Localization in indoor environments using commodity off the shelf (Cots) hardware. Time, signal property, and particle filtering methods
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Localization of objects and people in indoor environments is of critical importance in applications ranging from delivering innovative positioning services, healthcare, advertising, shopping, stocking and warehousing, emergency situations such as building fires and many more. For example in hospitals, the timely access to all the necessary equipment and personnel in an emergency situation may determine a patient’s survival. Locating critical resources and equipment with accuracy and speed is especially needed in cases where such assets are misplaced or not adequately organized in space. In another example, in airports and other transportation facilities where typical outdoor positioning systems do not function, indoor positioning systems based on 802.11 or similar technologies can be used for tracking valuable items. Numerous and varied uses of indoor localization range from improving the behavior of communication systems, e.g. timely planning of proactive handovers between access points or base stations leading to the improved end user’s experience, locating/tracking assets for security applications, or even delivering targeted advertisements inside shopping centers. Depending on the underlying technology, various methods may be used to locate a wireless user connecting to a wireless infrastructure comprising of access points and/or base stations. These methods include techniques based on Received Signal Strength (RSS), Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA) etc. This work examines and proposes new methods to increase localization accuracy in indoor environments through the use of 802.11 commodity off-the-shelf equipment. Multiple approaches are followed for developing optimal indoor localization techniques and systems. Firstly, existing well-known indoor localization techniques are replicated leading to a greater understanding of their advantages, drawbacks, and limitations. Subsequently current limitations are addressed aiming towards the development of new techniques and algorithms to progress beyond the current state of the art. The algorithms in this work address wireless indoor localization using off-the-shelf equipment focusing mostly on time-based localization techniques, although a novel RSSI method is also investigated and proposed. Two different approaches are presented regarding the use of the proposed algorithms in conjunction with other techniques for implementing a real-time localization system. In regards to time domain techniques, an innovative time-based localization method that utilizes the information provided by 802.11 Beacon packet data is ii also presented. The advantage of this method over other time-based methods is that the Mobile Terminal (MT) overcomes the need for establishing a connection to an Access Point (AP), sending and receiving packets and clock synchronization. The method works by analyzing Beacon timestamp and MAC timestamp. The use of Beacon Interval, Beacon timestamp and Service Set Identifier (SSID) of a packet, enabled optimal processing of the collected MAC timestamps to provide a hybrid TDOA-TOA localization method. Another contribution of this work is a novel approach that is able to accelerate network packet time-based methods, based on a packet validation process. It is shown that the localization process time can be shortened without reducing positioning accuracy. Moreover, the problem of tracking a moving target in an indoor environment using time-based methods is also addressed by utilizing a particle filtering method based on the kinematic characteristics of the target and past measurements. This method offers target trajectory information which has the potential to improve localization. The particle filter based method sequentially estimates the posterior of the target state and is able to fuse past and present measurements from multiple sensors with the target kinematic information in order to improve localization. Although the main focus of this work is on time-based localization methods, during the research various RSS-based methods have been evaluated. Time based and RSS based localization methods are the two most popular indoor localization approaches. In order to go a step further in performance evaluation of the proposed methods, a cross comparison study between these approaches was necessary to take advantage of the best of both worlds. Following several experiments, it was found that while typical RSS methods utilize the average signal of surrounding APs recorded at stationary points, better localization results can be achieved by utilizing multiple RSS signals, e.g. over a path. It was observed that using RSS over travelled paths can improve localization accuracy and creates more accurate data-driven measurement models. This has led to the development of a new WKNN localization technique – described in a paper titled “a novel RSS-based localization method using path analysis”. Last but not least, in this thesis the terms “Localization” and “Positioning” are used interchangeably and they refer to the same definition.