Organic Indoor Location Discovery

We describe an indoor, room-level location discovery method based on spatial variations in "wifi signatures," i.e., MAC addresses and signal strengths of existing wireless access points. The principal novelty of our system is its organic nature; it builds signal strength maps from the natural mobility and lightweight contributions of ordinary users, rather than dedicated effort by a team of site surveyors. Whenever a user's personal device observes an unrecognized signature, a GUI solicits the user's location. The resulting location-tagged signature or "bind" is then shared with other clients through a common database, enabling devices subsequently arriving there to discover location with no further user contribution. Realizing a working system deployment required three novel elements: (1) a human-computer interface for indicating location over intervals of varying duration; (2) a client-server protocol for pre-fetching signature data for use in localization; and (3) a location-estimation algorithm incorporating highly variable signature data. We describe an experimental deployment of our method in a nine-story building with more than 1,400 distinct spaces served by more than 200 wireless access points. At the conclusion of the deployment, users could correctly localize to within 10 meters 92 percent of the time

Implementing a real-time locating system based on wireless sensor networks and artificial neural networks to mitigate the multipath effect

Wireless Sensor Networks comprise an ideal technology to develop Real-Time Locating Systems (RTLSs) aimed at indoor environments, where existing global navigation satellite systems do not work correctly due to the blockage of the satellite signals. In this regard, one of the main challenges is to deal with the problems that arise from the effects of the propagation of radio frequency waves, such as multipath. This paper presents an innovative mathematical model for improving the accuracy of RTLSs, focusing on the mitigation of the multipath effect by using Multi-Layer Perceptron Artificial Neural Networks. The model is used to implement a novel indoor Real-Time Locating System based on Wireless Sensor Networks

AoA-aware Probabilistic Indoor Location Fingerprinting using Channel State Information

With expeditious development of wireless communications, location fingerprinting (LF) has nurtured considerable indoor location based services (ILBSs) in the field of Internet of Things (IoT). For most pattern-matching based LF solutions, previous works either appeal to the simple received signal strength (RSS), which suffers from dramatic performance degradation due to sophisticated environmental dynamics, or rely on the fine-grained physical layer channel state information (CSI), whose intricate structure leads to an increased computational complexity. Meanwhile, the harsh indoor environment can also breed similar radio signatures among certain predefined reference points (RPs), which may be randomly distributed in the area of interest, thus mightily tampering the location mapping accuracy. To work out these dilemmas, during the offline site survey, we first adopt autoregressive (AR) modeling entropy of CSI amplitude as location fingerprint, which shares the structural simplicity of RSS while reserving the most location-specific statistical channel information. Moreover, an additional angle of arrival (AoA) fingerprint can be accurately retrieved from CSI phase through an enhanced subspace based algorithm, which serves to further eliminate the error-prone RP candidates. In the online phase, by exploiting both CSI amplitude and phase information, a novel bivariate kernel regression scheme is proposed to precisely infer the target's location. Results from extensive indoor experiments validate the superior localization performance of our proposed system over previous approaches

Assisted GPS solution in cellular networks

The ‘Wireless Enhanced 911’ rules, which were issued in 1996, state that the position of a mobile device should be sent to Public Safety Answering Point (‘PSAP’) once a 9-1-1 call takes place from it. The rules imposed the cellular carriers to integrate a technology into their networks so that the mobile device location can be transferred once a 9-1-1 call is made. One of the chosen technologies was the Global Positioning System (GPS). The solution suggests integrating a GPS receiver into every cellular device. But the GPS receiver, as a stand alone solution, has some major performance limitations in regards to the Wireless Enhanced 911 requirements. The Assisted GPS (A-GPS) technology improves the GPS receiver performances. It reduces the time it takes the receiver to calculate its location. It also enhances the receiver’s reception sensitivity and improves the calculated position accuracy. With the A-GPS technology, the GPS receiver solution becomes compatible with the rules requirements. Two of the four large wireless carriers in the U.S. had chosen the A-GPS as their location solution in their networks. The A-GPS technology became an important part of the cellular industry. The intention of the thesis is to explore the A-GPS solution and to show its necessity in today’s GPS-based solutions. The following aspects are reviewed in the thesis – how the A-GPS solution works, how it improves the GPS receiver performances, the technology that is being used to implement it, and how it integrates to the cellular network. Another A-GPS related aspect that is reviewed in the thesis is the integration of location-based applications in cellular networks. The location-based applications service is a new and growing market in the cellular industry as a result of the deployed location solutions

Determining the location of a bus in real-time using LORA technology.

Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfilment of the requirements for the award of Bachelor of Science degree in Electrical and Electronics Engineering, April 2019.Travelers without prior information about the bus they are waiting, often waste time at bus stations which affects their day activity plan. GPS receivers are used to address this problem by providing real-time about the location of the bus, however, it is quite expensive and power consuming to use and every bus needs to have a GSM/GPRS module to be able to send the information to the internet. This project suggests a low-cost and less power consuming approach of using LoRa technology, geometry methods to determine the location of the bus and only one GSM module for multiple buses to transmit the data to the internet. Results in the project show success in determining the location of the bus in real-time.Ashesi University

Indoor localization using place and motion signatures

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2013.This electronic version was submitted and approved by the author's academic department as part of an electronic thesis pilot project. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from department-submitted PDF version of thesis.Includes bibliographical references (p. 141-153).Most current methods for 802.11-based indoor localization depend on either simple radio propagation models or exhaustive, costly surveys conducted by skilled technicians. These methods are not satisfactory for long-term, large-scale positioning of mobile devices in practice. This thesis describes two approaches to the indoor localization problem, which we formulate as discovering user locations using place and motion signatures. The first approach, organic indoor localization, combines the idea of crowd-sourcing, encouraging end-users to contribute place signatures (location RF fingerprints) in an organic fashion. Based on prior work on organic localization systems, we study algorithmic challenges associated with structuring such organic location systems: the design of localization algorithms suitable for organic localization systems, qualitative and quantitative control of user inputs to "grow" an organic system from the very beginning, and handling the device heterogeneity problem, in which different devices have different RF characteristics. In the second approach, motion compatibility-based indoor localization, we formulate the localization problem as trajectory matching of a user motion sequence onto a prior map. Our method estimates indoor location with respect to a prior map consisting of a set of 2D floor plans linked through horizontal and vertical adjacencies. To enable the localization system, we present a motion classification algorithm that estimates user motions from the sensors available in commodity mobile devices. We also present a route network generation method, which constructs a graph representation of all user routes from legacy floor plans. Given these inputs, our HMM-based trajectory matching algorithm recovers user trajectories. The main contribution is the notion of path compatibility, in which the sequential output of a classifier of inertial data producing low-level motion estimates (standing still, walking straight, going upstairs, turning left etc.) is examined for metric/topological/semantic agreement with the prior map. We show that, using only proprioceptive data of the quality typically available on a modern smartphone, our method can recover the user's location to within several meters in one to two minutes after a "cold start."by Jun-geun Park.Ph.D

Space-partitioning with cascade-connected ANN structures for positioning in mobile communication systems

The world around us is getting more connected with each day passing by – new portable devices employing wireless connections to various networks wherever one might be. Locationaware computing has become an important bit of telecommunication services and industry. For this reason, the research efforts on new and improved localisation algorithms are constantly being performed. Thus far, the satellite positioning systems have achieved highest popularity and penetration regarding the global position estimation. In spite the numerous investigations aimed at enabling these systems to equally procure the position in both indoor and outdoor environments, this is still a task to be completed. This research work presented herein aimed at improving the state-of-the-art positioning techniques through the use of two highly popular mobile communication systems: WLAN and public land mobile networks. These systems already have widely deployed network structures (coverage) and a vast number of (inexpensive) mobile clients, so using them for additional, positioning purposes is rational and logical. First, the positioning in WLAN systems was analysed and elaborated. The indoor test-bed, used for verifying the models’ performances, covered almost 10,000m2 area. It has been chosen carefully so that the positioning could be thoroughly explored. The measurement campaigns performed therein covered the whole of test-bed environment and gave insight into location dependent parameters available in WLAN networks. Further analysis of the data lead to developing of positioning models based on ANNs. The best single ANN model obtained 9.26m average distance error and 7.75m median distance error. The novel positioning model structure, consisting of cascade-connected ANNs, improved those results to 8.14m and 4.57m, respectively. To adequately compare the proposed techniques with other, well-known research techniques, the environment positioning error parameter was introduced. This parameter enables to take the size of the test environment into account when comparing the accuracy of the indoor positioning techniques. Concerning the PLMN positioning, in-depth analysis of available system parameters and signalling protocols produced a positioning algorithm, capable of fusing the system received signal strength parameters received from multiple systems and multiple operators. Knowing that most of the areas are covered by signals from more than one network operator and even more than one system from one operator, it becomes easy to note the great practical value of this novel algorithm. On the other hand, an extensive drive-test measurement campaign, covering more than 600km in the central areas of Belgrade, was performed. Using this algorithm and applying the single ANN models to the recorded measurements, a 59m average distance error and 50m median distance error were obtained. Moreover, the positioning in indoor environment was verified and the degradation of performances, due to the crossenvironment model use, was reported: 105m average distance error and 101m median distance error. When applying the new, cascade-connected ANN structure model, distance errors were reduced to 26m and 2m, for the average and median distance errors, respectively. The obtained positioning accuracy was shown to be good enough for the implementation of a broad scope of location based services by using the existing and deployed, commonly available, infrastructure

Adaptive, reliable, and accurate positioning model for location-based services

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents a new strategy in achieving highly reliable and accurate position solutions fulfilling the requirements of Location-Based Services (LBS) pedestrians’ applications. The new strategy is divided into two main parts. The first part integrates the available positioning technology within the surrounding LBS application context by introducing an adaptive LBS framework. The context can be described as a group of factors affecting the application behaviour; this includes environmental states, available resources and user preferences. The proposed adaptive framework consists of several stages, such as defining the contextual factors that have a direct effect on the positioning performance, identifying preliminary positioning performance requirements associated with different LBS application groups, and introducing an intelligent positioning services selection function. The second part of this work involves the design and development of a novel positioning model that is responsible for delivering highly reliable, accurate and precise position solutions to LBS users. This new model is based on the single frequency GPS Standard Positioning Service (SPS). Additionally, it is incorporated within the adaptive LBS framework while providing the position solutions, in which all identified contextual factors and application requirements are accounted. The positioning model operates over a client-server architecture including two main components, described as the Localisation Server (LS) and the Mobile Unit (MU). Hybrid functional approaches were developed at both components consisting of several processing procedures allowing the positioning model to operate in two position determination modes. Stand-alone mode is used if enough navigation information was available at the MU using its local positioning device (GPS/EGNOS receiver). Otherwise, server-based mode is utilised, in which the LS intervenes and starts providing the required position solutions. At the LS, multiple sources of GPS augmentation services were received using the Internet as the sole augmentation data transportation medium. The augmentation data was then processed and integrated for the purpose of guaranteeing the availability of valid and reliable information required for the provision of accurate and precise position solutions. Two main advanced position computation methods were developed at the LS, described as coordinate domain and raw domain. The positioning model was experimentally evaluated. According to the reported results, the LS through the developed position computation methods, was able to provide position samples with an accuracy of less than 2 meters, with high precision at 95% confidence level; this was achieved in urban, rural, and open space (clear satellite view) navigation environments. Additionally, the integrity of the position solutions was guaranteed in such environments during more than 90% of the navigation time, taking into consideration the identified integrity thresholds (Horizontal Alert Limits (HAL)=11 m). This positioning performance has outperformed the existing GPS/EGNOS service which was implemented at the MU in all scenarios and environments. In addition, utilising a simulation evaluation facility the developed positioning model performance was quantified with reference to a hybrid positioning service that will be offered by future Galileo Open Service (OS) along with GPS/EGNOS. Using the statistical t-test, it was concluded that there is no significant difference in terms of the position samples’ accuracy achieved from the developed positioning model and the hybrid system at a particular navigation environment described as rural area. The p-value was 0.08 and the level of significance used was 0.05. However, a significant difference in terms of the service integrity for the advantage of the hybrid system was experienced in all remaining scenarios and environments more especially the urban areas due to surrounding obstacles and conditions

RF Location Tracking: A Modular Antenna System Implementation

From the Amazon Prime Air drone delivery service to the usage of unmanned aerial vehicles (UAV) in military operations, recent years have seen the development of autonomous flight technologies becoming one of the major research topics in the drone industry. Tracking the geographic position of drones is a crucial part of any autonomous flight, but the common methods of drone location tracking either have too large of an error margin or require extensive environmental setup. The aforementioned issues are major roadblocks in the advancement of autonomous flight operations. The proposed solution is a new and improved method to track the location of a drone relative to a single reference point. This method will not require any environmental setup and offers a greater degree of precision than the commonly used Global Positioning System (GPS). The designed proof of concept model, which is a completely modular and self-reliant radio-frequency (RF) based location tracking system, was built to show the viability of this new drone tracking method. The tracking system can determine the relative location of a radio-frequency source with only one receiver module. By requiring only one receiver, this tracking system eliminates the need to set up a triangulation zone. Additionally, optimizing the tracking system to generate a location from the RF telemetry signals needed in user-drone communication, the solution effectively presents an efficient manner to track a drone without the need for additional attachments. The proposed solution introduces a novel method that has the potential to vastly improve autonomous flight development and push it to full realization and fruition