Online tests of Kalman filter consistency
Research output: Contribution to journal › Article › Scientific › peer-review
|Journal||International Journal of Adaptive Control and Signal Processing|
|Publication status||Published - 2016|
|Publication type||A1 Journal article-refereed|
The normalised innovation squared (NIS) test, which is used to assess whether a Kalman filter's noise assumptions are consistent with realised measurements, can be applied online with real data, and does not require future data, repeated experiments or knowledge of the true state. In this work, it is shown that the NIS test is equivalent to three other model criticism procedures, which are as follows: (i) it can be derived as a Bayesian p-test for the prior predictive distribution; (ii) as a nested-model parameter significance test; and (iii) from a recently-proposed filter residual test. A new NIS-like test corresponding to a posterior predictive Bayesian p-test is presented.
ASJC Scopus subject areas
- Kalman filter, Model consistency, Normalised innovations squared, Predictive distribution