Entwicklung und Implementierung eines Peer-to-Peer Kalman Filters für Fußgänger- und Indoor-Navigation

Smartphones are an integral part of our society by now. They are used for messaging, searching the Internet, working on documents, and of course for navigation. Although smartphones are also used for car navigation their main area of application is pedestrian navigation. Almost all smartphones sold today comprise a GPS L1 receiver which provides position computation with accuracy between 1 and 10 m as long as the environment in beneficial, i.e. the line-of-sight to satellites is not obstructed by trees or high buildings. But this is often the case in areas where smartphones are used primarily for navigation. Users walk in narrow streets with high density, in city centers, enter, and leave buildings and the smartphone is not able to follow their movement because it loses satellite signals. The approach presented in this thesis addresses the problem to enable seamless navigation for the user independently of the current environment and based on cooperative positioning and inertial navigation. It is intended to realize location-based services in areas and buildings with limited or no access to satellite data and a large amount of users like e.g. shopping malls, city centers, airports, railway stations and similar environments. The idea of this concept was for a start based on cooperative positioning between users’ devices denoted here as peers moving within an area with only limited access to satellite signals at certain places (windows, doors) or no access at all. The devices are therefore not able to provide a position by means of satellite signals. Instead of deploying solutions based on infrastructure, surveying, and centralized computations like range measurements, individual signal strength, and similar approaches a decentralized concept was developed. This concept suggests that the smartphone automatically detects if no satellite signals are available and uses its already integrated inertial sensors like magnetic field sensor, accelerometer, and gyroscope for seamless navigation. Since the quality of those sensors is very low the accuracy of the position estimation decreases with each step of the user. To avoid a continuously growing bias between real position and estimated position an update has to be performed to stabilize the position estimate. This update is either provided by the computation of a position based on satellite signals or if signals are not available by the exchange of position data with another peer in the near vicinity using peer-to-peer ad-hoc networks. The received and the own position are processed in a Kalman Filter algorithm and the result is then used as new position estimate and new start position for further navigation based on inertial sensors. The here presented concept is therefore denoted as Peer-to-Peer Kalman Filter (P2PKF)

Indoor Navigation of Emergency Agents

Existing indoor navigation solutions usually rely on pre-installed sensor networks, whereas emergency agents are interested in fully auto-deployable systems. In this paper, an almost self-deployable solution based on Radio-frequency identification tags and inertial Micro Electro Mechanical Sensors is presented. The benefits of the solution are evaluated and compared with the pure inertial positioning system

Tracking with Sparse and Correlated Measurements via a Shrinkage-based Particle Filter

This paper presents a shrinkage-based particle filter method for tracking a mobile user in wireless networks. The proposed method estimates the shadowing noise covariance matrix using the shrinkage technique. The particle filter is designed with the estimated covariance matrix to improve the tracking performance. The shrinkage-based particle filter can be applied in a number of applications for navigation, tracking and localization when the available sensor measurements are correlated and sparse. The performance of the shrinkage-based particle filter is compared with the posterior Cramer-Rao lower bound, which is also derived in the paper. The advantages of the proposed shrinkage-based particle filter approach are demonstrated via simulation and experimental results

사용자 상황인지 딥러닝을 사용한 GPS 반송파 / 관성 센서 결합 스마트폰 보행자 항법

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 여재익.본 논문에서는 스마트폰 Galaxy S8 환경에서 GPS / INS 결합 보행자 항법을 수행하였으며, 스마트폰 센서의 특성을 자세히 분석하였다. 이에 최근 공개된 Android GNSS API 를 사용하여 GPS 원시데이터를 항법에 이용하면서, Cycle slip 을 보정한 Carrier phase 를 이용한 속도 결정법을 사용하였다. 이로 인해 기존의 NMEA GPS 를 사용한 방식의 스마트폰 보행자 항법보다 정밀한 위치, 속도 항법이 가능하였고, 성능을 향상 시켰다. 또한 사용자 상황 분석이 가능한 분류 딥러닝 기법을 사용하여 각 보행 상황에 따른 분류감지가 가능하였음 보였으며, LSTM 의 입력부분을 변화한 몇가지 딥러닝 모델의 성능을 비교하였다. 이를 통해서 사용자의 보행 상황에 따른 적응적 보행자 항법 파라메터 결정이 가능함의 가능성을 보였다.In this research, the overall construction of the smartphone GPS / INS pedestrian dead reckoning system is detaily described with considering the smartphone sensor measurement properties. Also, the recent android GNSS API which can provide the raw GPS measurement is used. With carrier phase, the cycleslip compensated velocity determination is considered. As a result, the carrier phase /INS integrated pedestrian dead reckoning shows the more precise navigation accuracy than NMEA. Moreover, The deep learning approach is applied in the user context classification to change the parameters in the pedestrian dead reckoning system. The author compares the effect of several transformed inputs for the LSTM model and validate each classification performances.Abstract i Contents ii List of Figures iv List of Tables vii Chapter 1. Introduction 1 1.1 Motivation and Backgrounds 1 1.2 Research Purpose and Contribution 3 1.3 Contents and Methods of Research 3 Chapter 2. Smartphone GPS / INS measurements analysis 4 2.1 Smartphone GNSS measurements 4 2.1.1 Android Raw GNSS Measurements API 4 2.1.2 Raw GPS Measurements Properties 7 2.1.3 Smartphone NMEA Location Provider 8 2.1.4 Pseudorange Based Position Estimation 10 2.1.5 Position Determination Experiment 11 2.2 Smartphone INS Measurements 12 2.2.1 Android Sensor Manager API 12 2.2.2 INS Measurements Properties 13 2.2.3 Noise level, Constant bias, Scale factor, Calibration 14 2.2.4. Accelerometer, Gyroscope Calibration Experiment 17 2.2.5 Magnetometer Ellipse Fitting Calibration 22 2.2.6 Random Bias, Allan Variance Exiperiment 25 2.3 Developed Android Smartphone App 30 Chapter 3. Pedestrian Dead Reckoning 31 3.1 Pedestrian Dead-Reckoning System 31 3.1.1 Attitude Determination Quaternion Kalman Filter 32 3.1.2 Attitude Determination Simulation , Experiment 35 3.1.3 Walking Detection 39 3.1.4 Step Counting, Stride Length 41 3.1.5 Pedestrian Dead Reckoning Experiment 45 Chapter 4. Carrier phase / INS integrated Pedestrian Dead Reckoning 50 4.1 Carrier phase Cycleslip Compensation & Velocity Determination 50 4.1.1 Carrier phase Cycleslip Compensation 50 4.1.2 Android Environment Cycle slip Detection 51 4.1.3 False Alarm & Miss Detection Analysis 55 4.1.4 Doppler, Carrier Based Velocity Estimation 56 4.1.5 Cycle slip Compensation & Velocity Determination Experiment 58 4.2 Raw GPS / INS Integrated Pedestrian Dead Reckoning 63 4.2.1 GPS / INS Integration 63 4.2.2 Position Determination Extended Kalman Filter 65 4.2.3 Raw GPS / INS Integrated Pedestrian Dead Reckoning Experiment 66 Chapter 5. User Context Classification Deep learning for Adaptive PDR 69 5.1 Smartphone Location / Walking Context Classification 69 5.1.1 Smartphone Location / Walking Context Dataset 69 5.1.2 Deep Learning Models 71 5.1.3 Comparison of Input Transformations 72 Chapter 6. Conclusion & Future work 76 Chapter 7. Bibliography 77Maste

Filtering Noise Caused by Sensor Selection for an Ultra-wide Band Position Tracking System

A filter designed to be optimal for alternating multi-sensor tracking systems is presented. This filter can be used on ultra-wide band position tracking systems such as the Ubisense system. In this thesis, a comprehensive evaluation of the accuracy of the Ubisense tracking system in a multi-room building is given. Then the multi-modality of the noise distribution of such a system is shown. A multi-sensor tracking system is then simulated and a Kalman filter is used to filter the measurements. It is shown that the Kalman filter is not optimal for such a system and the need for a filter that addresses the issue of multi-modality is explained. Finally a modified particle filter is designed and is shown to effectively reduce the noise in an alternating multi-sensor position tracking system

Reference-Free Displacement Estimation of Bridges Using Kalman Filter-Based Multimetric Data Fusion

Displacement responses of a bridge as a result of external loadings provide crucial information regarding structural integrity and current conditions. Due to the relative characteristic of displacement, the conventional measurement approach requires reference points to firmly install the transducers, while the points are often unavailable for bridges. In this paper, a displacement estimation approach using Kalman filter-based data fusion is proposed to provide a practical means for displacement measurement. The proposed method enables accurate displacement estimation by optimally utilizing acceleration and strain in combination that have high availability and are free from reference points for sensor installation. The Kalman filter is formulated using a state-space model representing the double integration of acceleration and model-based strain-displacement relationship. The validation of the proposed method is conducted successfully by a numerical simulation and a field experiment, which shows the efficacy and accuracy of the proposed approach in bridge displacement measurement.ope

Opportunistic timing signals for pervasive mobile localization

Mención Internacional en el título de doctorThe proliferation of handheld devices and the pressing need of location-based services call for precise and accurate ubiquitous geographic mobile positioning that can serve a vast set of devices. Despite the large investments and efforts in academic and industrial communities, a pin-point solution is however still far from reality. Mobile devices mainly rely on Global Navigation Satellite System (GNSS) to position themselves. GNSS systems are known to perform poorly in dense urban areas and indoor environments, where the visibility of GNSS satellites is reduced drastically. In order to ensure interoperability between the technologies used indoor and outdoor, a pervasive positioning system should still rely on GNSS, yet complemented with technologies that can guarantee reliable radio signals in indoor scenarios. The key fact that we exploit is that GNSS signals are made of data with timing information. We then investigate solutions where opportunistic timing signals can be extracted out of terrestrial technologies. These signals can then be used as additional inputs of the multi-lateration problem. Thus, we design and investigate a hybrid system that combines range measurements from the Global Positioning System (GPS), the world’s most utilized GNSS system, and terrestrial technologies; the most suitable one to consider in our investigation is WiFi, thanks to its large deployment in indoor areas. In this context, we first start investigating standalone WiFi Time-of-flight (ToF)-based localization. Time-of-flight echo techniques have been recently suggested for ranging mobile devices overWiFi radios. However, these techniques have yielded only moderate accuracy in indoor environments because WiFi ToF measurements suffer from extensive device-related noise which makes it challenging to differentiate between direct path from non-direct path signal components when estimating the ranges. Existing multipath mitigation techniques tend to fail at identifying the direct path when the device-related Gaussian noise is in the same order of magnitude, or larger than the multipath noise. In order to address this challenge, we propose a new method for filtering ranging measurements that is better suited for the inherent large noise as found in WiFi radios. Our technique combines statistical learning and robust statistics in a single filter. The filter is lightweight in the sense that it does not require specialized hardware, the intervention of the user, or cumbersome on-site manual calibration. This makes the method we propose as the first contribution of the present work particularly suitable for indoor localization in large-scale deployments using existing legacy WiFi infrastructures. We evaluate our technique for indoor mobile tracking scenarios in multipath environments, and, through extensive evaluations across four different testbeds covering areas up to 1000m2, the filter is able to achieve a median ranging error between 1:7 and 2:4 meters. The next step we envisioned towards preparing theoretical and practical basis for the aforementioned hybrid positioning system is a deep inspection and investigation of WiFi and GPS ToF ranges, and initial foundations of single-technology self-localization. Self-localization systems based on the Time-of-Flight of radio signals are highly susceptible to noise and their performance therefore heavily rely on the design and parametrization of robust algorithms. We study the noise sources of GPS and WiFi ToF ranging techniques and compare the performance of different selfpositioning algorithms at a mobile node using those ranges. Our results show that the localization error varies greatly depending on the ranging technology, algorithm selection, and appropriate tuning of the algorithms. We characterize the localization error using real-world measurements and different parameter settings to provide guidance for the design of robust location estimators in realistic settings. These tools and foundations are necessary to tackle the problem of hybrid positioning system providing high localization capabilities across indoor and outdoor environments. In this context, the lack of a single positioning system that is able the fulfill the specific requirements of diverse indoor and outdoor applications settings has led the development of a multitude of localization technologies. Existing mobile devices such as smartphones therefore commonly rely on a multi-RAT (Radio Access Technology) architecture to provide pervasive location information in various environmental contexts as the user is moving. Yet, existing multi-RAT architectures consider the different localization technologies as monolithic entities and choose the final navigation position from the RAT that is foreseen to provide the highest accuracy in the particular context. In contrast, we propose in this work to fuse timing range (Time-of-Flight) measurements of diverse radio technologies in order to circumvent the limitations of the individual radio access technologies and improve the overall localization accuracy in different contexts. We introduce an Extended Kalman filter, modeling the unique noise sources of each ranging technology. As a rich set of multiple ranges can be available across different RATs, the intelligent selection of the subset of ranges with accurate timing information is critical to achieve the best positioning accuracy. We introduce a novel geometrical-statistical approach to best fuse the set of timing ranging measurements. We also address practical problems of the design space, such as removal of WiFi chipset and environmental calibration to make the positioning system as autonomous as possible. Experimental results show that our solution considerably outperforms the use of monolithic technologies and methods based on classical fault detection and identification typically applied in standalone GPS technology. All the contributions and research questions described previously in localization and positioning related topics suppose full knowledge of the anchors positions. In the last part of this work, we study the problem of deriving proximity metrics without any prior knowledge of the positions of the WiFi access points based on WiFi fingerprints, that is, tuples of WiFi Access Points (AP) and respective received signal strength indicator (RSSI) values. Applications that benefit from proximity metrics are movement estimation of a single node over time, WiFi fingerprint matching for localization systems and attacks on privacy. Using a large-scale, real-world WiFi fingerprint data set consisting of 200,000 fingerprints resulting from a large deployment of wearable WiFi sensors, we show that metrics from related work perform poorly on real-world data. We analyze the cause for this poor performance, and show that imperfect observations of APs with commodity WiFi clients in the neighborhood are the root cause. We then propose improved metrics to provide such proximity estimates, without requiring knowledge of location for the observed AP. We address the challenge of imperfect observations of APs in the design of these improved metrics. Our metrics allow to derive a relative distance estimate based on two observed WiFi fingerprints. We demonstrate that their performance is superior to the related work metrics.This work has been supported by IMDEA Networks InstitutePrograma Oficial de Doctorado en Ingeniería TelemáticaPresidente: Francisco Barceló Arroyo.- Secretario: Paolo Casari.- Vocal: Marco Fior

Hybridisation of GNSS with other wireless/sensors technologies onboard smartphones to offer seamless outdoors-indoors positioning for LBS applications

Location-based services (LBS) are becoming an important feature on today’s smartphones (SPs) and tablets. Likewise, SPs include many wireless/sensors technologies such as: global navigation satellite system (GNSS), cellular, wireless fidelity (WiFi), Bluetooth (BT) and inertial-sensors that increased the breadth and complexity of such services. One of the main demand of LBS users is always/seamless positioning service. However, no single onboard SPs technology can seamlessly provide location information from outdoors into indoors. In addition, the required location accuracy can be varied to support multiple LBS applications. This is mainly due to each of these onboard wireless/sensors technologies has its own capabilities and limitations. For example, when outdoors GNSS receivers on SPs can locate the user to within few meters and supply accurate time to within few nanoseconds (e.g. ± 6 nanoseconds). However, when SPs enter into indoors this capability would be lost. In another vain, the other onboard wireless/sensors technologies can show better SP positioning accuracy, but based on some pre-defined knowledge and pre-installed infrastructure. Therefore, to overcome such limitations, hybrid measurements of these wireless/sensors technologies into a positioning system can be a possible solution to offer seamless localisation service and to improve location accuracy. This thesis aims to investigate/design/implement solutions that shall offer seamless/accurate SPs positioning and at lower cost than the current solutions. This thesis proposes three novel SPs localisation schemes including WAPs synchronisation/localisation scheme, SILS and UNILS. The schemes are based on hybridising GNSS with WiFi, BT and inertial-sensors measurements using combined localisation techniques including time-of-arrival (TOA) and dead-reckoning (DR). The first scheme is to synchronise and to define location of WAPs via outdoors-SPs’ fixed location/time information to help indoors localisation. SILS is to help locate any SP seamlessly as it goes from outdoors to indoors using measurements of GNSS, synched/located WAPs and BT-connectivity signals between groups of cooperated SPs in the vicinity. UNILS is to integrate onboard inertial-sensors’ readings into the SILS to provide seamless SPs positioning even in deep indoors, i.e. when the signals of WAPs or BT-anchors are considered not able to be used. Results, obtained from the OPNET simulations for various SPs network size and indoors/outdoors combinations scenarios, show that the schemes can provide seamless and locate indoors-SPs under 1 meter in near-indoors, 2-meters can be achieved when locating SPs at indoors (using SILS), while accuracy of around 3-meters can be achieved when locating SPs at various deep indoors situations without any constraint (using UNILS). The end of this thesis identifies possible future work to implement the proposed schemes on SPs and to achieve more accurate indoors SPs’ location