-An algorithm for pedestrian navigation in indoor and urban canyon environments is presented. It considers platforms with low processing power and lowcost sensors. A combination of Wi-Fi positioning and dead reckoning, based on a Hidden Markov Model, is used. The positions of the Wi-Fi fingerprints in the database are used as hidden states. Dead reckoning is taken for state transition and a database correlation of the Wi-Fi signal strength measurements is performed in the measurement update. The dead reckoning consists of an accelerometer driven step length estimation and a magnetic field based heading calculation. Simulations and tests demonstrate that in this way ambiguities common in Wi-Fi positioning can be solved and outages can be bridged. Therefore, higher accuracy and robustness can be achieved.

Due to an increasing number of public and private access points in indoor and urban environments, Wi-Fi positioning becomes more and more attractive for pedestrian navigation. In the last ten years different approaches and solutions have been developed. But Wi-Fi hardware and network protocols have not been designed for positioning. Therefore, Wi-Fi devices have different hardware characteristics that lead to different positioning accuracies. In this article we analyze and discuss hardware characteristics of Wi-Fi devices with a focus on the so called Wi-Fi fingerprinting technique for positioning. The analysis is based on measurements collected using a static setup in an anechoic chamber to minimize signal reflections and noise. Characteristics like measurement offsets and practical polling intervals of different mobile devices have been examined. Based on this analysis a calibration approach to compensate the measurement offsets of Wi-Fi devices is proposed. Experimen tal results in a typically office building are presented to evaluate the improvement in localization accuracy using the calibration approach

We present an algorithm for pedestrian navigation optimized for smart mobile platforms using the present low-cost sensors and the limited processing power. The algorithm is based on a Hidden Markov Model that combines Wi-Fi positioning and dead reckoning. The hidden states are the positions of the Wi-Fi fingerprints in the database. The state transition includes dead reckoning based on step length estimation from acceleration measurements and compass heading calculated from magnetic field measurements. In the measurement update a database correlation of the actual Wi-Fi signal strength measurements with the stored values in the fingerprints has been performed. In simulations and tests we demonstrate that in this way ambiguities common in Wi-Fi positioning can be reduced. Therefor, higher accuracy and robustness can be achieved.

In urban environments GNSS signals can be either blocked by buildings, so that the number of satellites with direct line-of-sight (LOS) is reduced considerably, or reflected by surfaces, so that signals from the same satellite are received over multiple paths. Taking into account all available signals would typically result in a rather poor position estimate. It is therefore essential to distinguish LOS from non-LOS or multipath-contaminated signals and include this information in the GNSS process model. This is done using a classification algorithm based on code (pseudorange) and carrier phase observations. On the other hand, Wi-Fi fingerprinting is complementary to GNSS in the sense that this approach benefits from signal degradations caused by shadowing through obstacles and reflections leading to unique variations in the radio map and less ambiguity in the mapping of signal strength measurements to positions. The fusion of GNSS and Wi-Fi measurements is done with a particle filter, which uses the probabilistic measurement process models for GNSS and Wi-Fi as inputs. The advantage of the particle filter is its ability to work with non-linear dynamical models and non-Gaussian probability distributions. The evaluation was done with a modular ARM-processor based hardware platform (miniLOK) with Android as operating system, which provides an interface to the raw data of the GNSS receiver. The algorithms were implemented in Java on top of the awiloc® software framework, which is a positioning system including Wi-Fi fingerprinting developed at Fraunhofer IIS. The paper starts with the construction of measurement process models for GNSS and Wi-Fi and explains how these models are integrated into the particle filter framework. The experimental results are presented together with a description of the setup and equipment. The achieved positioning uncertainty in urban environments is discussed

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