Design of an unbiased finite impulse response filter for a smart sensor to estimate state of CO concentration

Carbon monoxide (CO) is produced by incomplete combustion of organic materials. Dense urban areas present high concentration of CO which might be harmful for human life. Large and expensive industrial analyzers, placed at specific locations, are used to monitor this gas concentration, creating a poor density monitoring network. To increase granularity of the measurement grid, low cost smart sensors are located over the zone of interest. The reliability of such devises is increased by using unbiased, robust, predictive, and desirably blind signal processing algorithms. In this paper, we propose a novel blind iterative unbiased finite impulse response (UFIR) filtering algorithm, which meets the above requirements. Experimental verification is given for both the missing and complete measurement data of the CO concentration. High accuracy and precision of the predictive UFIR estimator are demonstrated in a short time and on a long time scale.