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23 lines
641 B
23 lines
641 B
import numpy as np
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class KF1D:
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# this EKF assumes constant covariance matrix, so calculations are much simpler
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# the Kalman gain also needs to be precomputed using the control module
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def __init__(self, x0, A, C, K):
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self.x = x0
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self.A = A
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self.C = np.atleast_2d(C)
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self.K = K
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self.A_K = self.A - np.dot(self.K, self.C)
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# K matrix needs to be pre-computed as follow:
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# import control
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# (x, l, K) = control.dare(np.transpose(self.A), np.transpose(self.C), Q, R)
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# self.K = np.transpose(K)
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def update(self, meas):
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self.x = np.dot(self.A_K, self.x) + np.dot(self.K, meas)
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return self.x
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