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46 lines
1.5 KiB
46 lines
1.5 KiB
#pragma once |
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/* |
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* This class implements a simple 1-D Extended Kalman Filter to estimate the Relative body frame postion of the lading target and its relative velocity |
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* position and velocity of the target is predicted using delta velocity |
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* The predictions are corrected periodically using the landing target sensor(or camera) |
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*/ |
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class PosVelEKF { |
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public: |
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// Initialize the covariance and state matrix |
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// This is called when the landing target is located for the first time or it was lost, then relocated |
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void init(float pos, float posVar, float vel, float velVar); |
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// This functions runs the Prediction Step of the EKF |
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// This is called at 400 hz |
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void predict(float dt, float dVel, float dVelNoise); |
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// fuse the new sensor measurement into the EKF calculations |
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// This is called whenever we have a new measurement available |
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void fusePos(float pos, float posVar); |
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// Get the EKF state position |
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float getPos() const { return _state[0]; } |
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// Get the EKF state velocity |
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float getVel() const { return _state[1]; } |
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// get the normalized innovation squared |
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float getPosNIS(float pos, float posVar); |
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private: |
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// stored covariance and state matrix |
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/* |
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_state[0] = position |
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_state[1] = velocity |
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*/ |
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float _state[2]; |
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/* |
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Covariance Matrix is ALWAYS symmetric, therefore the following matrix is assumed: |
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P = Covariance Matrix = |_cov[0] _cov[1]| |
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|_cov[1] _cov[2]| |
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*/ |
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float _cov[3]; |
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};
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