Thank to strong grow of MicroElectroMechanicalSystem (MEMS) technology, high performance and small size sensors are widely used in many areas such as landslide, navigation, mobile phones, etc. However, there are several kinds of errors are still existing in MEMS based sensors that need a carefully analyzing and calibration. | VNU Journal of Science: Mathematics – Physics, Vol. 32, No. 2 (2016) 34-42 Characterizing Stochastic Errors of MEMS – Based Inertial Sensors Pham Van Tang1,*, Tran Duc Tan2, Chu Duc Trinh2 1 2 Military Academy of Logistics, Hanoi, Vietnam VNU University of Engineering and Technology, 144 Xuan Thuy, Hanoi, Vietnam Received 14 April 2016 Revised 15 May 2016; Accepted 24 June 2016 Abstract: Thank to strong grow of MicroElectroMechanicalSystem (MEMS) technology, high performance and small size sensors are widely used in many areas such as landslide, navigation, mobile phones, etc. However, there are several kinds of errors are still existing in MEMS based sensors that need a carefully analyzing and calibration. By each year, the performances of commercial sensors are also improved. In this paper, we focused on characterizing the stochastic errors of accelerometers and gyroscopes integrated with a latest smart phone of Apple Inc. Iphone6+. The MP67B is a custom version of the InvenSense 6-Axis device (3-Axis gyroscope and 3-Axis accelerometer) made for Apple. This research will play an important step to decide whether we can create an Inertial Navigation System (INS) in the same device (. the smart phone, the users do not need to equip a single device for positioning application). The Allan variance method is exploited to analyze the stochastic errors in these sensors. Experiments proved that the main sources of errors in these sensors are white noises. The Iphone5 can operate as a lowcost solution of positioning and navigation device. Keywords: Sensor, MEMS, Stochastic Errors. 1. Introduction∗ Nowadays, thanks to the progress of MEMS technology, the inertial sensor become smaller, cheaper and more precise. They are widely used in the INS/GPS integrated systems. However, the measurement data of sensors are usually affected by different types of error sources, such as sensor noises, scale factor, and bias variations, etc. This sensors need testing and calibration
