#include <otUKF.h>
Public Member Functions | |
| otUKF () | |
| void | create (otMatrix &XInit, otMatrix &P, otMatrix &Rv, otMatrix &Rn, bool(*bNonlinearUpdateX)(otMatrix &, otMatrix &, otMatrix &), bool(*bNonlinearUpdateY)(otMatrix &, otMatrix &, otMatrix &)) |
| void | vReset (otMatrix &XInit, otMatrix &P, otMatrix &Rv, otMatrix &Rn) |
| bool | bUpdate (otMatrix &Y, otMatrix &U) |
| otMatrix | GetX () |
| otMatrix | GetY () |
| otMatrix | GetP () |
| otMatrix | GetErr () |
| void | close () |
Protected Member Functions | |
| bool | bCalculateSigmaPoint () |
| bool | bUnscentedTransform (otMatrix &Out, otMatrix &OutSigma, otMatrix &P, otMatrix &DSig, bool(*_vFuncNonLinear)(otMatrix &xOut, otMatrix &xInp, otMatrix &U), otMatrix &InpSigma, otMatrix &InpVector, otMatrix &_CovNoise) |
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Create the class |
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Run once every sampling time |
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Initialization: P (k=0|k=0) = Identitas * covariant(P(k=0)), typically initialized with some big number. x(k=0|k=0) = Expected value of x at time-0 (i.e. x(k=0)), typically set to zero. Rv, Rn = Covariance matrices of process & measurement. As this implementation the noise as AWGN (and same value for every variable), this is set to Rv=diag(RvInit,...,RvInit) and Rn=diag(RnInit,...,RnInit). |
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Return Predicted state estimate |
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