Investigations of self-contained sensors for personal navigation

Satellite-based radionavigation systems provide accurate and reliable positioning and navigation whenever the satellite-to-receiver path is free from obstacles. In personal navigation this condition cannot always be guaranteed. Many applications require accurate position solutions in downtown areas and indoor. Due to the very low transmission power and signal delays caused by the obstacles, the performance of satellite navigation systems decreases in these environments. Conversely, self-contained sensors can provide navigation information in any environment. Algorithms for obtaining changes in position using accelerometer and gyro data are well known. In addition, the advances in Micro Electro Mechanical System (MEMS) technology enable the use of low-cost, low-power and inexpensive sensors in personal navigation devices. One of the main problems in transforming the known inertial navigation technology to hand-held devices is that present-day MEMS sensors cannot provide similar accuracy as sensors used in marine or aerospace navigation. In this thesis, this fact is acknowledged, and the aim is to construct a framework for algorithms that can take advantage of the sensor information but are much less sensitive to large measurement errors than the traditional inertial navigation algorithms. In personal navigation the orientation of the unit cannot be restricted as in the case of vehicular navigation, and this significantly reduces the applicability of traditional navigation calibration and mechanization algorithms. Special attention of this thesis is brought to algorithms that provide sensor navigation information independently of the unit orientation. A technique that classifies the user motion using only sensor information independent of orientation is developed. This method is developed from the navigation algorithm point of view, where successful detection of a walking user and a stationary user enables usage of specific navigation algorithms that are less sensitive to sensor errors. The same algorithm is useful in other applications requiring situational awareness of the user’s mode of transportation.

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