{'fraction_non_alphanumeric': 0.021967526265520534, 'fraction_numerical': 0.009551098376313277, 'mean_word_length': 4.887640449438202, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Combining numerous gyroscopes for accuracy improvement using autoregressive process for rate signal modeling

A signal processing technique for gyro array is presented in this paper to reduce noise and improve the accuracy of micro-electro-mechanical system (MEMS) gyroscope. To improve the dynamic performance, the true rate is modeled by an autoregressive (AR) model instead of a random walk. According to the analysis results of the Allan variance, the measurement model is simplified and a mathematical model is established. Based on this model, a novel Kalman filter (KF) with modified estimation process for combining outputs of a gyroscope array is designed. The performance and the affect factors of the gyro array are analyzed by using a steady-state covariance. The experimental results indicate that the RMSE of the gyroscopes can be reduced from 0.4925 deg/s to 0.0034 deg/s by an array composed of six gyroscopes in static test. The dynamic test is also discussed and the validity of the proposed modeling and fusion method is proved.