Bias Compensation for UWB Ranging for Pedestrian Geolocation Applications

We present an effective bias compensation method to process none-line-of-sight (NLoS) and long distance line-of-sight (LD-LoS) ultra wideband (UWB) range measurement signals used to aid a pedestrian inertial navigation system (INS). The common UWB bias compensation techniques use machine learning methods to identify and remove the bias in the measurements. These techniques are computationally expensive and require extensive prior data. Here, we propose to use an algorithmic compensation technique that accounts for the bias by estimating it using the Schmidt-Kalman filter (SKF). Next, we exploit the positivity of the error in the UWB range measurements to propose a novel constrained sigma point based correction filtering that can be used atop the SKF for further improvement in the positioning accuracy of the UWB-aided pedestrian inertial navigation. Experiments demonstrate the effectiveness of our methods

User Experience Enhancement on Smartphones using Wireless Communication Technologies

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 박세웅.Recently, various sensors as well as wireless communication technologies such as Wi-Fi and Bluetooth Low Energy (BLE) have been equipped with smartphones. In addition, in many cases, users use a smartphone while on the move, so if a wireless communication technologies and various sensors are used for a mobile user, a better user experience can be provided. For example, when a user moves while using Wi-Fi, the user experience can be improved by providing a seamless Wi-Fi service. In addition, it is possible to provide a special service such as indoor positioning or navigation by estimating the users mobility in an indoor environment, and additional services such as location-based advertising and payment systems can also be provided. Therefore, improving the user experience by using wireless communication technology and smartphones sensors is considered to be an important research field in the future. In this dissertation, we propose three systems that can improve the user experience or convenience by usingWi-Fi, BLE, and smartphones sensors: (i) BLEND: BLE beacon-aided fast Wi-Fi handoff for smartphones, (ii) PYLON: Smartphone based Indoor Path Estimation and Localization without Human Intervention, (iii) FINISH: Fully-automated Indoor Navigation using Smartphones with Zero Human Assistance. First, we propose fast handoff scheme called BLEND exploiting BLE as secondary radio. We conduct detailed analysis of the sticky client problem on commercial smartphones with experiment and close examination of Android source code. We propose BLEND, which exploits BLE modules to provide smartphones with prior knowledge of the presence and information of APs operating at 2.4 and 5 GHz Wi-Fi channels. BLEND operating with only application requires no hardware and Android source code modification of smartphones.We prototype BLEND with commercial smartphones and evaluate the performance in real environment. Our measurement results demonstrate that BLEND significantly improves throughput and video bitrate by up to 61% and 111%, compared to a commercial Android application, respectively, with negligible energy overhead. Second, we design a path estimation and localization system, termed PYLON, which is plug-and-play on Android smartphones. PYLON includes a novel landmark correction scheme that leverages real doors of indoor environments consisting of floor plan mapping, door passing time detection and correction. It operates without any user intervention. PYLON relaxes some requirements for localization systems. It does not require any modifications to hardware or software of smartphones, and the initial location of WiFi APs, BLE beacons, and users. We implement PYLON on five Android smartphones and evaluate it on two office buildings with the help of three participants to prove applicability and scalability. PYLON achieves very high floor plan mapping accuracy with a low localization error. Finally, We design a fully-automated navigation system, termed FINISH, which addresses the problems of existing previous indoor navigation systems. FINISH generates the radio map of an indoor building based on the localization system to determine the initial location of the user. FINISH relaxes some requirements for current indoor navigation systems. It does not require any human assistance to provide navigation instructions. In addition, it is plug-and-play on Android smartphones. We implement FINISH on five Android smartphones and evaluate it on five floors of an office building with the help of multiple users to prove applicability and scalability. FINISH determines the location of the user with extremely high accuracy with in one step. In summary, we propose systems that enhance the users convenience and experience by utilizing wireless infrastructures such as Wi-Fi and BLE and various smartphones sensors such as accelerometer, gyroscope, and barometer equipped in smartphones. Systems are implemented on commercial smartphones to verify the performance through experiments. As a result, systems show the excellent performance that can enhance the users experience.1 Introduction 1 1.1 Motivation 1 1.2 Overview of Existing Approaches 3 1.2.1 Wi-Fi handoff for smartphones 3 1.2.2 Indoor path estimation and localization 4 1.2.3 Indoor navigation 5 1.3 Main Contributions 7 1.3.1 BLEND: BLE Beacon-aided Fast Handoff for Smartphones 7 1.3.2 PYLON: Smartphone Based Indoor Path Estimation and Localization with Human Intervention 8 1.3.3 FINISH: Fully-automated Indoor Navigation using Smartphones with Zero Human Assistance 9 1.4 Organization of Dissertation 10 2 BLEND: BLE Beacon-Aided FastWi-Fi Handoff for Smartphones 11 2.1 Introduction 11 2.2 Related Work 14 2.2.1 Wi-Fi-based Handoff 14 2.2.2 WPAN-aided AP Discovery 15 2.3 Background 16 2.3.1 Handoff Procedure in IEEE 802.11 16 2.3.2 BSS Load Element in IEEE 802.11 16 2.3.3 Bluetooth Low Energy 17 2.4 Sticky Client Problem 17 2.4.1 Sticky Client Problem of Commercial Smartphone 17 2.4.2 Cause of Sticky Client Problem 20 2.5 BLEND: Proposed Scheme 21 2.5.1 Advantages and Necessities of BLE as Secondary Low-Power Radio 21 2.5.2 Overall Architecture 22 2.5.3 AP Operation 23 2.5.4 Smartphone Operation 24 2.5.5 Verification of aTH estimation 28 2.6 Performance Evaluation 30 2.6.1 Implementation and Measurement Setup 30 2.6.2 Saturated Traffic Scenario 31 2.6.3 Video Streaming Scenario 35 2.7 Summary 38 3 PYLON: Smartphone based Indoor Path Estimation and Localization without Human Intervention 41 3.1 Introduction 41 3.2 Background and Related Work 44 3.2.1 Infrastructure-Based Localization 44 3.2.2 Fingerprint-Based Localization 45 3.2.3 Model-Based Localization 45 3.2.4 Dead Reckoning 46 3.2.5 Landmark-Based Localization 47 3.2.6 Simultaneous Localization and Mapping (SLAM) 47 3.3 System Overview 48 3.3.1 Notable RSSI Signature 49 3.3.2 Smartphone Operation 50 3.3.3 Server Operation 51 3.4 Path Estimation 52 3.4.1 Step Detection 52 3.4.2 Step Length Estimation 54 3.4.3 Walking Direction 54 3.4.4 Location Update 55 3.5 Landmark Correction Part 1: Virtual Room Generation 56 3.5.1 RSSI Stacking Difference 56 3.5.2 Virtual Room Generation 57 3.5.3 Virtual Graph Generation 59 3.5.4 Physical Graph Generation 60 3.6 Landmark Correction Part 2: From Floor Plan Mapping to Path Correction 60 3.6.1 Candidate Graph Generation 60 3.6.2 Backbone Node Mapping 62 3.6.3 Dead-end Node Mapping 65 3.6.4 Final Candidate Graph Selection 66 3.6.5 Door Passing Time Detection 68 3.6.6 Path Correction 70 3.7 Particle Filter 71 3.8 Performance Evaluation 73 3.8.1 Implementation and Measurement Setup 73 3.8.2 Step Detection Accuracy 77 3.8.3 Floor Plan Mapping Accuracy 77 3.8.4 Door Passing Time 78 3.8.5 Walking Direction and Localization Performance 81 3.8.6 Impact of WiFi AP and BLE Beacon Number 84 3.8.7 Impact of Walking Distance and Speed 84 3.8.8 Performance on Different Areas 87 3.9 Summary 87 4 FINISH: Fully-automated Indoor Navigation using Smartphones with Zero Human Assistance 91 4.1 Introduction 91 4.2 Related Work 92 4.2.1 Localization-based Navigation System 92 4.2.2 Peer-to-peer Navigation System 93 4.3 System Overview 93 4.3.1 System Architecture 93 4.3.2 An Example for Navigation 95 4.4 Level Change Detection and Floor Decision 96 4.4.1 Level Change Detection 96 4.5 Real-time navigation 97 4.5.1 Initial Floor and Location Decision 97 4.5.2 Orientation Adjustment 98 4.5.3 Shortest Path Estimation 99 4.6 Performance Evaluation 99 4.6.1 Initial Location Accuracy 99 4.6.2 Real-Time Navigation Accuracy 100 4.7 Summary 101 5 Conclusion 102 5.1 Research Contributions 102 5.2 Future Work 103 Abstract (In Korean) 118 감사의 글Docto

Maximum convergence algorithm for WiFi based indoor positioning system

WiFi-based indoor positioning is widely exploited thanks to the existing WiFi infrastructure in buildings and built-in sensors in smartphones. The techniques for indoor positioning require the high-density training data to archive high accuracy with high computation complexity. In this paper, the approach for indoor positioning systems which is called the maximum convergence algorithm is proposed to find the accurate location by the strongest receiver signal in the small cluster and K nearest neighbours (KNN) of other clusters. Also, the K-mean clustering is deployed for each access point to reduce the computation complexity of the offline databases. Moreover, the pedestrian dead reckoning (PDR) method and Kalman filter with the information from the received signal strength (RSS) and inertial sensors are applied to the WiFi fingerprinting to increase the efficiency of the mobile object's position. The different experiments are performed to compare the proposed algorithm with the others using KNN and PDR. The recommended framework demonstrates significant proceed based on the results. The average precision of this system can be lower than 1.02 meters when testing in the laboratory environment with an area of 7x7 m using three access points

Performances Comparison of Nonlinear Filters for Indoor WLAN Positioning

Indoor WLAN positioning should be modeled as a nonlinear and non-Gaussian dynamic system due to the complex indoor environment, radio propagation and motion behaviour. The aim of this paper is to analyze different filtering strategies for real life indoor WLAN positioning systems. The performance criteria for the comparison are the mean of localization errors and computational complexity. Three nonlinear filters are analyzed: Fourier density approximation (FF), particle filter (PF) and grid-based filter (GF), which are representatives for deterministic and random density approximation approaches. Our experimental results help to choose the appropriate filtering techniques under different resource limitations