Fiber optic gyro noise reduction based on hybrid CEEMDAN-LWT method

Abstract

Abstract The noise involved in drift signal of fiber optic gyroscope (FOG) mostly comes from electronic component, detection circuit and variable working environment. Gaussian white noise and fractional noise submerged in FOG output is difficult to be eliminated by conventional methods because of the non-stationary characteristics. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is a novel nonlinear and non-stationary signal processing method, which is exploited in FOG denoising. Lifting wavelet transform (LWT) technology is employed to combine with CEEMDAN method to expedite the computing efficiency and improve filtering accuracy, so that a hybrid CEEMDAN-LWT-based model is achieved. Comparison analysis with other filtering methods based on empirical mode decomposition (EMD) and its improved version is done. And ensemble empirical mode decomposition (EEMD) method combined with LWT considered as EEMD-LWT method is also applied as compared. Experimental analysis results show that new hybrid method outperforms other EMD-based filtering methods. The new method requires only 11.3% sifting iterations of the EEMD-LWT method. Meanwhile, the rate white noise, bias instability and quantization noise buried in FOG output signal decreases from 0 . 0031 ° / h , 0 . 0352 ° / h and 0 . 5412 ° to 0 . 0005 ° / h , 0 . 0056 ° / h and 0 . 0231 ° , respectively. Furthermore, de-trended fluctuation analysis (DFA) algorithm is employed to evaluate the effectiveness of hybrid method for the FOG signal filtering.