2 November 2004 A neural network model of optical gyros drift errors with application to vehicular navigation
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Abstract
Inertial navigation systems (INS) incorporating three mutually orthogonal accelerometers and three mutually orthogonal gyroscopes are integrated with global positioning systems (GPS) to provide reliable and accurate positioning information for vehicular navigation. Because of their high reliability and accuracy, ring laser gyroscopes (RLG) and fiber optic gyroscopes (FOG) are usually utilized inside most of the present INS. However, bias drift at the output of these optical gyroscopes may deteriorate the performance of the overall INS/GPS navigation system. This paper introduces a method to enhance the performance of optical gyros in two phases. The first phase utilizes wavelet multi-resolution analysis to band limit the gyro measurement and improves its signal-to-noise ratio. The second phase employs radial-basis function (RBF) neural networks to predict drift errors. The drift model provided by the RBF network is established using the gyro raw measurement and time as inputs and provides the drift error at its output. The RBF neural networks are utilized in this study since they generally have simpler architecture and faster training procedure than other neural network types. The proposed method is applied to E-core 2000 FOG (KVH Industries Inc., Rhode Island, USA).
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Rashad Sharaf, Aboelmagd Noureldin, "A neural network model of optical gyros drift errors with application to vehicular navigation", Proc. SPIE 5558, Applications of Digital Image Processing XXVII, (2 November 2004); doi: 10.1117/12.558493; https://doi.org/10.1117/12.558493
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