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1. Introduction
In recent years, a new concept called hybrid positioning systems (HPSs) is more and more popular in navigation and location-based services research community [1]. HPSs are systems for finding the location of a mobile device by using several different positioning technologies in indoor environments. Mostly, GPS is one major component of such systems, aided by others such as tower signals, wireless internet signals, Indoor Messaging System (IMES), bluetooth sensors, or other local positioning systems [1, 2]. So far many different hybrid positioning systems are being explored and gradually applied in services from Google Maps for Mobile, Combain Mobile, SkyHook, openBmap, Navizon, PlaceEngine, Xtify, and so forth.
Obviously, HPS will be well developed in urban location-based service areas because of the huge commercial market. So studies are rapidly increasing and more practically viable than ever [1, 3–6]. But in these literatures, researchers paid more attention to the architecture of HPS, or how to choose and integrate different sensors or signals reliably [1, 5, 6]. Actually, for getting reasonable accuracy and precision, position estimator is one of the key factors in the hybrid positioning systems, which will directly affect the quality of system performance. As we all know, for an HPS use in real environments, there is hardly enough measuring information for a unique position because of the reflection conditions of the environment, which will influence the WLAN or the other radio signals. On the other hand, the measurement functions are nonlinear functions of the motion state, so we have to think about the estimation accuracy the variations because the sensors’ signals may significantly change depending on the motion of vehicle (or person) and slight differences of the positions at which the signals are measured [7]. For example, due to the influence of the...