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65 lines
1.3 KiB
C
65 lines
1.3 KiB
C
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#include "kalman.h"
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const double Q_angle = 0.01;
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const double Q_bias = 0.003;
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const double R_measure = 0.03;
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static double angle;
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static double bias;
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static double rate;
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double P[2][2];
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double K[2];
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double y;
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double S;
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void Kalman_Init()
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{
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bias = 0;
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rate = 0;
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angle = 180;
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P[0][0] = 0;
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P[0][1] = 0;
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P[1][0] = 0;
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P[1][1] = 0;
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}
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// <20><><EFBFBD><EFBFBD> <20> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>\<5C><><EFBFBD>, <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD> <20> <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>.
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double Kalman_GetAngle(double newAngle, double newRate, double dt)
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{
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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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S = P[0][0] + R_measure;
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// Step 5
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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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y = newAngle - angle;
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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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P[0][0] -= K[0] * P[0][0];
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P[0][1] -= K[0] * P[0][1];
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P[1][0] -= K[1] * P[0][0];
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P[1][1] -= K[1] * P[0][1];
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return angle;
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}
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