Wi-Fi Positioning System Research Articles

Real-time GPS based outdoor WiFi localization system with map display

There are many applications such as security, medical and rescue operations, sports events, and so on where one may need to find out the exact location of a transmitting wireless local area network (WiFi or WLAN) access point. In this paper, we look at the design of an outdoor WiFi localization system based on a GPS. This system utilizes a rotating unidirectional multi-element Yagi type unidirectional antenna to determine in real-time the direction of the maximum signal strength from a WiFi access point. This information is combined with the GPS co-ordinates of the measurement points to triangulate and find the exact co-ordinates of the access point. These co-ordinates are then transferred to the Google Earth application to automatically pin-point the access point on a world map. This is a novel application where once the user has issued the relevant command, the software will perform all necessary steps without interaction. This includes controlling the hardware, obtaining readings, performing calculations, and showing the location determined on a map. We present the theory and experimental results to illustrate the performance and accuracy of our outdoor localization system.

Accuracy of iPhone Locations: A Comparison of Assisted GPS, WiFi and Cellular Positioning

AbstractThe 3G iPhone was the first consumer device to provide a seamless integration of three positioning technologies: Assisted GPS (A‐GPS), WiFi positioning and cellular network positioning. This study presents an evaluation of the accuracy of locations obtained using these three positioning modes on the 3G iPhone. A‐GPS locations were validated using surveyed benchmarks and compared to a traditional low‐cost GPS receiver running simultaneously. WiFi and cellular positions for indoor locations were validated using high resolution orthophotography. Results indicate that A‐GPS locations obtained using the 3G iPhone are much less accurate than those from regular autonomous GPS units (average median error of 8 m for ten 20‐minute field tests) but appear sufficient for most Location Based Services (LBS). WiFi locations using the 3G iPhone are much less accurate (median error of 74 m for 58 observations) and fail to meet the published accuracy specifications. Positional errors in WiFi also reveal erratic spatial patterns resulting from the design of the calibration effort underlying the WiFi positioning system. Cellular positioning using the 3G iPhone is the least accurate positioning method (median error of 600 m for 64 observations), consistent with previous studies. Pros and cons of the three positioning technologies are presented in terms of coverage, accuracy and reliability, followed by a discussion of the implications for LBS using the 3G iPhone and similar mobile devices.

On outdoor positioning with Wi-Fi

Though GPS is the most popular positioning system at present it does not perform well in indoor environments and metropolitan city areas. Wi-Fi positioning has received much attention due to its advantages with respect to indoor positioning and the wide spread of the Wi-Fi access points (APs). Its performance in an outdoor environment is also of interest as a Wi-Fi based positioning system can overcome the shortcomings of GPS. In this paper, the Wi-Fi positioning technologies which can be used in outdoor environments - trilateration and fingerprinting, are discussed. An experiment based on fingerprinting has been carried out in the Sydney CBD area where Wi-Fi APs are densely deployed. The test results show that the Wi-Fi positioning system based on fingerprinting works well for outdoor localization, especially when directional information is utilized.

Bluebot: Asset tracking via robotic location crawling

From manufacturers, distributors, and retailers of consumer goods to government departments, enterprises of all kinds are gearing up to use RFID technology to increase the visibility of goods and assets within their supply chain and on their premises. However, RFID technology alone lacks the capability to track the location of items once they are moved within a facility. We present a prototype automatic location sensing system that employs a robot, with an attached RFID reader, which periodically surveys the space, associating items it detects with its own location determined with the Wi-Fi positioning system. We propose three algorithms that combine the detected tag’s reading with previous samples to refine its location. Our experiments have shown that our positioning algorithms can bring a two fold improvement over the positional accuracy limitations in both the RFID reader and the positioning technology.

A Real-Time Indoor WiFi Localization System Utilizing Smart Antennas

In this paper, we study issues associated with the implementation of a real-time WiFi localization system for an indoor environment. This system utilizes smart antennas to receive signal strength from a mobile target (access point) and send the signal strength information to a data processing station. This information is combined to find the direction of arrival of the signal and triangulate the mobile target position. No prior radio-frequency finger-printing is required, thus reducing the computational complexity. We present experimental results to illustrate the performance and accuracy of the localization system for an indoor environment set-up <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .

Advanced Integration of WiFi and Inertial Navigation Systems for Indoor Mobile Positioning

This paper presents an aided dead-reckoning navigation structure and signal processing algorithms for self localization of an autonomous mobile device by fusing pedestrian dead reckoning and WiFi signal strength measurements. WiFi and inertial navigation systems (INS) are used for positioning and attitude determination in a wide range of applications. Over the last few years, a number of low-cost inertial sensors have become available. Although they exhibit large errors, WiFi measurements can be used to correct the drift weakening the navigation based on this technology. On the other hand, INS sensors can interact with the WiFi positioning system as they provide high-accuracy real-time navigation. A structure based on a Kalman filter and a particle filter is proposed. It fuses the heterogeneous information coming from those two independent technologies. Finally, the benefits of the proposed architecture are evaluated and compared with the pure WiFi and INS positioning systems.

Applicability of a Wireless Fidelity Positioning System to Tracking Free-range Domestic Fowl (Gallus gallus domesticus) and Helmeted Guineafowl (Numida meleagris galeata)

This paper reports the results of an experiment for testing the applicability of a Wireless Fidelity (WiFi) positioning system to tracking free-range chickens. In this system, the location of a chicken on a continuous plane (an experiment field) at any time was represented by a point (referred to as a location point) on a one meter square grid lattice, and the trajectory of a chicken by the sequence of its location points. The system recorded the location point of a chicken at every second. The experiment used eight Domestic Fowl (Gallus gallus domesticus) and two Helmeted Guineafowl (Numida meleagris galeata), which were kept freely in a park (170 by 90 m), where their spatial behavior was observed for five days. The complete data were collected for three days. Because the observed location points contained random noise, they were treated as probabilistic variables. Data analysis showed that location accuracy was 2.6 m with a probability of 0.95. The living space of a chicken was represented by a two-dimensional probability density function of location points. The function was estimated by the kernel method with the bi-weight kernel function whose bandwidth was 2.6 m. The trajectory of a chicken was estimated by the moving average method. This experiment showed that the WiFi positioning system was practically applicable to tracking free-range chickens.