Stochastic characteristics of temperature-dependent MEMS-based inertial sensor error

The GPS/INS integrated system has been widely used in hydrographic surveying for positioning and orientation purposes. However, existing systems use high-end inertial sensors, which are very expensive and bulky. To overcome the cost and size of inertial sensors, Micro-Electro-Mechanical System (MEMS)-based inertial technology is proposed. A major drawback of low-coast MEMS-based inertial sensors however, is that their output signals are contaminated by high-level noise. In addition, because of their miniature size, MEMS sensors are very sensitive to ambient environmental conditions, (e.g. temperature), which cause the sensor to exhibit large, rapidly changing errors. Unless the high frequency noise component is suppressed, and accurate temperature-dependent stochastic model is built, optimization the filtering methodology cannot be achieved. This paper examines the effect of varying the temperature points on the MEMS inertial sensors' noise models using Allan variance and Least-Squares Spectral Analysis (LSSA). Allan variance is a method of representing root mean square random drift error as a function of averaging times. LSSA is an alternative to the classical Fourier methods and has been applied successfully by a number of researchers in the study of the noise characteristics of experimental series. Static data sets are collected under different temperature points using two MEMS-based IMUs, namely MotionPakII and Crossbow AHRS300CC. The performance of different MEMS inertial sensors is predicted from the Allan variance estimation results at different temperature points and the LSSA is used to study the noise characteristics and define the sensors' stochastic model parameters. It is shown that the stochastic characteristics of MEMS-based inertial sensors can be identified using Allan variance estimation and LSSA and the sensors' stochastic model parameters are temperature-dependent. Also, the Kaiser Window FIR low pass filter is used to investigate the effect of de-noising stage on the stochastic model. It is shown that the stochastic model is also dependent on the chosen cut-off frequency.