Nonlinear estimation for arrays of chemical sensors

Reliable detection of hazardous materials is a fundamental requirement of any national security program. Such materials can take a wide range of forms including metals, radioisotopes, volatile organic compounds, and biological contaminants. In particular, detection of hazardous materials in highly challenging conditions - such as in cluttered ambient environments, where complex collections of analytes are present, and with sensors lacking specificity for the analytes of interest - is an important part of a robust security infrastructure. Sophisticated single sensor systems provide good specificity for a limited set of analytes but often have cumbersome hardware and environmental requirements. On the other hand, simple, broadly responsive sensors are easily fabricated and efficiently deployed, but such sensors individually have neither the specificity nor the selectivity to address analyte differentiation in challenging environments. However, arrays of broadly responsive sensors can provide much of the sensitivity and selectivity of sophisticated sensors but without the substantial hardware overhead. Unfortunately, arrays of simple sensors are not without their challenges - the selectivity of such arrays can only be realized if the data is first distilled using highly advanced signal processing algorithms. In this paper we will demonstrate how the use of powerful estimation algorithms, based on those commonly used within the target tracking community, can be extended to the chemical detection arena. Herein our focus is on algorithms that not only provide accurate estimates of the mixture of analytes in a sample, but also provide robust measures of ambiguity, such as covariances.