Combining Numerous Uncorrelated MEMS Gyroscopes for Accuracy Improvement Based on an Optimal Kalman Filter

In this paper, an approach to improve the accuracy of microelectromechanical systems (MEMS) gyroscopes by combining numerous uncorrelated gyroscopes is presented. A Kalman filter (KF) is used to fuse the output signals of several uncorrelated sensors. The relationship between the KF bandwidth and the angular rate input is quantitatively analyzed. A linear model is developed to choose suitable system parameters for a dynamic application of the concept. Simulation and experimental tests of a six-gyroscope array proved that the presented approach was effective to improve the MEMS gyroscope accuracy. The experimental results indicate that six identical gyroscopes with a noise density of 0.11°/s/√Hz and a bias instability of 62°/h can be combined to form a virtual gyroscope with a noise density of 0.03°/s/√Hz and a bias instability of 16.8°/h . The accuracy improvement is better than that of a simple averaging process of the individual sensors

Combining numerous uncorrelated MEMS gyroscopes for accuracy improvement based on an optimal Kalman filter

In this paper, an approach to improve the accuracy of microelectromechanical systems (MEMS) gyroscopes by combining numerous uncorrelated gyroscopes is presented. A Kalman filter (KF) is used to fuse the output signals of several uncorrelated sensors. The relationship between the KF bandwidth and the angular rate input is quantitatively analyzed. A linear model is developed to choose suitable system parameters for a dynamic application of the concept. Simulation and experimental tests of a six-gyroscope array proved that the presented approach was effective to improve the MEMS gyroscope accuracy. The experimental results indicate that six identical gyroscopes with a noise density of 0.11°/h and a bias instability of 62°/s can be combined to form a virtual gyroscope with a noise density of 0.03°/h and a bias instability of 16.8°/s. The accuracy improvement is better than that of a simple averaging process of the individual sensors. © 1963-2012 IEEE.status: publishe

Development of a Prototype Attitude Determination System (PADS) for High Altitude Research Balloons

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de Controle e Automação.Sistemas de determinação de atitude são um requisito para a maioria dos problemas de navegação e controle. Tradicionalmente, sistemas inerciais de orientação (AHRS) têm sido usados para prover dados de orientação com alta confiabilidade e precisão. No entanto, este tipo de solução geralmente usa sensores muito caros. Tendo em vista disso, um sistema de determinação de orientação (atitude) de baixo custo foi proposto no âmbito deste projeto usando componentes que podem ser encontrados nos grandes distribuidores de produtos electrónicos, os quais incluem um conjunto de giroscópios MEMS, clinômetros, um magnetômetro e uma câmera estrelar. Os sensores serão utilizados de forma hierárquica, o que significa que os sensores mais rápidos e menos precisos são atualizados pelos sensores mais lentos, porém mais precisos. Este projeto é parte de um programa da NASA lançado recentemente intitulado “Undergraduate Student Instrument Project Educational Flight Opportunity” (USIP), o qual promove uma cooperação entre universidades e pesquisadores da NASA no desenvolvimento de experimentos científicos de forma a estabelecer uma mão de obra que possua conhecimentos científicos e tecnológicos no estado da arte e capacidade de gerenciar grandes projetos. A maior parte deste projeto foi desenvolvido na universidade The Catholic University of America com ajuda de parceiros em NASA-Goddard Space Flight Center. Em um primeiro passo, as saídas de um conjunto de giroscópios foram fusionadas através de um filtro de Kalman, de forma a criar um giroscópio virtual com maior precisão do que um único giroscópio. Esta tecnologia tem provado ser muito eficaz e possui um grande potencial, tendo em vista que sensores MEMS estão experimentando uma rápida melhoria em termos de precisão, robustez, tamanho e resposta dinâmica. Em um segundo passo, um algoritmo baseado em um filtro de Kalman estendido, foi desenvolvido a fim de fusionar a orientação fornecida pelo giroscópio virtual, pelo magnetômetro e pelos clinômetros. A câmera estrelar é sensor o mais lento e fornece dados de orientação através da comparação da posição das estrelas. O sistema proposto destina-se a aplicações científicas para balões de alta altitude. Estes sistemas, tipicamente, precisam de sistemas de 5 apontamento na ordem de arco segundos. A carga científica foi projetada para manter todos os sensores e coletar dados em condições normais de voo. Além disso, um programa em MATLAB foi desenvolvido para processar todos os dados dos sensores e implementar os algoritmos de fusão de dados baseados no filtro de Kalman. A carga científica inclui todos os instrumentos responsáveis pela detecção de orientação, aquisição de dados e processamento, controle de temperatura e regulação de tensão para os componentes.Attitude determination systems are a requirement for most navigation and control problems. Traditionally, attitude and heading reference systems (AHRS) have been used to provide attitude with high reliability and accuracy. However, such solutions usually use very expensive sensors. Considering that, a low-cost attitude determination system has been proposed in the scope of this project using commercial-off-the-shelf components, which include a set of MEMS gyroscopes, clinometers, a magnetometer and a star-tracking camera. The sensors will be used in a hierarchical manner, which means that the faster and less accurate sensors are updated by the slower but more precise sensors. This project is part of a NASA program recently released untitled as "Undergraduate Student Instrument Flight Project Educational Opportunity" (USIP), which promotes the cooperation between universities and NASA leading scientists in the development of scientific experiments in order to establish high qualified workers that have state-of-art scientific and technological knowledge and ability to manage large projects. Most of this project was developed at The Catholic University of America with the help of partners in NASA-Goddard Space Center Flight. In a first step, the outputs from a set of gyroscopes were fused through a Kalman Filter to create a virtual gyroscope with higher accuracy than a single gyroscope. This technique has been proved to be very successful and has a great potential, considering that MEMS sensors are experiencing rapid improvements in terms of precision, robustness, size and dynamic response. In a second step, an Extended Kalman Filter algorithm was developed in order to fuse the attitude provided by the virtual gyroscope, clinometers and magnetometer. The Star Tracking Camera is the slowest sensor and provides absolute attitude data by comparing the position of the stars. The proposed system is intended to Scientific High Altitude Balloon applications, which typically require an accurate pointing system in the order of arc seconds. A payload was designed to hold all sensors and gather attitude data during flight conditions. In addition, a program in MATLAB was developed to process all the data from sensors and implement the Kalman Filter fusion algorithms. The 7 payload carries all the instruments responsible for the attitude sensing, data acquisition & processing, temperature control and voltage regulation