Sensor Fused Indoor Positioning Using Dual Band WiFi Signal Measurements

A ubiquitous and accurate positioning system for mobile devices is of great importance both to business and research due to the large number of applications and services it enables. In most outdoor environments this problem was solved by the introduction of the Global Positioning System (GPS). In indoor or suburban areas however, the GPS signals are often too weak to enable a reliable position estimate. Instead, other techniques must be utilized to provide accurate positioning. One of these is trilateration based on WiFi signal strengths. This is an auspicious technology to use partly because of the large number of access points (APs) in our everyday environment, and partly due to the possibility of measuring signal strength with a normal smartphone. The technique is further enabled by the move to include transmitters at 2.4 as well as 5 GHz in modern APs, providing a better basis for accurate position estimations. Furthermore, the motion sensors present in today’s smartphones are accurate enough to provide a short-time estimate of the user’s movement with high accuracy. In this thesis, both of these technologies are used to develop an accurate method for indoor positioning, and the contributions can be summed up into two points. The first contribution is an investigation of the behavior of two WiFi frequencies, 2.4 and 5 GHz, where their time dependent noise is proven to be almost uncorrelated with each other. This is then exploited to develop aWiFi-only trilateration algorithm by the use of a particle filter (PF), where the only restriction is that the locations of the APs need to be known. The second contribution is adding an accelerometer and a gyroscope to the algorithm, to provide a more accurate estimation. A step counter is developed using the accelerometer, and the gyroscope detects changes in heading while the WiFi signal strengths give information about the position. This makes it possible to alongside the position also estimate both heading and step length, while still keeping the only restriction of knowing the AP locations. The resulting algorithm produces position estimates with a mean error less than two meters for a specific use case, and around three meters when a more lenient user behavior is allowed

Sensor Fused Indoor Positioning Using Dual Band WiFi Signal Measurements

In this paper, signal strengths from known WiFi access points are used together with a particle filter to perform indoor navigation. It is shown that more information is obtained by using signals of both 2.4 and 5.0 GHz, compared to using only one frequency. Thus, using both frequencies provides a more accurate positioning. The second contribution is an algorithm where WiFi measurements are combined with pedestrian dead reckoning (PDR), which is based on step counting using an accelerometer and hypotheses of the heading using a gyroscope. This was found to provide further accuracy compared to more conventional methods

Indoor positioning of shoppers using a network of bluetooth low energy beacons

In this paper we present our work on the indoor positioning of users (shoppers), using a network of Bluetooth Low Energy (BLE) beacons deployed in a large wholesale shopping store. Our objective is to accurately determine which product sections a user is adjacent to while traversing the store, using RSSI readings from multiple beacons, measured asynchronously on a standard commercial mobile device. We further wish to leverage the store layout (which imposes natural constraints on the movement of users) and the physical configuration of the beacon network, to produce a robust and efficient solution. We start by describing our application context and hardware configuration, and proceed to introduce our node-graph model of user location. We then describe our experimental work which begins with an investigation of signal characteristics along and across aisles. We propose three methods of localization, using a “nearest-beacon” approach as a base-line; exponentially averaged weighted range estimates; and a particle-filter method based on the RSSI attenuation model and Gaussian-noise. Our results demonstrate that the particle filter method significantly out-performs the others. Scalability also makes this method ideal for applications run on mobile devices with more limited computational capabilitie

Item Tracer

One of our daily issues for searching indoor lost item remain unresolved until today as there is no any systematic way of locating it. Unaccounted amount of time and energy has been wasted each day trying to retrieve it based on memory. Therefore, in this project, a prototype is proposed to locate indoor lost item utilizing received signal strength (RSS) for distance estimation. The prototype primary consists of a small size tag for attaching on any item and a reader for computing the estimated location of the tag. A positioning algorithm is developed to analyse the behaviour of received signal strength and calculate the probability of the target location. As the nature of indoor environment varies across each location, the prototype is tested at multiple indoor locations for refining the algorithm and verifying its robustness and consistency in estimating the target location. The results obtained showed that the percentage of error for direction probability is 32 % and accuracy of distance is at 0.9m

Application of Half Spaces in Bounding Wireless Internet Signals for use in Indoor Positioning

The problem of outdoor positioning has been largely solved via the use of GPS. This thesis addresses the problem of determining position in areas where GPS is unavailable. No clear solution exists for indoor localization and all approximation methods offer unique drawbacks. To mitigate the drawbacks, robust systems combine multiple complementary approaches. In this thesis, fusion of wireless internet access points and inertial sensors was used to allow indoor positioning without the need for prior information regarding surroundings. Implementation of the algorithm involved development of three separate systems. The first system simply combines inertial sensors on the Android Nexus 7 to form a step counter capable of providing marginally accurate initial measurements. Having achieved reliable initial measurements, the second system receives signal strength from nearby wireless internet access points, augmenting the sensor data in order to generate half-planes. The half-planes partition the available space and bound the possible region in which each access point can exist. Lastly, the third system addresses the tendency of the step counter to lose accuracy over time by using the recently established positions of the access points to correct flawed values. The resulting process forms a simple feedback loop. A primary contribution of this thesis is an algorithm for determining access point position. Testing shows that in certain applications access points relatively near the user's path of travel can be positioned with high accuracy. Additionally, the nature of the design means that the geometric algorithm has a tendency to achieve maximum performance in environments containing many twists and turns while suffering from a lack of useful data on straight paths. In contrast, winding areas confound the step counter which performs better when used in long straight stretches of constant movement. When combined, these trends complement one another and result in a robust final product