The noise generated by the fiber optic gyroscope's physical structure, optical components, and photodetectors has a huge impact on the accuracy and sensitivity of its output. Moreover, the fiber optic gyroscope itself is particularly sensitive to interference from the external environment. As long as there is a slight jitter from the outside world, the output of the FOG will change, resulting in errors between the data output by FOG and the real data. There are many external factors that will cause random drift to the fiber optic gyroscope, which is also a random error.

The actual output of the fiber optic gyroscope includes not only the horizontal component of the earth's rotation angular velocity and the constant drift of the gyroscope, but also the random drift and measurement noise of the gyroscope. The former is useful information required for north-seeking calculations, and the latter is a harmful signal that causes the North-seeking accuracy to decrease. In view of the output characteristics and error mechanism of the fiber optic gyroscope, as well as the measurement noise generated during the north seeking process, using effective digital signal processing methods for filtering and denoising is an effective measure to improve the north seeking accuracy. The wavelet denoising method is an adaptive denoising method that adaptively adjusts the detection bandwidth of the filter according to changes in the fiber optic gyro output signal, which can effectively suppress random errors in the fiber optic gyro output signal.

Wavelet analysis (or wavelet transform) is a new transformation analysis method. Its main advantage is that it can fully highlight the characteristics of certain aspects of the problem through transformation. Therefore, wavelet analysis has been successfully applied in many fields. In particular, the discrete digital algorithm of wavelet transform has been widely used in the transformation research of many problems.

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