  Information about the strength of gas sources in buildings has a number of
applications in the area of building automation and control, including
temperature and ventilation control, fire detection and security systems. Here,
we consider the problem of estimating the strength of a gas source in an
enclosure when some of the parameters of the gas transport process are unknown.
Traditionally, these problems are either solved by the Maximum-Likelihood (ML)
method which is accurate but computationally intense, or by Recursive Least
Squares (RLS, also Kalman) filtering which is simpler but less accurate. In
this paper, we suggest a different statistical estimation procedure based on
the concept of Method of Moments. We outline techniques that make this
procedure computationally efficient and amenable for recursive implementation.
We provide a comparative analysis of our proposed method based on experimental
results as well as Monte-Carlo simulations. When used with the building control
systems, these algorithms can estimate the gaseous strength in a room both
quickly and accurately, and can potentially provide improved indoor air quality
in an efficient manner.
