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.