1 files changed, 0 insertions, 202 deletions
diff --git a/src/AltEst/filters.cpp b/src/AltEst/filters.cpp
deleted file mode 100644
index 7902065..0000000
--- a/src/AltEst/filters.cpp
+++ /dev/null
@@ -1,202 +0,0 @@
-/*
-   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);
-}