix3x3(sigma, (1.0/3.0)*pow(ca, 2)*norm, identity);
+    copyMatrix3x3(covariance, sigma);
+}
+
+void KalmanFilter::predictState(float predictedState[3], float gyro[3], float deltat)
+{
+    // helper matrices
+    float identity[3][3];
+    identityMatrix3x3(identity);
+    float skewFromGyro[3][3];
+    skew(skewFromGyro, gyro);
+    // Predict state
+    scaleAndAccumulateMatrix3x3(identity, -deltat, skewFromGyro);
+    matrixDotVector3x3(predictedState, identity, currentState);
+    normalizeVector(predictedState);
+}
+
+void KalmanFilter::predictErrorCovariance(float covariance[3][3], float gyro[3], float deltat)
+{
+    // required matrices
+    float Q[3][3];
+    float identity[3][3];
+    identityMatrix3x3(identity);
+    float skewFromGyro[3][3];
+    skew(skewFromGyro, gyro);
+    float tmp[3][3];
+    float tmpTransposed[3][3];
+    float tmp2[3][3];
+    // predict error covariance
+    getPredictionCovariance(Q, currentState, deltat);
+    scaleAndAccumulateMatrix3x3(identity, -deltat, skewFromGyro);
+    copyMatrix3x3(tmp, identity);
+    transposeMatrix3x3(tmpTransposed, tmp);
+    matrixProduct3x3(tmp2, tmp, currErrorCovariance);
+    matrixProduct3x3(covariance, tmp2, tmpTransposed);
+    scaleAndAccumulateMatrix3x3(covariance, 1.0, Q);
+}
+
+void KalmanFilter::updateGain(float gain[3][3], float errorCovariance[3][3])
+{
+    // required matrices
+    float R[3][3];
+    float HTransposed[3][3];
+    transposeMatrix3x3(HTransposed, H);
+    float tmp[3][3];
+    float tmp2[3][3];
+    float tmp2Inverse[3][3];
+    // update kalman gain
+    // P.dot(H.T).dot(inv(H.dot(P).dot(H.T) + R))
+    getMeasurementCovariance(R);
+    matrixProduct3x3(tmp, errorCovariance, HTransposed);
+    matrixProduct3x3(tmp2, H, tmp);
+    scaleAndAccumulateMatrix3x3(tmp2, 1.0, R);
+    invert3x3(tmp2Inverse, tmp2);
+    matrixProduct3x3(gain, tmp, tmp2Inverse);
+}
+
+void KalmanFilter::updateState(float updatedState[3], float predictedState[3], float gain[3][3], float accel[3])
+{
+    // required matrices
+    float tmp[3];
+    float tmp2[3];
+    float measurement[3];
+    scaleVector(tmp, ca, previousAccelSensor);
+    subtractVectors(measurement, accel, tmp);
+    // update state with measurement
+    // predicted_state + K.dot(measurement - H.dot(predicted_state))
+    matrixDotVector3x3(tmp, H, predictedState);
+    subtractVectors(tmp, measurement, tmp);
+    matrixDotVector3x3(tmp2, gain, tmp);
+    sumVectors(updatedState, predictedState, tmp2);
+    normalizeVector(updatedState);
+}
+
+void KalmanFilter::updateErrorCovariance(float covariance[3][3], float errorCovariance[3][3], float gain[3][3])
+{
+    // required matrices
+    float identity[3][3];
+    identityMatrix3x3(identity);
+    float tmp[3][3];
+    float tmp2[3][3];
+    // update error covariance with measurement
+    matrixProduct3x3(tmp, gain, H);
+    matrixProduct3x3(tmp2, tmp, errorCovariance);
+    scaleAndAccumulateMatrix3x3(identity, -1.0, tmp2);
+    copyMatrix3x3(covariance, tmp2);
+}
+
+
+KalmanFilter::KalmanFilter(float ca, float sigmaGyro, float sigmaAccel)
+{
+    this->ca = ca;
+    this->sigmaGyro = sigmaGyro;
+    this->sigmaAccel = sigmaAccel;
+}
+
+float KalmanFilter::estimate(float gyro[3], float accel[3], float deltat)
+{
+    float predictedState[3];
+    float updatedState[3];
+    float errorCovariance[3][3];
+    float updatedErrorCovariance[3][3];
+    float gain[3][3];
+    float accelSensor[3];
+    float tmp[3];
+    float accelEarth;
+    scaleVector(accel, 9.81, accel); // Scale accel readings since they are measured in gs
+    // perform estimation
+    // predictions
+    predictState(predictedState, gyro, deltat);
+    predictErrorCovariance(errorCovariance, gyro, deltat);
+    // updates
+    updateGain(gain, errorCovariance);
+    updateState(updatedState, predictedState, gain, accel);
+    updateErrorCovariance(updatedErrorCovariance, errorCovariance, gain);
+    // Store required values for next iteration
+    copyVector(currentState, updatedState);
+    copyMatrix3x3(currErrorCovariance, updatedErrorCovariance);
+    // return vertical acceleration estimate
+    scaleVector(tmp, 9.81, updatedState);
+    subtractVectors(accelSensor, accel, tmp);
+    copyVector(previousAccelSensor, accelSensor);
+    dotProductVectors(& accelEarth, accelSensor, updatedState);
+    return accelEarth;
+}
+
+
+float ComplementaryFilter::ApplyZUPT(float accel, float vel)
+{
+    // first update ZUPT array with latest estimation
+    ZUPT[ZUPTIdx] = accel;
+    // and move index to next slot
+    uint8_t nextIndex = (ZUPTIdx + 1) % ZUPT_SIZE;
+    ZUPTIdx = nextIndex;
+    // Apply Zero-velocity update
+    for (uint8_t k = 0; k < ZUPT_SIZE; ++k) {
+        if (abs(ZUPT[k]) > accelThreshold) return vel;
+    }
+    return 0.0;
+}
+
+
+ComplementaryFilter::ComplementaryFilter(float sigmaAccel, float sigmaBaro, float accelThreshold)
+{
+    // Compute the filter gain
+    gain[0] = sqrt(2 * sigmaAccel / sigmaBaro);
+    gain[1] = sigmaAccel / sigmaBaro;
+    // If acceleration is below the threshold the ZUPT counter
+    // will be increased
+    this->accelThreshold = accelThreshold;
+    // initialize zero-velocity update
+    ZUPTIdx = 0;
+    for (uint8_t k = 0; k < ZUPT_SIZE; ++k) {
+        ZUPT[k] = 0;
+    }
+}
+
+void ComplementaryFilter::estimate(float * velocity, float * altitude, float baroAltitude,
+        float pastAltitude, float pastVelocity, float accel, float deltat)
+{
+    // Apply complementary filter
+    *altitude = pastAltitude + deltat*(pastVelocity + (gain[0] + gain[1]*deltat/2)*(baroAltitude-pastAltitude))+
+        accel*pow(deltat, 2)/2;
+    *velocity = pastVelocity + deltat*(gain[1]*(baroAltitude-pastAltitude) + accel);
+    // Compute zero-velocity update
+    *velocity = ApplyZUPT(accel, *velocity);
+}