Inertial systems design and performance analysis critically depend on the development of the inertial sensors test and analysis. The requirements for the wide use of electromechanical inertial sensors necessitate the exploitation of the test and analysis method that allows optimal elimination of the disturbance in the test over the whole frequency band of interest. A method based on identification using adaptive noise cancellation is proposed to process the sensors’ signal. In theory, there is no correlation between the signal components reflecting the sensors noise and disturbance from the test environment. The semi-physical simulation based on the identification of sensor ontology is established to pick up the noise in the test as the noise background. The normalized least-mean-squares (NLMS) algorithm is applied to abstract the sensors output as adaptive noise canceller. The experiments of dynamically tuned gyroscopes and quartz accelerometers demonstrate the validity of the method, besides the results of tests in different bandwidths after denoising are consistent in accuracy level. Additionally, its implementation in different grades of inertial sensors is presented to support the effectiveness of the algorithm and the importance of bandwidth in the test.
CraigR (1972) Theory of operation of a two-axis-rate gyro. IEEE Transactions on Aerospace and Electronic Systems26(5): 722–731.
3.
El-SheimyNHouHNiuX (2008) Analysis and modeling of inertial sensors using Allan variance. IEEE Transactions on Instrumentation and Measurement57(1): 140–149.
4.
GalvaoRKHHadjiloucasS (2005) Subspace system identification framework for the analysis of multimode propagation of THz-transient signals. Measurement Science & Technology16(5): 1037–1053.
5.
GreenhallCA (1998) Spectral ambiguity of allan variance. IEEE Transactions on Instrumentation and Measurement47(3): 623–627.
6.
GuerrierSSkaloudJSteblerY. (2012) A framework for inertial sensor calibration using complex stochastic error models. Position Location And Navigation Symposium (Plans). 2012 IEEE/ION: 849–861.
7.
GuerrierSSkaloudJSteblerY. (2013) Wavelet-variance-based estimation for composite stochastic processes. Journal of the American Statistical Association108(503): 1021–1030.
8.
GuptaNMehraR (1974) Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations. IEEE Transactions on Automatic Control19(6): 774–783.
9.
HaykinS (2002) Adaptive filter theory. Upper Saddle River, NJ: Prentice-Hall.
10.
HernandezW (2001) Improving the response of an accelerometer by using optimal filtering. Sensors and Actuators A: Physical88(3): 198–208.
11.
HernandezWVicenteJD (2009) Improving the response of accelerometers for automotive applications by using LMS adaptive filters. Sensors10(1): 313–329.
12.
HouHEl-SheimyN (2001) Inertial sensors errors modeling using Allan variance. Proceedings of the 16th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS/GNSS 2003), pp. 2860–2867.
13.
IEEE Std 154–2005 (2005) IEEE Recommended Practice for Inertial Sensor Test Equipment, Instrumentation, Data Acquisition, and Analysis.
14.
IEEE Std 952–1997 (1998) IEEE Standard Specification Format Guide and Test Procedure for Single-Axis Interferometric Fiber Optic Gyros.
15.
JafaripanahMAl-HashimiBMWhiteNM (2005) Application of analog adaptive filters for dynamic sensor compensation. IEEE Transactions on Instrumentation and Measurement54(1): 245–251.
16.
LiJFangJ (2013) Not fully overlapping Allan variance and total variance for inertial sensor stochastic error analysis. IEEE Transactions on Instrumentation and Measurement62(10): 2659–2672.
17.
MohammedESpirosP (2008) Calibration and stochastic modelling of inertial navigation sensor errors. Journal of Global Positioning Systems7(2): 170–182.
18.
NiNChanCC (2008) Enhancing the measurement accuracy of a cavity-enhanced fiber chemical sensor by an adaptive filter. Measurement Science & Technology19(11): 115–203.
19.
NikkhahAAHeydariPKhaloozadehH. (2009) Gyroscope random drift modeling using neural networks fuzzy neural and traditional time-series methods. JAST6(1): 37–44.
20.
OlivaresAGorrizJMRamirezJ. (2011) Accurate human limb angle measurement: Sensor fusion through Kalman, least mean squares and recursive least-squares adaptive filtering. Measurement Science & Technology22(2): 025801-1-15.
21.
PengFLZhangZBGouXM. (2014) Motion artifact removal from photoplethysmographic signals by combining temporally constrained independent component analysis and adaptive filter. Biomedical Engineering Online13: 50. Available at: http://www.biomedcentral.com/content/pdf/1475-925X-13-50.pdf
22.
RahmanMZUKarthikGVSFathimaSY. (2013) An efficient cardiac signal enhancement using time–frequency realization of leaky adaptive noise cancelers for remote health monitoring systems. Measurement46(10): 3815–3835.
23.
SabatiniA (2006) A wavelet-based bootstrap method applied to inertial sensor stochastic error modelling using the Allan variance. Measurement Science & Technology17: 2980–2988.
24.
SkaloudJSchwarzKP (2000) Accurate orientation for air borne mapping systems. Photogrammetric Engineering and Remote Sensing66(4): 393–401.
25.
SkaloudJBrutonAMSchwarzKP (1999) Detection and filtering of short-term noise in inertial sensors. Journal of the Instituted of Navigation46(2): 97–107.
26.
SongJWLeeJGKangT (2002) Digital rebalance loop design for a dynamically tuned gyroscope using H2 methodology. Control Engineering Practice10(10): 1127–1140.
27.
SteblerYGuerrierSSkaloudJ. (2011) Constrained expectation-maximization algorithm for stochastic inertial error modeling: Study of feasibility. Measurement Science and Technology22(8): 085204-1-12.
28.
ThenozhiSYuWGarridoR (2013) A novel numerical integrator for velocity and position estimation. Transactions of the Institute of Measurement and Control35(6): 824–833.
29.
TianWWuSCZhouZB (2012) High resolution space quartz-flexure accelerometer based on capacitive sensing and electrostatic control technology. Review of Scientific Instruments83(9): 095002-1-6.
30.
VaccaroRJZakiAS (2012) Statistical modeling of rate gyros. IEEE Transactions on Instrumentation and Measurement61(3): 673–684.
31.
XingZGebre-EgziabherD (2008) Modeling and bounding low cost inertial sensor errors. Position, Location and Navigation Symposium, 2008 IEEE/ION, pp. 1122–1132.
32.
XuGTianWJinZ (2006) AGO–SVM drift modelling method for a dynamically tuned gyroscope. Measurement Science & Technology17(1): 161–167.
33.
YousefiRNouraniMPanahiI (2012) Adaptive cancellation of motion artifact in wearable biosensors. Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE, August 28 2012–September 1 2012, pp. 2004–2008.
34.
ZhangYDuCMuQ (2013) Random error modelling and compensation of accelerometer in airborne remote sensing stabilized platform. Transactions of the Institute of Measurement and Control35(4): 503–509.
