Generic placeholder image

International Journal of Sensors, Wireless Communications and Control

Author(s): Amanpreet Kaur*, Archana Mantri and Vipan Kumar

DOI: 10.2174/2210327909666190409124143

DownloadDownload PDF Flyer Cite As
Design and Development of MEMS Sensors Based Inertial Navigation Systems for Aerial Vehicles: A Case Study

Page: [179 - 188] Pages: 10

  • * (Excluding Mailing and Handling)

Abstract

Background & Objective: MEMS sensors are rapidly growing as a sensing technology in all spheres of science and engineering. MEMS technology is playing an important role in avionics for miniaturization of systems and MEMS based Inertial Navigation System (INS) is one of the example. The situational awareness and performance of an aerial vehicle is computed with the help of an INS. This paper describes the case study for design of MEMS based low cost rugged INS for aerial vehicles. The 9 Degrees of Freedom (DOF) that are obtained from the sensors provide an inaccurate attitude information of aerial vehicles due to presence of external accelerations and the gyroscopic drifts in MEMS sensors. In order to overcome such problems and for the precise and reliable computation of orientation information, the error characteristics of accelerometers, magnetometers and gyroscopes have been combined into a sensor fusion algorithm with ‘Kalman Filter’ to compute the accurate orientation information. The processing has been done on STM32F407VGT6 microcontroller board. An accuracy of ± 0.1 degrees is achieved for Roll and Pitch and ± 1.0 degrees for Yaw have been obtained. The experimental results have been obtained in statically (keeping the device in a static position) and dynamically (rotating the device at different angles along roll, pitch and yaw axis) at room temperature of 22°C.

Methods: The design is different in a way that it has used a unique combination of trio MEMS sensors network consisting of FXOS8700CQ Accelerometer, FXAS21000 Gyroscope, FXOS8700CQ Magnetometer.

Results: The attitude estimation algorithm has been implemented on the 32-bit microcontroller. The information data is processed and displayed on 88.9 mm TFT-LCD through Graphical User Interface (GUI).

Keywords: Gyroscope, INS, MEMS, pitch, roll, yaw.

Graphical Abstract

[1]
Hu Y, Yan Y, Liang J, Wang L. A miniature, low-cost MEMS AHRS with application to posture control of robotic fish. IEEE Conf Proc IEEE Eng Med Biol Soc 2013 2013; 1392-5.
[http://dx.doi.org/10.1109/I2MTC.2013.6555642]
[2]
Allik B, Miller C, Piovoso MJ, Zurakowski R. Nonlinear estimators for censored data: A comparison of the EKF, the UKF and the Tobit Kalman filter. Am Control Conf 2015 2015; 5146-.
[http://dx.doi.org/10.1109/ACC.2015.7172142]
[3]
Wertz JR. Spacecraft attitude determination and control 2012; 1-184.
[4]
Noureldin A, Karamat TB, Eberts MD, El-Shafie A. Performance enhancement of MEMS-based INS/GPS integration for low-cost navigation applications. IEEE Trans Vehicular Technol 2009; 58(3): 1077-96.
[http://dx.doi.org/10.1109/TVT.2008.926076]
[5]
Guerrero-Castellanos JF, Madrigal-Sastre H, Durand S, Marchand N, Guerrero-Sánchez WF, Salmerón BB. Design and implementation of an attitude and heading reference system (AHRS). 8th international conference on electrical engineering, computing science and automatic control Mexico 2011.
[6]
Jimenez AR, Seco F, Prieto C, Guevara J. A comparison of pedestrian dead-reckoning algorithms using a low-cost MEMS IMU. IEEE International Symposium on Intelligent Signal Processing Budapest, Hungary. 2009.
[http://dx.doi.org/10.1109/WISP.2009.5286542]
[7]
De Bento M, Eissfeller B, Machado F. How to deal with low performance imus in an integrated navigation system: Step by step. 2010 5th ESA Workshop on Satellite Navigation Technologies and European Workshop on GNSS Signals and Signal Processing (NAVITEC). Noordwijk, Netherlands, 2010.
[http://dx.doi.org/10.1109/NAVITEC.2010.5708042]
[8]
Hu Y, Yan Y, Liang J, Wang L. A miniature, low-cost MEMS AHRS with application to posture control of robotic fish. 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Minneapolis, MN, USA, 2013.
[http://dx.doi.org/10.1109/I2MTC.2013.6555642]
[9]
Kaur A, Balsundar P, Kumar V, Mantrj A, Raj AN. MEMS based inertial navigation system: An exploratory analysis. 2016 5th International Conference on Wireless Networks and Embedded Systems (WECON). Rajpura, India 2016.
[http://dx.doi.org/10.1109/WECON.2016.7993454]
[10]
Aggarwal P. MEMS-based integrated navigation 2010; 1-189.
[11]
Denti E, Galatolo R, Schettini F. An AHRS based on a Kalman filter for the integration of inertial magnetometric and GPS data. 27th International Congress of the Aeronautical Sciences Nice, France. 2010.
[12]
Tazartes D. A historical approach on inertial navigation systems. International Symposium On Inertial Sensors And Systems Hiroshima, Japan. 2014.
[13]
Zhang DG, Song XD, Wang X, Ma YY. Extended AODV routing method based on Distributed Minimum Transmission (DMT) for WSN. AEU Int J Electron Commun 2015; 69(1): 371-81.
[http://dx.doi.org/10.1016/j.aeue.2014.10.009]
[14]
Zhang D, Li G, Zheng K, Ming X, Pan ZH. An energy-balanced routing method based on forward-aware factor for wireless sensor networks. IEEE Trans Industr Inform 2014; 10(1): 766-73.
[http://dx.doi.org/10.1109/TII.2013.2250910]
[15]
Zhang DG, Wang X, Song XD. New medical image fusion approach with coding based on SCD in wireless sensor network. J Electr Eng Technol 2015; 10(6): 2384-92.
[http://dx.doi.org/10.5370/JEET.2015.10.6.2384]
[16]
Zhang DG, Zheng K, Zhang T, Wang X. A novel multicast routing method with minimum transmission for WSN of cloud computing service. Soft Comput 2015; 19(7): 1817-27.
[http://dx.doi.org/10.1007/s00500-014-1366-x]
[17]
Zhang DG, Zheng K, Zhao DX, Song XD, Wang X. Novel Quick Start (QS) method for optimization of TCP. Wireless Networks Jan 22 1: 211-2.
[18]
Zhang DG, Wang X, Song XD, Zhang T, Zhu YN. A new clustering routing method based on PECE for WSN. EURASIP J Wirel Commun Netw 2015; 2015(1): 162.
[http://dx.doi.org/10.1186/s13638-015-0399-x]
[19]
Zhang DG, Zhu YN, Zhao CP, Dai WB. A new constructing approach for a weighted topology of wireless sensor networks based on local-world theory for the Internet Of Things (IOT). Comput Math Appl 2012; 64(5): 1044-55.
[http://dx.doi.org/10.1016/j.camwa.2012.03.023]
[20]
Zhang DG, Li WB, Liu S, Zhang XD. Novel fusion computing method for bio-medical image of WSN based on spherical coordinate. J Vibroeng 2016; 18(1): 522-38.
[21]
Ma Z, Zhang D, Liu S, Song J, Hou Y. A novel compressive sensing method based on SVD sparse random measurement matrix in wireless sensor network. Eng Comput 2016; 33(8): 2448-62.
[http://dx.doi.org/10.1108/EC-09-2015-0269]
[22]
Zhang DG, Liu S, Zhang T, Liang Z. Novel unequal clustering routing protocol considering energy balancing based on network partition & distance for mobile education. J Netw Comput Appl 2017; 88: 1-9.
[http://dx.doi.org/10.1016/j.jnca.2017.03.025]
[23]
Zhang DG, Zhou S, Tang YM. A low duty cycle efficient MAC protocol based on self-adaption and predictive strategy. Mob Netw Appl 2018; 23(4): 828-39.
[http://dx.doi.org/10.1007/s11036-017-0878-x]
[24]
Zhang DG, Niu HL, Liu S. Novel PEECR-based clustering routing approach. Soft Comput 2017; 21(24): 7313-23.
[http://dx.doi.org/10.1007/s00500-016-2270-3]
[25]
Zhang DG, Niu HL, Liu S, Ming XC. Novel positioning service computing method for WSN. Wirel Pers Commun 2017; 92(4): 1747-69.
[http://dx.doi.org/10.1007/s11277-016-3632-y]
[26]
Zhang DG, Zhang T, Zhang J, Dong Y, Zhang XD. A kind of effective data aggregating method based on compressive sensing for wireless sensor network. EURASIP J Wirel Commun Netw 2018; 2018(1): 159.
[http://dx.doi.org/10.1186/s13638-018-1176-4]
[27]
White E, Rios JA. FAA certification of a MEMS attitude and heading reference system. Proceedings of Institute of Navigation National Technical Meeting Salt Lake City, UT. 2002.
[28]
Li D, Landry R, Lavoie P. Low-cost MEMS sensor-based attitude determination system by integration of magnetometers and GPS: A real-data test and performance evaluation. 2008 IEEE/ION Position Location and Navigation Symposium Monterey, CA, USA. 2008.
[29]
Jiang Q, Zeng Y, Liu Q, Jing H. Attitude and heading reference system for quadrotor based on MEMS sensors. 2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control Harbin, China 2012.
[http://dx.doi.org/10.1109/IMCCC.2012.257]
[30]
Wang Y, Soltani M. A nonlinear attitude estimator for attitude and heading reference systems based on MEMS sensors. 2016 SICE International Symposium on Control Systems (ISCS) Nagoya, Japan. 2016.
[http://dx.doi.org/10.1109/SICEISCS.2016.7470176]
[31]
Sheet: FXOS8700CQ - Freescale Semiconductor- NXP Multifunction “6-axis sensor with integrated linear accelerometer and magnetometer”. Available from:. https://cache.freescale.com/files/sensors/doc/data_sheet/FXOS8700CQ.pdf
[32]
Sheet: FXAS21000 Xtrinsic - Freescale Semiconductor, “3 Axis Gyroscope”. Available from: www.farnell.com/datasheets/1793967.pdf
[33]
Kang CW, Park CG. Euler angle based attitude estimation avoiding the singularity problem.IFAC Proc 2011; 44(1): 2096-102.
[http://dx.doi.org/10.3182/20110828-6-IT-1002.01993]
[34]
Young AD. Comparison of orientation filter algorithms for realtime wireless inertial posture tracking. Sixth International Workshop on Wearable and Implantable Body Sensor Networks Berkeley, CA, USA. 2009.
[http://dx.doi.org/10.1109/BSN.2009.25]
[35]
Lee JK, Park EJ, Robinovitch SN. Estimation of attitude and external acceleration using inertial sensor measurement during various dynamic conditions. IEEE Trans Instrum Meas 2012; 61(8): 2262-73.
[http://dx.doi.org/10.1109/TIM.2012.2187245] [PMID: 22977288]
[36]
Brückner HP, Spindeldreier C, Blume H. Modification and fixedpoint analysis of a kalman filter for orientation estimation based on 9D inertial measurement unit data. In 35th Annual International Conference of the IEEE Engi-neering in Medicine and Biology Society. Osaka, Japan, 2013.
[37]
Zihajehzadeh S, Loh D, Lee M, Hoskinson R, Park EJ. A cascaded two-step kalman filter for estimation of human body segment orientation using MEMS-IMU. In 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Chicago, IL, USA, 2014.
[http://dx.doi.org/10.1109/EMBC.2014.6945062]
[38]
Pakki K, Chandra B, Kothari M, Gu DW, Postlethwaite I. Multi-sensor state estimation using SDRE information filters. IFAC Proc Vol 2014; 47(3): 9141-6.
[http://dx.doi.org/10.3182/20140824-6-ZA-1003.01998]