The measurement of fast changing temperature fluctuations is a challenging problem due to the inherent limited bandwidth of temperature sensors. This results in a measured signal that is a lagged and attenuated version of the input. Compensation can be performed provided an accurate, parameterised sensor model is available. However, to account for the in influence of the measurement environment and changing conditions such as gas velocity, the model must be estimated in-situ. The cross-relation method of blind deconvolution is one approach for in-situ characterisation of sensors. However, a drawback with the method is that it becomes positively biased and unstable at high noise levels. In this paper, the cross-relation method is cast in the discrete-time domain and a bias compensation approach is developed. It is shown that the proposed compensation scheme is robust and yields unbiased estimates with lower estimation variance than the uncompensated version. All results are verified using Monte-Carlo simulations.
|Publication status||Published - Jun 2016|
|Event||4th IFAC International Conference on Intelligent Control and Automation Sciences - Reims, France|
Duration: 01 Jun 2016 → 03 Jun 2016
Conference number: 4
|Conference||4th IFAC International Conference on Intelligent Control and Automation Sciences|
|Abbreviated title||ICONS 2016|
|Period||01/06/2016 → 03/06/2016|