Hidden Markov model based localization using array antenna

We present a hidden Markov model based localization using array sensor. In this method, we use the eigenvector spanning signal subspace as a feature for location. The eigenvector does not depend on received signal strength (RSS) but on direction of arrival (DOA) of incident signals. As a result, the eigenvector is robust to fading and noise. In addition, the eigenvector is unique to the environment of propagation due to indoor reflection and diffraction of the electric wave. The conventional method based on fingerprinting does not take previous information into account. In this paper, we propose an algorithm that applies HMM to conventional fingerprinting of the eigenvector. This algorithm takes previous state of estimation into account by comparing the eigenvector obtained during observation with the one stored in the database. The database has the eigenvector obtained at each reference location according to setting in advance. In an indoor environment represented in a quantized grid, we decide the HMM transition probabilities denoting the possible moving range from previous estimation location. The most likely trajectory is calculated by means of the Viterbi algorithm. The results show that the localization accuracy is improved owing to the use of a possible moving range from the previous location.