Effective accelerometer test beds for output enhancement of an inertial navigation system

This paper is submitted upon a research to enhance the output of an accelerometer widely used in inertial navigation units. In our study we introduce relatively simple and effective test beds to collect accurate and diverse reference data. We also carried out calibration runs with a highly accurate electromechanical shaking table. The proposed test beds compare well with sophisticated counterparts. The collected data is used to train artificial neural networks (ANNs) which would improve the accelerometer outputs by estimating the reference data from the actual sensor outputs. The ANN performance is compared with classic low pass filtering methods to provide a relative performance criterion. In this paper we focus on test beds rather than to give the details of the whole study. The test beds introduced in this research can be used for acquiring reference data for implementation of other different filter methods as well.