A method is presented for augmenting an extended
Kalman filter with an adaptive element. The resulting estimator
provides robustness to parameter uncertainty and unmodeled
dynamics.
The standard optimum Kalman filter demands complete
knowledge of the system parameters, the input forcing functions, and
the noise statistics. Several adaptive methods have already been devised
to obtain the unknown information using the measurements and
the filter residuals.
A new blind adaptive multiuser detection scheme based on a hybrid of Kalman filter and
subspace estimation is proposed. It is shown that the detector can be expressed as an anchored
signal in the signal subspace and the coefficients can be estimated by the Kalman filter using only
the signature waveform and the timing of the desired user.
