Wi-Fi positioning system

Summary

Wi-Fi positioning system (WPS, also abbreviated as WiPS or WFPS) is a geolocation system that uses the characteristics of nearby Wi-Fi hotspots and other wireless access points to discover where a device is located. It is used where satellite navigation such as GPS is inadequate due to various causes including multipath and signal blockage indoors, or where acquiring a satellite fix would take too long. Such systems include assisted GPS, urban positioning services through hotspot databases, and indoor positioning systems. Wi-Fi positioning takes advantage of the rapid growth in the early 21st century of wireless access points in urban areas. The most common and widespread localization technique used for positioning with wireless access points is based on measuring the intensity of the received signal (received signal strength indication or RSSI) and the method of "fingerprinting". Typical parameters useful to geolocate the wireless access point include its SSID and MAC address. The

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https://en.wikipedia.org/wiki/Wi-Fi_positioning_system

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My master thesis proposes a general methodology to model pedestrian destination choice from WiFi localization in multimodal transport facilities (e.g., airports, railway stations). It is based on the output of [Danalet, A., B. Farooq and M. Bierlaire (2014) A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures, Transportation Research Part C: Emerging Technologies, 44, 146–170.] method to generate candidates of activity-episode sequences from WiFi measurements, locations of activities on a map and prior information. Destination choice is nested to the activity choice. An individual first chooses an activity ([Danalet, A. and M. Bierlaire (2015) Importance sampling for activity path choice, paper presented at the 15th Swiss Transport Research Conference (STRC), Monte Verità, Ascona, Switzerland.]), and then selects the destination where to perform it. We propose an approach to model destination choice accounting for panel nature of data. We compare static, dynamic strict exogenous and dynamic model with two different agent effect corrections inspired by [Wooldridge, J. M. (2002) Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity, Journal of applied econometrics, 44, ISSN 1753-9196.] method. In a case study using WiFi traces on EPFL campus, we focus on one activity type: catering. The choice set contains 21 alternatives on campus (restaurants, self-services, cafeterias, ...). Our models reveal that the choice of a catering facility depends mostly on habits (e.g., where an individual ate the previous time), distance to walk from the previous activity-episode (calculated with a weighted shortest path algorithm) but less on destination specific determinants (e.g., price, capacity). The models are successfully validated using the same WiFi dataset and we forecast possible changes concerning catering destinations on the campus.

2015

Modeling and Benchmarking Ultra-Wideband Localization for Mobile Robots

Alexander Bahr, Edmund James Colli-Vignarelli, Catherine Dehollain, Alcherio Martinoli

Ultra-Wideband Impulse Radio (UWB-IR) is a technology that has great potential to solve numerous mobile robotic and asset tracking problems in GPS-denied environments. Our goal is to help software and hardware designers in improving the state-of-the-art in UWB-based robotic localization. We developed a test-bed where an UWB transmitter is attached to a mobile robot. By combining the received signals with the robot’s position log acquired through the dead-reckoning sensors, we obtain UWB signals which are well referenced with respect to the transmitter-receiver distance and orientation. In addition, we provide a model for every component of the setup. The entire setup allows us to simulate from first principles every aspect of an UWB localization system and then to implement low-level signal processing as well as higher-level modulation and localization techniques. We implement an Automatic Gain Control (AGC) algorithm to demonstrate the rapid proto-typing capabilities of the test-bed. Our work shows how an UWB robotic system and its models can be involved in all phases of the development of a technology that can help robot’s navigation, localization and communication algorithms.

2012

An autonomous localisation and tracking method is a method independent from the reception of external data. In our approach we ignore methods like GPS- and WiFi- positioning and we focus on the use of inertial navigation system (INS) carried by the person and connected to a map database. The walking person is considered as a dynamic system, whose movements are measured by the INS. His trajectory is modified with respect to a dedicated motion model. Users location is estimated in the frame of Bayesian inference and is based on the association of the trajectory to the map database, a technique known as map- matching. Because of the non-linear nature of the estimation problem, non-inear filtering techniques like particle filters (Sequential Monte Carlo methods) are applied. In tracking mode simple geometric constrains will be observed in order to associate every point of the trajectory to element of the map database. In parallel the localisation mode rest active in order to keep the knowledge on the history of measurements. The algorithm is tested on the campus of EPFL; the process of localisation is entirely autonomous and gives promising results. That method of localisation can be applied to many pedestrian navigation tasks, in particular for the needs of the fire-brigades and security services.

2007