Robust Inference for State-Space Models with Skewed Measurement Noise
Research output: Contribution to journal › Article › Scientific › peer-review
|Number of pages||5|
|Journal||IEEE Signal Processing Letters|
|Publication status||Published - 1 Nov 2015|
|Publication type||A1 Journal article-refereed|
Filtering and smoothing algorithms for linear discrete-time state-space models with skewed and heavy-tailed measurement noise are presented. The algorithms use a variational Bayes approximation of the posterior distribution of models that have normal prior and skew-t-distributed measurement noise. The proposed filter and smoother are compared with conventional low-complexity alternatives in a simulated pseudorange positioning scenario. In the simulations the proposed methods achieve better accuracy than the alternative methods, the computational complexity of the filter being roughly 5 to 10 times that of the Kalman filter.
- Kalman filter, robust filtering, RTS smoother, skew t, skewness, t-distribution, variational Bayes