A configurable fingerprint-based hidden-Markov model for tracking in variable channel conditions

A novel scheme for mobile subscriber positioning is proposed based on the hidden-Markov model (HMM) and the cell-ID maximum-likelihood database correlation method also known as fingerprinting. Using a simulated channel environment, based on the Clearwire deployment of WiMAX base stations in San Jose, CA, we show that matching the right configuration of the model to the deployment environment can realize significant gains in performance. The proposed scheme balances the scalability inherent in hidden-Markov-based motion models deployed in large areas of interest against the existing channel conditions and computational capability. By utilizing a simulated channel this paper demonstrates the effect of base station deployment and shadowing on the fingerprint-based HMM motion model. Further, the benefits gained through scaling the HMM are explored.