94 lines
3.6 KiB
C++
94 lines
3.6 KiB
C++
/* Copyright (C) 2012 Kristian Lauszus, TKJ Electronics. All rights reserved.
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This software may be distributed and modified under the terms of the GNU
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General Public License version 2 (GPL2) as published by the Free Software
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Foundation and appearing in the file GPL2.TXT included in the packaging of
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this file. Please note that GPL2 Section 2[b] requires that all works based
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on this software must also be made publicly available under the terms of
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the GPL2 ("Copyleft").
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Contact information
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-------------------
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Kristian Lauszus, TKJ Electronics
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Web : http://www.tkjelectronics.com
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e-mail : kristianl@tkjelectronics.com
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*/
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#include "Kalman.h"
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Kalman::Kalman() {
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/* We will set the variables like so, these can also be tuned by the user */
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Q_angle = 0.001f;
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Q_bias = 0.003f;
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R_measure = 0.03f;
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angle = 0.0f; // Reset the angle
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bias = 0.0f; // Reset bias
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P[0][0] = 0.0f; // Since we assume that the bias is 0 and we know the starting angle (use setAngle), the error covariance matrix is set like so - see: http://en.wikipedia.org/wiki/Kalman_filter#Example_application.2C_technical
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P[0][1] = 0.0f;
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P[1][0] = 0.0f;
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P[1][1] = 0.0f;
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};
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// The angle should be in degrees and the rate should be in degrees per second and the delta time in seconds
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float Kalman::getAngle(float newAngle, float newRate, float dt) {
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// KasBot V2 - Kalman filter module - http://www.x-firm.com/?page_id=145
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// Modified by Kristian Lauszus
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// See my blog post for more information: http://blog.tkjelectronics.dk/2012/09/a-practical-approach-to-kalman-filter-and-how-to-implement-it
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// Discrete Kalman filter time update equations - Time Update ("Predict")
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// Update xhat - Project the state ahead
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/* Step 1 */
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rate = newRate - bias;
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angle += dt * rate;
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// Update estimation error covariance - Project the error covariance ahead
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/* Step 2 */
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P[0][0] += dt * (dt*P[1][1] - P[0][1] - P[1][0] + Q_angle);
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P[0][1] -= dt * P[1][1];
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P[1][0] -= dt * P[1][1];
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P[1][1] += Q_bias * dt;
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// Discrete Kalman filter measurement update equations - Measurement Update ("Correct")
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// Calculate Kalman gain - Compute the Kalman gain
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/* Step 4 */
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float S = P[0][0] + R_measure; // Estimate error
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/* Step 5 */
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float K[2]; // Kalman gain - This is a 2x1 vector
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K[0] = P[0][0] / S;
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K[1] = P[1][0] / S;
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// Calculate angle and bias - Update estimate with measurement zk (newAngle)
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/* Step 3 */
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float y = newAngle - angle; // Angle difference
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/* Step 6 */
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angle += K[0] * y;
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bias += K[1] * y;
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// Calculate estimation error covariance - Update the error covariance
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/* Step 7 */
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float P00_temp = P[0][0];
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float P01_temp = P[0][1];
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P[0][0] -= K[0] * P00_temp;
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P[0][1] -= K[0] * P01_temp;
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P[1][0] -= K[1] * P00_temp;
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P[1][1] -= K[1] * P01_temp;
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return angle;
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};
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void Kalman::setAngle(float angle) { this->angle = angle; }; // Used to set angle, this should be set as the starting angle
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float Kalman::getRate() { return this->rate; }; // Return the unbiased rate
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/* These are used to tune the Kalman filter */
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void Kalman::setQangle(float Q_angle) { this->Q_angle = Q_angle; };
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void Kalman::setQbias(float Q_bias) { this->Q_bias = Q_bias; };
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void Kalman::setRmeasure(float R_measure) { this->R_measure = R_measure; };
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float Kalman::getQangle() { return this->Q_angle; };
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float Kalman::getQbias() { return this->Q_bias; };
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float Kalman::getRmeasure() { return this->R_measure; };
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