A Study on Indoor Positioning using 3-Dimensionalization Geomagnetic Fingerprint

Indoor positioning based on geomagnetism has been actively studied because of the stable signal and high resolution positioning accuracy even when the time has elapsed. Because the geomagnetic signal can vary according to changes in azimuth, large positioning errors may occur, even from the same position. Therefore, this thesis proposes a fingerprint-based indoor positioning algorithm that fuses 2-Dimensional magnetic vectors and yaw-axis correction techniques. In the proposed 3-Dimensional system, the curvature is less biased heavily by using the Ellipse Coefficient Map of the geomagnetism based on the normalized linear least squares method even when database size is reduced, and the accuracy of positioning is improved by applying the geomagnetic signal equalization method. To verify the validity of the proposed algorithm in general indoor spaces of 48m × 30m, the results of the proposed method are compared with results obtained existing research based on geomagnetism intensity. The results show that the positioning accuracy is improved by 62.14% and the error distance is reduced by 3.98m.|지자기기반 실내위치인식은 시간이 경과되더라도 안정적인 신호 및 높은 분해능으로 측위 정확성이 높기 때문에 활발히 연구되고 있다. 그러나 동일한 위치에서도 방위 변화에 따른 지자기 신호가 일정하지 않기 때문에 위치 오차가 발생한다. 본 논문에서는 Fingerprint기반 2차원 자기벡터 및 yaw축 보정을 적용한 실내위치인식 알고리즘을 제안한다. 제안한 3차원화 시스템은 정규화 선형 최소자승법을 적용한 지자기의 Ellipse Coefficient Map을 설계함으로써 데이터베이스가 간소화됨에도 곡률편향이 거의 없고 지자기 신호 평활화 기법을 적용함으로써 위치인식 정확도를 향상시켰다. 제안한 알고리즘의 타당성을 검증하기 위하여 48m × 30m의 일반적인 실내공간에서 기존 지자기 세기기반 방식과 제안한 방법에 대한 결과를 비교 및 분석하였다. 실험 결과 위치 인식 정확도는 62.14% 개선하였고 오차거리는 3.98m 감소하였다.Abstract ⅳ 제 1 장 서 론 01 제 2 장 관련이론 06 2.1 지구자기장 06 2.2 자기벡터기반 방위각 획득 07 2.3 최소자승법 11 2.4 Fingerprint 측위 기법 13 제 3 장 제안한 실내 위치 인식 방법 15 3.1 시스템 구조 15 3.2 3차원화 Training phase 16 3.3 3차원화 Positioning phase 20 제 4 장 실험 및 결과 23 4.1 실험 환경 23 4.2 실험 결과 분석 27 제 5 장 결 론 38 참 고 문 헌 39Maste

A WiFi RSSI Ranking Fingerprint Positioning System and Its Application to Indoor Activities of Daily Living Recognition

WiFi RSSI (Received Signal Strength Indicators) seem to be the basis of the most widely used method for Indoor Positioning Systems (IPS) driven by the growth of deployed WiFi Access Points (AP), especially within urban areas. However, there are still several challenges to be tackled: its accuracy is often 2-3m, it’s prone to interference and attenuation effects, and the diversity of Radio Frequency (RF) receivers, e.g., smartphones, affects its accuracy. RSSI fingerprinting can be used to mitigate against interference and attenuation effects. In this paper, we present a novel, more accurate, RSSI ranking-based method that consists of three parts. First, an AP selection based on a Genetic Algorithm (GA) is applied to reduce the positioning computational cost and increase the positioning accuracy. Second, Kendall Tau Correlation Coefficient (KTCC) and a Convolutional Neural Network (CNN) are applied to extract the ranking features for estimating locations. Third, an Extended Kalman filter (EKF) is then used to smooth the estimated sequential locations before Multi-Dimensional Dynamic Time Warping (MD-DTW) is used to match similar trajectories or paths representing ADLs from different or the same users that vary in time and space In order to leverage and evaluate our IPS system, we also used it to recognise Activities of Daily Living (ADL) in an office like environment. It was able to achieve an average positioning accuracy of 1.42m and a 79.5% recognition accuracy for 9 location-driven activities

A review of smartphones based indoor positioning: challenges and applications

The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor positioning. This article is dedicated to review the most recent and interesting smartphones based indoor navigation systems, ranging from electromagnetic to inertia to visible light ones, with an emphasis on their unique challenges and potential real-world applications. A taxonomy of smartphones sensors will be introduced, which serves as the basis to categorise different positioning systems for reviewing. A set of criteria to be used for the evaluation purpose will be devised. For each sensor category, the most recent, interesting and practical systems will be examined, with detailed discussion on the open research questions for the academics, and the practicality for the potential clients

An Investigation of Indoor Positioning Systems and their Applications

PhDActivities of Daily Living (ADL) are important indicators of both cognitive and physical well-being in healthy and ill humans. There is a range of methods to recognise ADLs, each with its own limitations. The focus of this research was on sensing location-driven activities, in which ADLs are derived from location sensed using Radio Frequency (RF, e.g., WiFi or BLE), Magnetic Field (MF) and light (e.g., Lidar) measurements in three different environments. This research discovered that different environments can have different constraints and requirements. It investigated how to improve the positioning accuracy and hence how to improve the ADL recognition accuracy. There are several challenges that need to be addressed in order to do this. First, RF location fingerprinting is affected by the heterogeneity smartphones and their orientation with respect to transmitters, increasing the location determination error. To solve this, a novel Received Signal Strength Indication (RSSI) ranking based location fingerprinting methods that use Kendall Tau Correlation Coefficient (KTCC) and Convolutional Neural Networks (CNN) are proposed to correlate a signal position to pre-defined Reference Points (RPs) or fingerprints, more accurately, The accuracy has increased by up to 25.8% when compared to using Euclidean Distance (ED) based Weighted K-Nearest Neighbours Algorithm (WKNN). Second, the use of MF measurements as fingerprints can overcome some additional RF fingerprinting challenges, as MF measurements are far more invariant to static and dynamic physical objects that affect RF transmissions. Hence, a novel fast path matching data algorithm for an MF sensor combined with an Inertial Measurement Unit (IMU) to determine direction was researched and developed. It can achieve an average of 1.72 m positioning accuracy when the user walks far fewer (5) steps. Third, a device-free or off-body novel location-driven ADL method based upon 2D Lidar was investigated. An innovative method for recognising daily activities using a Seq2Seq model to analyse location data from a low-cost rotating 2D Lidar is proposed. It provides an accuracy of 88% when recognising 17 targeted ADLs. These proposed methods in this thesis have been validated in real environments.Chinese Scholarship Counci