Drift suppression method based on signal stability detection and adaptive Kalman filter for NMR sensor

Abstract

The small volume, high precision, and low cost of Nuclear Magnetic Resonance (NMR) sensors make them one of the best choices for future miniaturized and chip-scale Inertial Navigation System (INS). Due to technical and process limitations, NMR sensors inevitably exhibit random drift. To suppress these errors, a drift suppression method based on Signal Stability Detection and Adaptive Kalman Filter (SSD-AKF) for NMR sensors is proposed. Firstly, a state space model for the Kalman filter is established based on an Auto Regressive Moving Average (ARMA) sequence model. Secondly, to address the issue of reduced filtering accuracy caused by unstable signal noise in innovation-based AKF, an adaptive filtering method aided by a signal stability detection is proposed. The proposed method utilizes the standard deviation of prior information to assess the stability of the signal. Based on this assessment, the adaptive filter adjusts the gain matrix, ultimately enhancing the stability of the filter. The dynamic experimental results show that the proposed method can effectively improve filter performance and reduce sensor drift.