Fiber-optic gyroscope output signal filtering method

Abstract

The invention relates to a fiber-optic gyroscope output signal filtering method. According to the method, a gyroscope output signal at a current moment is estimated by multiplying a gyroscope output signal at a previous moment by a corresponding weight; on the basis of the initial value, the weight value is obtained by continuously calculating the difference value between the gyro signal collected at the current moment and the estimated value and iteratively obtaining the weight value. The method is high in real-time performance, small in time delay and short in initial transition process, a random noise model can be accurately established, noise with different time domain random characteristics can be eliminated, and meanwhile high-frequency noise in signals can be better removed.

Description

Technical Field

[0001] The invention relates to the technical field of digital filtering, and in particular to a method for filtering an output signal of a fiber optic gyroscope. Background Art

[0002] Fiber optic gyroscope is a new type of all-solid-state angular velocity sensor developed based on the Sagnac effect. In an ideal closed-loop state, its output signal is linearly related to the angular velocity of rotation. However, in actual optical fibers, factors such as backscattering and environmental changes will cause random noise and drift in the gyroscope output signal. Therefore, output signal processing methods must be used to suppress random noise and drift. The main methods for processing gyroscope output signals are:

[0003] 1) Model the random noise and drift of the gyro output signal and compensate based on the model.

[0004] 2) Directly filter the gyro output signal and suppress gyro noise and drift through signal denoising.

[0005] In actual situations, the random noise and drift of the gyroscope output signal are often weakly nonlinear, non-stationary, and slowly time-varying, making it impossible to establish an accurate model. In addition, the signal output is affected by uncertain factors in the external environment, and it is impossible to obtain accurate statistical characteristics in advance. The established model is also time-varying, and the model must be fitted and the parameters identified online to achieve the compensation effect, which is difficult to achieve in real-time control.

[0006] The use of effective filtering methods can eliminate noise that is higher than the carrier's attitude motion bandwidth and improve the accuracy of the fiber optic gyroscope output signal. Existing fiber optic gyroscope output signal filtering methods include smoothing filtering, wavelet transform filtering and IIR filtering methods. Among them,

[0007] 1) The sliding filter algorithm is simple, easy to implement, and easy to ensure real-time performance; it has a good filtering effect on stable signals, but a poor effect on sudden change signals. It filters out the spectrum outside the frequency band without distinction, so it also suppresses the high-frequency components of the real signal mixed in the noise signal, and cannot filter out the low-frequency interference;

[0008] 2) Wavelet transform filtering can conveniently and effectively filter out the high-frequency noise of the fiber optic gyroscope, and can also selectively suppress the noise component. The method is flexible. The larger the wavelet decomposition scale, the better the effect. However, the output signal noise variance needs to be estimated during the wavelet transform filtering process. A large amount of test data is required, and the amount of calculation will increase significantly, affecting the real-time performance of the gyroscope output.

[0009] 3) The initial transition time of IIR digital filtering is long and the system delay is large. Summary of the invention

[0010] In order to solve the problems of large delay, large amount of calculation, poor filtering effect of high-frequency noise and mutation in traditional filtering methods, the present invention provides a method for filtering the output signal of a fiber optic gyroscope. The method has better advantages in reducing the stability of gyroscope output, suppressing high-frequency noise, and real-time performance.

[0011] The optical fiber gyroscope output signal filtering method provided by the present disclosure estimates the gyroscope output signal at the current moment by multiplying the gyroscope output signal at the previous moment by the corresponding weight. The weight value is obtained by iteration.

[0012] The specific plan is:

[0013] First, the initial value of the weight is set to zero, and the gyro output signals at M previous moments are multiplied by the corresponding weights to calculate the estimated value of the gyro output signal at the current moment.

[0014] Calculate the difference between the gyro signal collected at the current moment and the estimated value.

[0015] The weight value at the next moment is estimated based on the difference, the weight value at the current moment, the gyro output signals at M previous moments, and the iteration step size μ.

[0016] Repeat the above process continuously, update the weights in real time, and gradually obtain a stable and convergent weight value.

[0017] The filtered gyro output signal is calculated using the new weight value.

[0018] The iteration step size μ is a very small constant used to control the convergence speed of the iteration. The larger the value of μ, the faster the iteration speed, but too large a value will cause the iteration process to diverge. The minimum value of μ is unlimited and can be greater than zero. The maximum value of μ is: max =1 / λ min

[0019] Among them, λ min is the minimum eigenvalue of the autocorrelation matrix of the gyro signal input vector. The autocorrelation matrix R is

[0020] R=U T (n-1)U(n-1)

[0021] U(n-1) is a gyro signal input vector, including M gyro output signals at previous moments.

[0022] Compared with the prior art, the beneficial effects of the present invention are: 1) higher real-time performance; 2) small time delay; 3) short initial transition process; 4) real-time modeling to determine random filtering parameters can establish a random noise model more accurately and eliminate noise with different random characteristics in the time domain; 5) it reflects the fluctuation of the original data more realistically, retains the useful information of the original signal, and the data after FLP filtering is free of burrs, which can better remove high-frequency noise in the signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and other objects, features and advantages of the present disclosure will become more apparent through a more detailed description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present disclosure.

[0024] Figure 1 is a flow chart of an exemplary embodiment according to the present disclosure. DETAILED DESCRIPTION

[0025] The preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0026] The present disclosure provides a method for filtering an output signal of a fiber optic gyroscope. Figure 1 As shown, it mainly includes the following steps:

[0027] 1) First, set the initial weight value B(0) to zero, and then max A constant is taken between and as the value of the iteration step size μ, and the value should take into account both the iteration speed and the filtering effect.

[0028] 2) Assume that the estimated value of the gyro signal at the current moment is

[0029]

[0030] Among them, U(n-1)={u(n-1)u(n-2)…u(nM)} is the vector composed of the gyro output signals at the previous moment, u(nk) is the gyro output signal at the previous nk moment, and the weight vector B={b1 b2…b M}; M is the order. The larger the order, the better the filtering effect. However, too large an order will increase the amount of calculation in the filtering process.

[0031] 3) The difference between the estimated value of the gyro output signal and the current value collected is

[0032]

[0033] 4) In order to minimize the prediction error e(n), the weight iteration formula is adopted:

[0034] B(n+1)=B(n)+μe(n)U(n-1)

[0035] Where B(n) is the weight vector of the filter at the current moment.

[0036] 5) Repeat the process from 2) to 4). After each iteration, a new weight data is obtained and a weight update is performed.

[0037] 6) Substitute the value of the new weight B into equation (1) to obtain the filtered gyro output signal.

[0038] The above technical scheme is only an exemplary embodiment of the present invention. For those skilled in the art, it is easy to make various types of improvements or modifications based on the application methods and principles disclosed in the present invention, and it is not limited to the method described in the above specific embodiment of the present invention. Therefore, the method described above is only preferred and does not have a restrictive meaning.

Claims

1. A method for filtering an output signal of a fiber optic gyroscope, comprising the following steps: S1, setting the initial weight value to zero, multiplying the gyro output signals at M previous moments by the corresponding weight to calculate the estimated value of the gyro output signal at the current moment; S2, calculating the difference between the gyro signal collected at the current moment and the estimated value; S3, estimating the weight value at the next moment according to the difference, the weight value at the current moment, the gyro output signals at M previous moments and the iteration step size μ; S4, repeat the above process continuously, update the weight in real time, and gradually obtain a stable and convergent weight value; S5, using the new weight value, based on the method of step S1, calculate and obtain the filtered gyro output signal.

2. The method according to claim 1, characterized in that The step S1 specifically includes: Set the initial weight value B(0) to zero, and set it to 0~μ max Take a constant between and as the value of the iteration step μ; Assume that U(n-1)={u(n-1)u(n-2)…u(nM)} is the vector composed of the gyro output signals at the previous moment, u(nk) is the gyro output signal at the previous nk moment, and the weight vector B={b1 b2…b M }, M is the order, then the estimated value of the gyro signal at the current moment is 3. The method according to claim 2, characterized in that The maximum value of the iteration step size μ is: m max =1 / min min Among them, λ min The minimum eigenvalue of the autocorrelation matrix of the gyro signal input vector; The autocorrelation matrix R is: R=U T (n-1)U(n-1) U(n-1) is a gyro signal input vector, including M gyro output signals at previous moments.

4. The method according to claim 2 or 3, characterized in that: In step S2, the difference between the estimated value of the gyro output signal and the current acquisition value is 5. The method according to claim 4, characterized in that In step S3, the weight value at the next moment is B(n+1)=B(n)+μe(n)U(n-1) Among them, B(n) is the weight vector of the filter at the current moment.

6. The method according to claim 4, characterized in that The step S4 comprises: Each time iterates, a new weight data is obtained and a weight update is performed; Based on the obtained new weight B value, an estimated value of the filtered gyro output signal is calculated; In this way, steps S1-S4 are repeated to obtain the final weight value.

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