Wireless local area network (WLAN) location is the perfect complement to global positioning system (GPS) receivers for providing users with location information. WLAN operates in many areas in which GPS receivers cannot establish a lock, such as in indoor environments and metropolitan (obstruction by tall buildings) areas. There are many references to WLAN location in scientific literature. Most of this literature concentrates on signal-strength-based approaches. The advantage of signal strength approaches is that they are easy to implement, usually only requiring software modifications. In this paper, we discuss a novel time of arrival (TOA) approach that Intel research has developed. Although this approach typically requires minor silicon or firmware modifications to implement, it can deliver significant performance improvements over signal-strength-based measurements, as shown in this paper. Currently, Intel is introducing this method as an approach to add fine-accuracy location into the IEEE WLAN standard, 802.11 v.

An overview is provided of the several components of a research effort aimed at the development of a theory of multiresolution stochastic modeling and associated techniques for optimal multiscale statistical signal and image processing. A natural framework for developing such a theory is the study of stochastic processes indexed by nodes on lattices or trees in which different depths in the tree or lattice correspond to different spatial scales in representing a signal or image. In particular, it is shown how the wavelet transform directly suggests such a modeling paradigm. This perspective then leads directly to the investigation of several classes of dynamic models and related notions of multiscale stationarity in which scale plays the role of a time-like variable. The investigation of models on homogeneous trees is emphasized. The framework examined here allows for consideration, in a very natural way, of the fusion of data from sensors with differing resolutions. Also, thanks to the fact that wavelet transforms do an excellent job of 'compressing' large classes of covariance kernels, it is seen that these modeling paradigms appear to have promise in a far broader context than one might expect.>

The authors introduce a class of multiscale stochastic processes which are Markov in scale and which are characterized by dynamic state models evolving in scale. The models for these processes are motivated by the theory of multiscale representations and the wavelet transform. The authors formulate an optimal estimation problem based on these models, which has potential applications to sensor fusion problems where there exist data from sensors of differing resolution, and provide an efficient algorithm based on the wavelet transform. They give examples applying these models to first-order Gauss-Markov processes.>