Analisis Metode Pendeteksian Langkah Kaki pada Pedestrian Ddead Reckoning

Pedestrian Dead Reckoning merupakan salah satu bagian dari sistem navigasi personal yang diterapkan untuk pejalan kaki. Posisi ditentukan oleh posisi sebelumnya, jarak yang ditempuh dan arah melangkah. Deteksi langkah merupakan salah satu faktor penting pada sistem navigsi PDR. Jarak yang ditempuh dapat ditentukan dengan mengetahui jumlah langkah ketika berjalan dikalikan dengan jarak untuk satu kali melangkah yang dianggap konstan. Banyak penelitian yang telah dilakukan untuk mendeteksi langkah manusia dan dalam tugas akhir ini akan mengulas tentang metode pendeteksian langkah kaki manusia. Pendeteksian langkah dilakukan dengan melihat nilai sensor akselerometer ketika berjalan. Sensor akselerometer yang digunakan adalah sensor 3 axis HITACHI H48C yang dipasang pada sepatu. Nilai percepatan ketiga axis yang terbaca oleh sensor ketika berjalan kemudian dikirim ke netbook. Nilai percepatan ketiga axis tersebut diolah sehingga didapatkan sinyal magnitude, sinyal energi, sinyal product, dan sinyal sum. Pendeteksian langkah kaki dilakukan dengan menganalisis sinyal yang didapatkan menggunakan pendekatan nilai threshold dan nilai variansi. Berdasarkan pengujian dan analisis yang dilakukan dapat diketahui bahwa fase stance merupakan fase yang paling mudah dideteksi karena pada fase stance sinyal akan stabil pada rentang nilai tertentu. Penggunaan nilai variansi pada pendeteksian langkah berguna untuk membuat sinyal pada fase stance akan berada pada nilai nol. Pendeteksian menggunakan nilai variansi memiliki tingkat keberhasilan lebih besar dibandingkan dengan sinyal aslinya. Kata Kunci : stance, swing, magnitude, threshold, variansi

Indoor location systems in emergency scenarios - A Survey

Indoor location data are critical in emergency situations. Command centers need to monitor their operational forces. Rescuers need to find potential victims to carry proper care and the building’s occupants need to find the way for fast evacuation. Despite the growing body of research in indoor location, no technique is considered appropriate for different situations. Furthermore, few studies have analyzed the applicability of these techniques in an emergency setting, which has particular characteristics. This survey reviews works in indoor location applied to emergency scenarios, analyzing their applicability in relation to existing requirements in these types of situations

Nine-Axis IMU sensor fusion using the AHRS algorithm and neural networks

This paper presents data processing method for Attitude Heading and Reference System (AHRS) based on Artificial Neural Networks (ANN). The system consist of MEMS (Micro Electro-Mechanical Systems) based on Inertial Measurement Unit (IMU) consisting of tri-axis gyroscopes, accelerometers and magnetometers providing three dimensional linear accelerations and angular rates. Training data was generated by simulation fusion of samples collected during the flight of Quadcopter. The presented results shows proper functioning of the neural network. Moreover, the presented system provide the possibility to easily add other sensors e.g. GPS, in order to achieve better performance

Data Fusion Algorithms for Multiple Inertial Measurement Units

A single low cost inertial measurement unit (IMU) is often used in conjunction with GPS to increase the accuracy and improve the availability of the navigation solution for a pedestrian navigation system. This paper develops several fusion algorithms for using multiple IMUs to enhance performance. In particular, this research seeks to understand the benefits and detriments of each fusion method in the context of pedestrian navigation. Three fusion methods are proposed. First, all raw IMU measurements are mapped onto a common frame (i.e., a virtual frame) and processed in a typical combined GPS-IMU Kalman filter. Second, a large stacked filter is constructed of several IMUs. This filter construction allows for relative information between the IMUs to be used as updates. Third, a federated filter is used to process each IMU as a local filter. The output of each local filter is shared with a master filter, which in turn, shares information back with the local filters. The construction of each filter is discussed and improvements are made to the virtual IMU (VIMU) architecture, which is the most commonly used architecture in the literature. Since accuracy and availability are the most important characteristics of a pedestrian navigation system, the analysis of each filter’s performance focuses on these two parameters. Data was collected in two environments, one where GPS signals are moderately attenuated and another where signals are severely attenuated. Accuracy is shown as a function of architecture and the number of IMUs used

RSS-Based Fusion of UWB and WiFi-Based Ranging for Indoor Positioning

Publisher Copyright: © 2021 CEUR-WS. All rights reserved.WiFi positioning with estimated ranges using Round Trip Time (RTT) measurements based on IEEE 802.11 Wireless Local Area Network (WLAN) has become well known since Fine Timing Measurement (FTM) protocol has been characterized. However, the multipath effect is one of the barriers to accurate time-based range measurement. On the other hand, Ultra Wide Band (UWB)-based range measurement has fair resistance to multipath effects but its accuracy is highly dependant on the orientation of the antennas in the transmitter and the receiver and its transmit power is also limited due to the applied regulations. This paper utilizes a Received Signal Strength (RSS)-based fusion of both UWB and WiFi-based range measurements to increase the indoor positioning accuracy. The proposed method takes the advantage of WiFi FTM protocol as well as Two-Way Ranging (TWR) for UWB devices. The empirical range measurement campaign is done at the University of Helsinki premises. Test points with known positions are considered as the ground truth to evaluate the results. The outcome proves that fusing UWB and WiFi ranges for indoor positioning, improves the accuracy in comparison with using the UWB or WiFi alone.Peer reviewe

It's the Human that Matters: Accurate User Orientation Estimation for Mobile Computing Applications

Ubiquity of Internet-connected and sensor-equipped portable devices sparked a new set of mobile computing applications that leverage the proliferating sensing capabilities of smart-phones. For many of these applications, accurate estimation of the user heading, as compared to the phone heading, is of paramount importance. This is of special importance for many crowd-sensing applications, where the phone can be carried in arbitrary positions and orientations relative to the user body. Current state-of-the-art focus mainly on estimating the phone orientation, require the phone to be placed in a particular position, require user intervention, and/or do not work accurately indoors; which limits their ubiquitous usability in different applications. In this paper we present Humaine, a novel system to reliably and accurately estimate the user orientation relative to the Earth coordinate system. Humaine requires no prior-configuration nor user intervention and works accurately indoors and outdoors for arbitrary cell phone positions and orientations relative to the user body. The system applies statistical analysis techniques to the inertial sensors widely available on today's cell phones to estimate both the phone and user orientation. Implementation of the system on different Android devices with 170 experiments performed at different indoor and outdoor testbeds shows that Humaine significantly outperforms the state-of-the-art in diverse scenarios, achieving a median accuracy of $15^\circ$ averaged over a wide variety of phone positions. This is $558\%$ better than the-state-of-the-art. The accuracy is bounded by the error in the inertial sensors readings and can be enhanced with more accurate sensors and sensor fusion.Comment: Accepted for publication in the 11th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (Mobiquitous 2014

Performance Study of MTx Motion Tracker for Indoor Geolocation

The objective of this project is to explore the characteristics of inertial systems using the MTx Motion Tracker device for indoor geolocation. We develop a testbed for performance evaluations to determine the precision of the MTx Motion Tracker. This test bed is based on conducted experiments carried out to evaluate the operation of the device. In each experiment, we collect raw acceleration and relative angles from the device and implemented our algorithms through MATLAB software to evaluate its performance

INDOOR LOCALIZATION AND TRACKING: METHODS, TECHNOLOGIES AND RESEARCH CHALLENGES

The paper presents a comprehensive survey of contemporary methods, technologies and systems for localization and tracking of moving objects in indoor environment and gives their comparison according to various criteria, such as accuracy, privacy, scalability and type of location data. Some representative examples of indoor LBS applications available on the market are presented that are based on reviewed localization technologies. The prominent research directions in this domain are categorized and discussed

Wireless Positioning and Tracking for Internet of Things in GPS-denied Environments

Wireless positioning and tracking have long been a critical technology for various applications such as indoor/outdoor navigation, surveillance, tracking of assets and employees, and guided tours, among others. Proliferation of Internet of Things (IoT) devices, the evolution of smart cities, and vulnerabilities of traditional localization technologies to cyber-attacks such as jamming and spoofing of GPS necessitate development of novel radio frequency (RF) localization and tracking technologies that are accurate, energy-efficient, robust, scalable, non-invasive and secure. The main challenges that are considered in this research work are obtaining fundamental limits of localization accuracy using received signal strength (RSS) information with directional antennas, and use of burst and intermittent measurements for localization. In this dissertation, we consider various RSS-based techniques that rely on existing wireless infrastructures to obtain location information of corresponding IoT devices. In the first approach, we present a detailed study on localization accuracy of UHF RF IDentification (RFID) systems considering realistic radiation pattern of directional antennas. Radiation patterns of antennas and antenna arrays may significantly affect RSS in wireless networks. The sensitivity of tag antennas and receiver antennas play a crucial role. In this research, we obtain the fundamental limits of localization accuracy considering radiation patterns and sensitivity of the antennas by deriving Cramer-Rao Lower Bounds (CRLBs) using estimation theory techniques. In the second approach, we consider a millimeter Wave (mmWave) system with linear antenna array using beamforming radiation patterns to localize user equipment in an indoor environment. In the third approach, we introduce a tracking and occupancy monitoring system that uses ambient, bursty, and intermittent WiFi probe requests radiated from mobile devices. Burst and intermittent signals are prominent characteristics of IoT devices; using these features, we propose a tracking technique that uses interacting multiple models (IMM) with Kalman filtering. Finally, we tackle the problem of indoor UAV navigation to a wireless source using its Rayleigh fading RSS measurements. We propose a UAV navigation technique based on Q-learning that is a model-free reinforcement learning technique to tackle the variation in the RSS caused by Rayleigh fading