首页资源分类嵌入式处理器STM8 > 姿态融合的一阶互补滤波、二阶互补滤波、卡尔曼滤波核心程序

姿态融合的一阶互补滤波、二阶互补滤波、卡尔曼滤波核心程序

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姿态融合的一阶互补滤波、二阶互补滤波、卡尔曼滤波核心程序

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//一阶互补 // a=tau / (tau + loop time) // newAngle = angle measured with atan2 using the accelerometer //加速度传感器输出值 // newRate = angle measured using the gyro // looptime = loop time in millis() float tau = 0.075; float a = 0.0; float Complementary(float newAngle, float newRate, int looptime) { float dtC = float(looptime) / 1000.0; a = tau / (tau + dtC); x_angleC = a * (x_angleC + newRate * dtC) + (1 - a) * (newAngle); return x_angleC; } //二阶互补 // newAngle = angle measured with atan2 using the accelerometer // newRate = angle measured using the gyro // looptime = loop time in millis() float Complementary2(float newAngle, float newRate, int looptime) { float k = 10; float dtc2 = float(looptime) / 1000.0; x1 = (newAngle - x_angle2C) * k * k; y1 = dtc2 * x1 + y1; x2 = y1 + (newAngle - x_angle2C) * 2 * k + newRate; x_angle2C = dtc2 * x2 + x_angle2C; return x_angle2C; } //Here too we just have to set the k and magically we get the angle. 卡尔曼滤波 // KasBot V1 - Kalman filter module float Q_angle = 0.01; //0.001 float Q_gyro = 0.0003; //0.003 float R_angle = 0.01; //0.03 float x_bias = 0; float P_00 = 0, P_01 = 0, P_10 = 0, P_11 = 0; float y, S; float K_0, K_1; // newAngle = angle measured with atan2 using the accelerometer // newRate = angle measured using the gyro // looptime = loop time in millis() float kalmanCalculate(float newAngle, float newRate, int looptime) { float dt = float(looptime) / 1000; x_angle += dt * (newRate - x_bias); P_00 += - dt * (P_10 + P_01) + Q_angle * dt; P_01 += - dt * P_11; P_10 += - dt * P_11; P_11 += + Q_gyro * dt; y = newAngle - x_angle; S = P_00 + R_angle; K_0 = P_00 / S; K_1 = P_10 / S; x_angle += K_0 * y; x_bias += K_1 * y; P_00 -= K_0 * P_00; P_01 -= K_0 * P_01; P_10 -= K_1 * P_00; P_11 -= K_1 * P_01; return x_angle; } //To get the answer, you have to set 3 parameters: Q_angle, R_angle, R_gyro. //详细卡尔曼滤波 /* -*- indent-tabs-mode:T; c-basic-offset:8; tab-width:8; -*- vi: set ts=8: * $Id: tilt.c,v 1.1 2003/07/09 18:23:29 john Exp $ * * 1 dimensional tilt sensor using a dual axis accelerometer * and single axis angular rate gyro. The two sensors are fused * via a two state Kalman filter, with one state being the angle * and the other state being the gyro bias. * * Gyro bias is automatically tracked by the filter. This seems * like magic. * * Please note that there are lots of comments in the functions and * in blocks before the functions. Kalman filtering is an already complex * subject, made even more so by extensive hand optimizations to the C code * that implements the filter. I've tried to make an effort of explaining * the optimizations, but feel free to send mail to the mailing list, * autopilot-devel@lists.sf.net, with questions about this code. * * * (c) 2003 Trammell Hudson * ************* * * This file is part of the autopilot onboard code package. * * Autopilot is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * Autopilot 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 General Public License for more details. * * You should have received a copy of the GNU General Public License * along with Autopilot; if not, write to the Free Software * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA * */ #include /* * Our update rate. This is how often our state is updated with * gyro rate measurements. For now, we do it every time an * 8 bit counter running at CLK/1024 expires. You will have to * change this value if you update at a different rate. */ static const float dt = ( 1024.0 * 256.0 ) / 8000000.0; /* * Our covariance matrix. This is updated at every time step to * determine how well the sensors are tracking the actual state. */ static float P[2][2] = { { 1, 0 }, { 0, 1 }, }; /* * Our two states, the angle and the gyro bias. As a byproduct of computing * the angle, we also have an unbiased angular rate available. These are * read-only to the user of the module. */ float angle; float q_bias; float rate; /* * R represents the measurement covariance noise. In this case, * it is a 1x1 matrix that says that we expect 0.3 rad jitter * from the accelerometer. */ static const float R_angle = 0.3; /* * Q is a 2x2 matrix that represents the process covariance noise. * In this case, it indicates how much we trust the acceleromter * relative to the gyros. */ static const float Q_angle = 0.001; static const float Q_gyro = 0.003; /* * state_update is called every dt with a biased gyro measurement * by the user of the module. It updates the current angle and * rate estimate. * * The pitch gyro measurement should be scaled into real units, but * does not need any bias removal. The filter will track the bias. * * Our state vector is: * * X = [ angle, gyro_bias ] * * It runs the state estimation forward via the state functions: * * Xdot = [ angle_dot, gyro_bias_dot ] * * angle_dot = gyro - gyro_bias * gyro_bias_dot = 0 * * And updates the covariance matrix via the function: * * Pdot = A*P + P*A' + Q * * A is the Jacobian of Xdot with respect to the states: * * A = [ d(angle_dot)/d(angle) d(angle_dot)/d(gyro_bias) ] * [ d(gyro_bias_dot)/d(angle) d(gyro_bias_dot)/d(gyro_bias) ] * * = [ 0 -1 ] * [ 0 0 ] * * Due to the small CPU available on the microcontroller, we've * hand optimized the C code to only compute the terms that are * explicitly non-zero, as well as expanded out the matrix math * to be done in as few steps as possible. This does make it harder * to read, debug and extend, but also allows us to do this with * very little CPU time. */ void state_update( const float q_m /* Pitch gyro measurement */) { /* Unbias our gyro */ const float q = q_m - q_bias; /* * Compute the derivative of the covariance matrix * * Pdot = A*P + P*A' + Q * * We've hand computed the expansion of A = [ 0 -1, 0 0 ] multiplied * by P and P multiplied by A' = [ 0 0, -1, 0 ]. This is then added * to the diagonal elements of Q, which are Q_angle and Q_gyro. */ const float Pdot[2 * 2] = { Q_angle - P[0][1] - P[1][0], /* 0,0 */ - P[1][1], /* 0,1 */ - P[1][1], /* 1,0 */ Q_gyro /* 1,1 */ }; /* Store our unbias gyro estimate */ rate = q; /* * Update our angle estimate * angle += angle_dot * dt * += (gyro - gyro_bias) * dt * += q * dt */ angle += q * dt; /* Update the covariance matrix */ P[0][0] += Pdot[0] * dt; P[0][1] += Pdot[1] * dt; P[1][0] += Pdot[2] * dt; P[1][1] += Pdot[3] * dt; } /* * kalman_update is called by a user of the module when a new * accelerometer measurement is available. ax_m and az_m do not * need to be scaled into actual units, but must be zeroed and have * the same scale. * * This does not need to be called every time step, but can be if * the accelerometer data are available at the same rate as the * rate gyro measurement. * * For a two-axis accelerometer mounted perpendicular to the rotation * axis, we can compute the angle for the full 360 degree rotation * with no linearization errors by using the arctangent of the two * readings. * * As commented in state_update, the math here is simplified to * make it possible to execute on a small microcontroller with no * floating point unit. It will be hard to read the actual code and * see what is happening, which is why there is this extensive * comment block. * * The C matrix is a 1x2 (measurements x states) matrix that * is the Jacobian matrix of the measurement value with respect * to the states. In this case, C is: * * C = [ d(angle_m)/d(angle) d(angle_m)/d(gyro_bias) ] * = [ 1 0 ] * * because the angle measurement directly corresponds to the angle * estimate and the angle measurement has no relation to the gyro * bias. */ void kalman_update( const float ax_m, /* X acceleration */ const float az_m /* Z acceleration */ ) { /* Compute our measured angle and the error in our estimate */ const float angle_m = atan2( -az_m, ax_m ); const float angle_err = angle_m - angle; /* * C_0 shows how the state measurement directly relates to * the state estimate. * * The C_1 shows that the state measurement does not relate * to the gyro bias estimate. We don't actually use this, so * we comment it out. */ const float C_0 = 1; /* const float C_1 = 0; */ /* * PCt<2,1> = P<2,2> * C'<2,1>, which we use twice. This makes * it worthwhile to precompute and store the two values. * Note that C[0,1] = C_1 is zero, so we do not compute that * term. */ const float PCt_0 = C_0 * P[0][0]; /* + C_1 * P[0][1] = 0 */ const float PCt_1 = C_0 * P[1][0]; /* + C_1 * P[1][1] = 0 */ /* * Compute the error estimate. From the Kalman filter paper: * * E = C P C' + R * * Dimensionally, * * E<1,1> = C<1,2> P<2,2> C'<2,1> + R<1,1> * * Again, note that C_1 is zero, so we do not compute the term. */ const float E = R_angle + C_0 * PCt_0 /* + C_1 * PCt_1 = 0 */ ; /* * Compute the Kalman filter gains. From the Kalman paper: * * K = P C' inv(E) * * Dimensionally: * * K<2,1> = P<2,2> C'<2,1> inv(E)<1,1> * * Luckilly, E is <1,1>, so the inverse of E is just 1/E. */ const float K_0 = PCt_0 / E; const float K_1 = PCt_1 / E; /* * Update covariance matrix. Again, from the Kalman filter paper: * * P = P - K C P * * Dimensionally: * * P<2,2> -= K<2,1> C<1,2> P<2,2> * * We first compute t<1,2> = C P. Note that: * * t[0,0] = C[0,0] * P[0,0] + C[0,1] * P[1,0] * * But, since C_1 is zero, we have: * * t[0,0] = C[0,0] * P[0,0] = PCt[0,0] * * This saves us a floating point multiply. */ const float t_0 = PCt_0; /* C_0 * P[0][0] + C_1 * P[1][0] */ const float t_1 = C_0 * P[0][1]; /* + C_1 * P[1][1] = 0 */ P[0][0] -= K_0 * t_0; P[0][1] -= K_0 * t_1; P[1][0] -= K_1 * t_0; P[1][1] -= K_1 * t_1; /* * Update our state estimate. Again, from the Kalman paper: * * X += K * err * * And, dimensionally, * * X<2> = X<2> + K<2,1> * err<1,1> * * err is a measurement of the difference in the measured state * and the estimate state. In our case, it is just the difference * between the two accelerometer measured angle and our estimated * angle. */ angle += K_0 * angle_err; q_bias += K_1 * angle_err; }

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