Long-term drift error compensation method for gravity sensor

Abstract

The invention discloses a long-term drift error compensation method for a gravity sensor. The long-term drift error compensation method includes the steps that a real-time quantitative prediction model of a long-term drift error compensation system for gravity sensor data is established through sample gravity data, and after calculating, real-time quantitative prediction parameters and an input sequence and a target sequence of the sample gravity data required for modeling are obtained; then a gravity data target post-training sequence and an input post-training sequence after training are obtained; a high-precision target sequence after gravity data mean clustering treatment is obtained again; and the target sequence Y and the high-precision target sequence are identified to obtain estimation of random drift errors of the measured gravity data, and estimation of the drift errors is subtracted from the gravity data measured at the next moment to compensate the random drift errors in the measured gravity data. According to the long-term drift error compensation method for the gravity sensor, the problem of poor identification modeling of a nonlinear system by traditional real-time quantitative prediction modeling is solved, and the identification accuracy and robustness of a radial symmetric scalar tree network are improved.

Description

technical field [0001] The invention relates to a gravity sensor measurement technology, in particular to a long-term drift error compensation method of a gravity sensor. Background technique [0002] For the identification of nonlinear systems, traditional parameter estimation models have great difficulties in modeling long-term random drift of gravity sensors and error compensation. In contrast, tree-like networks show clear superiority. Since the tree-like network has the ability to approximate any nonlinear mapping through learning, it is used for modeling and identification of nonlinear systems without being limited by nonlinear models, and it is easy to implement in engineering. In addition, the traditional model trained with limited modeling data cannot include all data features, and the recognition accuracy is not high. The real-time quantitative predictive modeling is to analyze and model the measured gravity data itself, which is the embodiment of the data charac...

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Problems solved by technology

In addition, the traditional model trained with limited modeling data cannot include all data features, and the recognition accuracy is not high.

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