Cramér-Rao Lower Bound for Linear Filtering with t-Distributed Measurement Noise
Piche, Robert (2016)
Piche, Robert
IEEE
2016
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201609164512
https://urn.fi/URN:NBN:fi:tty-201609164512
Kuvaus
Peer reviewed
Tiivistelmä
The Cramér-Rao lower bound (CRLB) on the achievable mean square error (MSE) can be used to evaluate approximate estimation algorithms. For linear filtering problems with non-Gaussian noises, the CRLB can be easily computed using the Kalman filter state covariance recursion with the Fisher information in place of the noise covariance term. This work studies a linear filtering problem with t-distributed measurement noise. It is found that for a t distribution with heavy tails, the CRLB significantly underestimates the optimal MSE, the Kalman filter has significantly larger MSE, and a computationally light variational-Bayes algorithm achieves nearly optimal MSE.
Kokoelmat
- TUNICRIS-julkaisut [16929]