Electric signal demodulation method for single-light-source single-axis fiber-optic gyroscope

Abstract

The invention discloses an electric signal demodulation method of a single-light-source single-axis fiber-optic gyroscope, and relates to the technical field of quantum optical precision measurement, and the method comprises the following steps: constructing a joint probability density function model of quantum fluctuation noise, dynamically correcting actually measured noise parameters through Bayesian inference, and generating a noise model containing shot noise variance and thermal noise mean; generating a plurality of groups of reference-measurement light path pairs by using Y waveguide beam splitting characteristics, calculating interference signal cross correlation coefficients of each group of light path pairs, and screening out two groups of high correlation signal pairs with the highest correlation degree; and according to the high-precision phase track, based on a phase-angular velocity conversion relation of a Sagnac effect, calculating a rotation angular velocity value by applying a Sagnac phase shift formula, and outputting the rotation angular velocity value. According to the invention, through dynamic noise modeling and a parameter adaptive adjustment mechanism, shot noise and thermal noise are efficiently suppressed, and the integrity and accuracy of the phase signal after noise reduction are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of quantum optical precision measurement, in particular to an electrical signal demodulation method for a single-light-source single-axis fiber optic gyroscope. Background Art

[0002] Quantum fluctuation noise, the noise floor in quantum optical systems, poses a key challenge in the field of high-precision phase measurement due to its precise modeling and suppression. In recent years, research on the statistical properties of noise based on quantum mechanics has steadily deepened, with joint probability density function models making progress in describing the combined characteristics of shot noise and thermal noise. Furthermore, multi-path interferometry techniques, using beam splitters to construct reference-measurement optical path pairs, have provided a new experimental approach for noise correlation analysis. Furthermore, adaptive filtering algorithms, such as the combination of wavelet transforms and FIR filters, have shown broad potential for suppressing electrical signal noise.

[0003] However, existing technologies still have limitations in dynamic noise modeling and the utilization of multi-path signals. Traditional noise models often rely on static parameters or empirical fitting, making it difficult to reflect the dynamic characteristics of quantum fluctuation noise in real time, resulting in insufficiently targeted noise suppression. Furthermore, the highly correlated nature of multi-path interference signals is not fully exploited, and conventional screening methods are prone to introducing correlation errors from fiber perturbation noise, which restricts further improvements in phase extraction accuracy. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an electrical signal demodulation method for a single-light-source single-axis fiber optic gyroscope to solve the problems of insufficient dynamic adaptability of existing quantum noise models and insufficient utilization of the high correlation characteristics of multi-optical path signals, resulting in limited phase extraction accuracy.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides an electrical signal demodulation method for a single-light-source single-axis fiber optic gyroscope, which includes constructing a joint probability density function model of quantum fluctuation noise, dynamically correcting the measured noise parameters through Bayesian inference, and generating a noise model including the shot noise variance and the thermal noise mean; utilizing the Y-waveguide beam splitting characteristics to generate multiple groups of reference-measurement optical path pairs, calculating the mutual correlation coefficient of the interference signals of each group of optical path pairs, and screening out the two groups of high-correlation signal pairs with the highest correlation; based on the noise model, adjusting the wavelet threshold and FIR filter bandwidth parameters to suppress the shot noise and thermal noise in the electrical signal output by the photodetector, and outputting the noise-reduced phase signal; performing weighted averaging on the two groups of highly correlated signal pairs screened out, with the weight being the mutual correlation coefficient of the interference signals of the corresponding optical path pairs, and extracting a high-precision phase trajectory after eliminating the fiber disturbance noise; and according to the high-precision phase trajectory, based on the phase-angular velocity conversion relationship of the Sagnac effect, applying the Sagnac phase shift formula to calculate the rotational angular velocity value and output it.

[0008] As a preferred solution of the electric signal demodulation method of the single-light-source single-axis fiber optic gyroscope of the present invention, wherein: the joint probability density function model of quantum fluctuation noise is constructed, and the specific steps are as follows:

[0009] Analyze the source of quantum fluctuation noise in a single-source, single-axis fiber optic gyroscope and determine the independence of shot noise and thermal noise;

[0010] Based on the theory of quantum optics, the shot noise is assumed to obey the Poisson distribution and the thermal noise to obey the Gaussian distribution;

[0011] Combining the statistical independence characteristics of shot noise and thermal noise, a joint probability density function model is established;

[0012] Verify the fit of the joint probability density function model to the actual noisy measurement data.

[0013] As a preferred solution of the electrical signal demodulation method of the single-light-source single-axis fiber optic gyroscope of the present invention, wherein: the measured noise parameters are dynamically corrected by Bayesian inference to generate a noise model including the shot noise variance and the thermal noise mean, the specific steps are as follows:

[0014] Obtain time series data of the measured interference signal of the fiber optic gyroscope;

[0015] Based on the joint probability density function model, the initial estimates of the shot noise variance and the thermal noise mean are set, and the posterior probability distribution is updated by combining the time series data using the Bayesian inference method.

[0016] The modified shot noise variance and thermal noise mean are extracted from the posterior probability distribution to generate a noise model.

[0017] As a preferred solution of the electrical signal demodulation method of the single-light-source single-axis fiber optic gyroscope of the present invention, the Y-waveguide beam splitting characteristic refers to the characteristic of the Y-waveguide splitting the input light into two optical signals with orthogonal polarization directions based on the birefringence effect, transmitting them through different paths and then combining them for output;

[0018] The specific steps of screening out the two groups of highly correlated signal pairs with the highest correlation are as follows:

[0019] The Y-waveguide beam splitting characteristics are used to generate multiple sets of optical signal pairs of reference and measurement optical paths, and the cross-correlation coefficient of the interference signal of each optical path pair is calculated.

[0020] Sort by mutual correlation coefficient from high to low, and select the first two groups of optical path pairs with the largest mutual correlation coefficient.

[0021] As a preferred solution of the electrical signal demodulation method of the single-light-source single-axis fiber optic gyroscope described in the present invention, the wavelet threshold refers to a critical value for selecting signal coefficients that is adaptively determined based on noise intensity and signal sparsity; and the FIR filter bandwidth parameter refers to the filter boundary frequency set according to the frequency range of the noise to be suppressed.

[0022] As a preferred solution of the electric signal demodulation method of the single-light-source single-axis fiber optic gyroscope of the present invention, wherein: the output of the noise-reduced phase signal is specifically performed as follows:

[0023] The electrical signal output by the photodetector is soft-thresholded using wavelet thresholding;

[0024] The electrical signal after soft threshold processing is input into the FIR filter, low-pass filtered according to the set bandwidth parameters, and the continuous phase change information is extracted through Hilbert transform, which is output as the phase signal after joint noise reduction by wavelet threshold and FIR filter.

[0025] As a preferred solution of the electrical signal demodulation method of the single-light-source single-axis fiber optic gyroscope of the present invention, the steps of extracting the high-precision phase trajectory after eliminating the fiber disturbance noise are as follows:

[0026] For the two groups of highly correlated signal pairs screened out, the phase signals after joint noise reduction are extracted respectively, and the two phase signals are weighted averaged using the mutual correlation coefficient of the two groups of signals as the weight;

[0027] The phase signal after weighted average is subjected to sliding average filtering to output a high-precision phase trajectory after eliminating fiber disturbance noise.

[0028] As a preferred solution of the electric signal demodulation method of the single-light-source single-axis fiber optic gyroscope of the present invention, wherein: the rotation angular velocity value is calculated and output by applying the Sagnac phase shift formula, the specific steps are as follows:

[0029] The phase change per unit time is extracted from the high-precision phase trajectory, and the rotational angular velocity value is calculated based on the phase-angular velocity conversion relationship of the Sagnac effect;

[0030] The temperature drift compensation and device nonlinearity calibration are performed on the rotation angular velocity value, the system error is corrected, and the calibrated rotation angular velocity value is output.

[0031] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the electrical signal demodulation method of the single-light source single-axis fiber optic gyroscope as described in the first aspect of the present invention is implemented.

[0032] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the electrical signal demodulation method of a single-light-source single-axis fiber optic gyroscope as described in the first aspect of the present invention is implemented.

[0033] The beneficial effects of this invention are as follows: by constructing a joint probability density function model of quantum fluctuation noise and using Bayesian inference to dynamically correct the measured noise parameters, it is possible to capture the dynamic changes in the shot noise variance and thermal noise mean in real time, providing a precise noise statistical basis for adjusting the wavelet threshold and FIR filter bandwidth parameters. Compared with traditional fixed-parameter filtering methods, this dynamic adjustment mechanism can significantly improve the targeted noise suppression. While effectively suppressing shot noise and thermal noise, it also maximizes the preservation of the key characteristics of the phase signal, thereby significantly improving the integrity and accuracy of the phase signal after noise reduction, laying a reliable foundation for the subsequent extraction of high-precision phase trajectory. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 The flowchart of the electrical signal demodulation method of the single-light source single-axis fiber optic gyroscope is shown.

[0036] Figure 2 Flowchart constructed for the noise model.

[0037] Figure 3 Schematic diagram of light path pair generation and screening.

[0038] Figure 4This is a flow chart of signal processing. DETAILED DESCRIPTION

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0040] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0041] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0042] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides an electrical signal demodulation method for a single-light-source single-axis fiber optic gyroscope, comprising the following steps:

[0043] S1: Construct a joint probability density function model of quantum fluctuation noise, dynamically correct the measured noise parameters through Bayesian inference, and generate a noise model that includes the shot noise variance and thermal noise mean.

[0044] S.1.1: Analyze the source of quantum fluctuation noise in a single-source, single-axis fiber optic gyroscope and determine the independence of shot noise and thermal noise.

[0045] Specifically, based on the theory of quantum optics, the optical link of the single-light-source single-axis fiber optic gyroscope (light source → coupler → fiber ring → detector) was sorted out to identify the main sources of quantum fluctuation noise (shot noise and thermal noise).

[0046] It should be noted that shot noise is caused by the quantum properties of the light field output by the light source (such as a laser), manifested as random fluctuations in photon counts, and belongs to the category of quantum noise; thermal noise is caused by inelastic scattering (such as Rayleigh scattering) and lattice vibration (phonon interaction) caused by the thermal motion of molecules in the optical fiber material, and belongs to the category of classical thermal disturbance noise.

[0047] Furthermore, the photoelectric signal output by the gyroscope is collected by a photoelectric detector, and the noise component is separated by a frequency domain filtering method.

[0048] Specifically, when shot noise is concentrated in the high frequency band (the same order of magnitude as the optical carrier frequency), a bandpass filter is used to extract high-frequency noise samples; when thermal noise is concentrated in the low frequency band (limited by the optical fiber thermal diffusion time constant), a low-pass filter is used to extract low-frequency noise samples;

[0049] It should be noted that the extracted high-frequency noise samples correspond to shot noise samples, and the low-frequency noise samples correspond to thermal noise samples (because shot noise is strongly correlated with the optical carrier frequency and thermal noise is dominated by thermal diffusion, the two frequency bands do not overlap and can be completely separated by filtering).

[0050] Calculate the covariance of the shot noise samples and the thermal noise samples. If the covariance approaches zero (for example, the covariance calculated for 1000 groups of samples is 0.002, which is much smaller than the mean of the product of the sample standard deviations, 0.1), it is confirmed that the two are statistically independent.

[0051] S1.2: Based on the theory of quantum optics, the shot noise is assumed to obey the Poisson distribution and the thermal noise to obey the Gaussian distribution.

[0052] Specifically, according to the coherent state theory of quantum mechanics, the quantum fluctuations of the light field cause the photon count to satisfy the Poisson distribution. For monochromatic light with an average photon count rate of λ, the probability mass function of the shot noise is expressed as:

[0053]

[0054] Where P(n;λ) represents the probability mass function of shot noise, which indicates the probability that the photon count value is n under the condition that the average photon count rate is λ, n is the discretized photon count value (non-negative integer, n = 0, 1, 2, ...), λ represents the average photon count rate, e represents a natural constant, and e -λ represents the exponential decay term, reflecting the normalized characteristics of the Poisson distribution, λ n represents the nth power term of the average photon counting rate, which is proportional to the nth power of the photon count. n! represents the factorial of n and is used to normalize the probability distribution.

[0055] Based on the central limit theorem of classical statistical physics, the thermal noise (such as phase disturbance) caused by the thermal motion of molecules in optical fibers is the superposition of a large number of independent thermal vibrations and obeys a Gaussian distribution. The probability density function of thermal noise is expressed as:

[0056]

[0057] Where, f(t; μ, σ 2 ) is the probability density function of thermal noise, which represents the probability density of thermal noise value t (continuous variable), t is the instantaneous value of thermal noise, μ is the mean value of thermal noise, σ 2 is the variance of thermal noise, Is a mathematical constant representing the square root of 2 times pi. Represents the normalization coefficient of the Gaussian distribution, ensuring that the probability density integral over the entire domain is 1, 2σ 2 represents the variance-related scaling factor used to control the decay rate, (t-μ) 2 It represents the square deviation of the thermal noise value t from the mean μ, Represents the core term of the Gaussian distribution, which is used to describe the exponential decay characteristics of noise values around the mean μ.

[0058] S1.3: Combine the statistical independence characteristics of shot noise and thermal noise to establish a joint probability density function model.

[0059] Specifically, since shot noise (discrete) and thermal noise (continuous) are statistically independent, the joint probability density function model is the product of their probability distributions (the joint distribution of discrete-continuous variables needs to be related through integrals).

[0060] Assuming the total noise is the linear superposition of the two, the joint probability density function model is expressed as:

[0061]

[0062] N=N s +N t ;

[0063] Where p(N) represents the joint probability density function of the total noise of quantum fluctuations, N is the linear superposition of the two, and represents the instantaneous value of the total noise. s represents shot noise, N t represents thermal noise, represents the infinite sum of the discretized photon count values, P(N s =n) represents the probability that the shot noise takes the value n, It represents the probability density that the thermal noise value is Nn when the total noise value is N.

[0064] S1.4: Verify the goodness of fit of the joint probability density function model to the actual noisy measurement data.

[0065] Specifically, under different environmental conditions, the photoelectric signal output by the gyroscope is collected, and the noise sample is extracted and standardized (the DC component is subtracted and the AC noise part is retained);

[0066] For each group of samples, the empirical mean of shot noise, the empirical mean and empirical variance of thermal noise are calculated respectively.

[0067] The Kolmogorov-Smirnov test is used to evaluate the consistency of the predicted distribution of the joint probability density function model with the empirical distribution:

[0068] Specifically, for shot noise, the maximum value D of the difference between the theoretical Poisson distribution and the empirical distribution is calculated. s ; For thermal noise, calculate the maximum value D of the difference between the theoretical Gaussian distribution and the empirical distribution of the cumulative distribution function t ;

[0069] If D s < critical value (such as when α=0.05, the critical value is When N=1000, the critical value is ≈0.043) and D t < the critical value, the joint probability density function model fits the actual data well. α represents the significance level, which is used to control the probability of false positives (the smaller α is, the larger the critical value is, and the stricter the test is).

[0070] It should be noted that it is necessary to confirm whether the Kolmogorov-Smirnov test is directly applicable to shot noise (discrete). If not, the chi-square test or other methods should be used instead. Specifically, for discrete shot noise, the chi-square test is used to evaluate the consistency of the theoretical Poisson distribution with the empirical distribution; for continuous thermal noise, the Kolmogorov-Smirnov test is used.

[0071] S1.5: Obtain the time series data of the measured interference signal of the fiber optic gyroscope.

[0072] Specifically, the fiber optic gyroscope is placed in a stable test environment (such as a constant temperature box to control temperature fluctuations to less than 0.1°C, and a vibration isolation platform to reduce mechanical vibration interference), and the output end is connected to a high-speed data acquisition device.

[0073] Furthermore, the sampling rate and acquisition duration of the data acquisition device can be set;

[0074] For example, the sampling rate is set to 100 kHz to cover the high-frequency characteristics of shot noise and the low-frequency characteristics of thermal noise; the acquisition time is set to 30 seconds to ensure that the acquired interference signal time series contains sufficient noise samples.

[0075] It should be noted that during the acquisition process, the fiber optic gyroscope was kept in normal working condition (such as stable laser output power and uniform fiber ring temperature) to avoid external electromagnetic interference or mechanical shock.

[0076] After the acquisition is completed, the DC offset is removed by the sliding average method, and then a bandpass filter is used to retain the signal components related to shot noise (high frequency band) and thermal noise (low frequency band) to obtain clean interference signal time series data.

[0077] S1.6: Based on the joint probability density function model, set the initial estimates of the shot noise variance and the thermal noise mean, and use the Bayesian inference method to update the posterior probability distribution in combination with the time series data.

[0078] Specifically, based on the established joint probability density function model (shot noise follows Poisson distribution, thermal noise follows Gaussian distribution), an initial estimate is set.

[0079] It should be noted that the initial value of the shot noise variance is obtained through theoretical calculation (the shot noise variance is equal to the laser output power divided by the energy of a single photon), and the initial value of the thermal noise mean is determined by the theoretical relationship between the fiber temperature, length, and thermal noise coefficient (the thermal noise mean is equal to the product of the heat diffusion-related parameters and the temperature and length).

[0080] For the shot noise variance, the inverse gamma distribution is selected as the prior (conjugate prior, easy to calculate), and the parameters are set to weak information prior (such as shape parameter 2, scale parameter 1), reflecting the initial assumption about the shot noise variance; for the thermal noise mean, the normal distribution is selected as the prior, and the mean is the initial estimate.

[0081] The time series data are regarded as independent and identically distributed samples, and the likelihood function is the probability product of the joint probability density function model under the observed data (that is, the product of the probability density function of the noise value at each time point that conforms to the Poisson distribution or Gaussian distribution).

[0082] It should be noted that the likelihood function is a statistical function used to assess the degree of match between model parameters and observed data. In this context, the likelihood function represents the probability product of the joint probability density function model given the observed time series data. In other words, it represents the joint probability of the observed data occurring given the known model parameters (such as the shot noise variance and the thermal noise mean). Time series data refers to the sequence of continuous sampled values of the fiber optic gyroscope interferometer signal obtained over a period of time using high-speed data acquisition equipment (such as an analog-to-digital converter).

[0083] It should also be noted that the continuous sampling value sequence contains noise samples under the combined action of shot noise and thermal noise. Each sampling point corresponds to a noise measurement value at a specific moment. The sampling values are arranged in chronological order to form a sequence, which serves as the basic observation data for constructing the likelihood function in subsequent Bayesian inference.

[0084] A Markov Chain Monte Carlo algorithm (specifically, the Metropolis-Hastings sampler) is used for iterative sampling: starting from an initial parameter value, candidate parameter values are generated and the log-ratio of the target distribution (the product of the prior and the likelihood) is calculated.

[0085] It should be noted that the initial parameter values refer to the initial value of the shot noise variance and the initial value of the thermal noise mean.

[0086] For example, with an acceptance rate of about 23.4%, valid samples are retained. After 10,000 iterations, the first 2,000 warm-up samples are removed (to ensure distribution convergence), and the remaining 8,000 iterations are used for posterior distribution estimation.

[0087] S1.7: Extract the corrected shot noise variance and thermal noise mean from the posterior probability distribution to generate a noise model.

[0088] Specifically, the corrected values of the shot noise variance and the thermal noise mean are extracted from the posterior distribution samples obtained by MCMC sampling.

[0089] Among them, the posterior mean is selected as the correction value (that is, the arithmetic mean of all retained samples).

[0090] Substitute the corrected parameters into the joint probability density function model to generate a noise model;

[0091] The generated noise model is a joint probability density function model that includes the corrected shot noise variance and the thermal noise mean, which can be used for subsequent noise characteristic analysis or gyroscope performance prediction.

[0092] S2: Utilize the Y-waveguide beam splitting characteristics to generate multiple reference-measurement optical path pairs, calculate the mutual correlation coefficient of the interference signals of each optical path pair, and select the two groups of highly correlated signal pairs with the highest correlation.

[0093] The Y-waveguide beam splitting characteristic refers to the characteristic of the Y-waveguide that, based on the birefringence effect, it splits the input light into two optical signals with orthogonal polarization directions, transmits them through different paths, and then combines them for output.

[0094] S2.1: Utilize the Y-waveguide beam splitting characteristics to generate multiple sets of optical signal pairs for reference and measurement optical paths, and calculate the cross-correlation coefficient for the interference signals of each optical path pair.

[0095] Specifically, based on the birefringence effect of the Y-waveguide, the polarization state of the input light is set by a polarization controller so that the Y-waveguide can evenly split the input light into two beams of orthogonal polarization light;

[0096] Configure the first optical path: Transmit the first orthogonal beam through the first transmission path of the fiber ring, and the second orthogonal beam through the second transmission path of the fiber ring. Combine the two beams at the detector to generate the first interference signal. Maintaining the Y-waveguide splitting structure, adjust the parameters of the polarization controller in the fiber ring. Configure the second optical path: Swap the transmission paths of the two orthogonal beams, so that the first beam passes through the second transmission path and the second beam passes through the first transmission path. Combine the beams to generate the second interference signal.

[0097] Repeat the configuration steps for the first and second optical paths, generate N optical path pairs by changing the polarization controller parameter combination, and synchronously collect the interference signal of each optical path pair to obtain time series data;

[0098] Based on time series data, the cross-correlation coefficient is obtained by calculating the product integral of each reference signal and the measured signal at different time delays and then dividing it by the product of the square roots of the energy of the two signals (classic normalized cross-correlation analysis).

[0099] S2.2: Sort by mutual correlation coefficient from high to low, and select the first two groups of optical path pairs with the largest mutual correlation coefficient.

[0100] Specifically, the mutual correlation coefficients of N groups of optical path pairs are arranged in descending order according to their numerical values, and the optical path pairs with the top two values are selected as high-correlation signal pairs, denoted as P1 and P2; where N refers to the total number of optical path pairs generated by changing the polarization controller parameter combination.

[0101] Extract the reference signal and measurement signal corresponding to P1, and the reference signal and measurement signal corresponding to P2;

[0102] Verify that the correlation coefficient between P1 and P2 satisfies R1>0.8*R max , if it is not satisfied, regenerate the light path pair and calculate the mutual correlation coefficient;

[0103] Among them, R max is the maximum mutual correlation coefficient among N groups of optical path pairs, R1 represents the first highly correlated signal pair after sorting (i.e., a group of highly correlated signal pairs with the highest correlation), and 0.8 represents the preset proportional threshold constant (fixed value 0.8); the screened P1 and P2 are output as highly correlated signal pairs and enter the subsequent weighted average processing flow.

[0104] S3: Based on the noise model, adjust the wavelet threshold and FIR filter bandwidth parameters to suppress the shot noise and thermal noise in the output electrical signal of the photodetector, and output the noise-reduced phase signal.

[0105] The wavelet threshold refers to the critical value for selecting signal coefficients, which is adaptively determined based on noise intensity and signal sparsity; the FIR filter bandwidth parameter refers to the filter boundary frequency set according to the frequency range of the noise to be suppressed.

[0106] S3.1: Use wavelet thresholding to perform soft thresholding on the electrical signal output by the photodetector.

[0107] Specifically, the input electrical signal is subjected to multi-scale wavelet decomposition to obtain wavelet coefficients of different frequency bands; the adaptive threshold of the wavelet coefficients of each frequency band is calculated based on the real-time estimation values of the shot noise variance and the thermal noise mean in the noise model;

[0108] It should be noted that the adaptive threshold calculation process for the wavelet coefficients in each frequency band is as follows: Based on the real-time estimates of the shot noise variance and the mean thermal noise in the noise model, the adaptive threshold is determined for each frequency band based on the noise energy distribution ratio. Specifically, for high-frequency wavelet coefficients (primarily containing shot noise components), the adaptive threshold is proportional to the shot noise variance; for low-frequency wavelet coefficients (primarily containing thermal noise components), the adaptive threshold is proportional to the mean thermal noise. By dynamically adjusting the proportionality coefficient, the adaptive threshold for each frequency band can effectively suppress noise while avoiding over-smoothing of signal details.

[0109] The wavelet coefficients above the adaptive threshold are soft-threshold processed (the wavelet coefficient value is subtracted from the absolute value of the adaptive threshold and the sign is retained), and the wavelet coefficients below the adaptive threshold are directly set to zero; the processed wavelet coefficients are restored to the denoised time domain signal through the wavelet reconstruction algorithm.

[0110] S3.2: The electrical signal after soft threshold processing is input into the FIR filter, low-pass filtered according to the set bandwidth parameters, and the continuous phase change information is extracted through Hilbert transform, which is output as the phase signal after the noise reduction by the wavelet threshold and FIR filter.

[0111] Specifically, the cutoff frequency of the FIR filter is set based on the frequency characteristics of thermal noise (concentrated in the low frequency band) and shot noise (same order of magnitude as the optical carrier) in the noise model;

[0112] For example, the thermal noise cutoff frequency is set to 0.1 times the optical carrier frequency, and the shot noise cutoff frequency is set to 0.5 times the optical carrier frequency.

[0113] The time domain signal is input into the FIR filter, and the low-pass filter coefficients are generated using a window function method (such as the Kaiser window) to avoid the phase signal from introducing additional distortion or amplitude distortion during the filtering process, ensuring that the phase trajectory extracted by the subsequent Hilbert transform is continuous and smooth;

[0114] Perform Hilbert transform on the filtered time domain signal to construct the analytical signal;

[0115] The phase trajectory of the analytical signal is extracted through the inverse tangent function, the phase jump is eliminated and a continuous phase signal is output.

[0116] S4: Perform weighted averaging on the two groups of highly correlated signal pairs selected, where the weight is the mutual correlation coefficient of the interference signals of the corresponding optical path pair, and extract the high-precision phase trajectory after eliminating the fiber disturbance noise.

[0117] S4.1: For the two groups of highly correlated signal pairs selected, extract the phase signals after joint noise reduction respectively, and perform weighted averaging of the two phase signals using the cross-correlation coefficient of the two groups of signals as weights.

[0118] Specifically, the joint denoised phase signals from the high correlation signal pairs corresponding to P1 and P2 are extracted respectively, and are recorded as Phase1 and Phase2;

[0119] Based on the calculated mutual correlation coefficients R1 and R2 (R1 is the mutual correlation coefficient of P1, and R2 is the mutual correlation coefficient of P2), the weighting coefficients are calculated: the weighting coefficient of P1 is R1 / (R1+R2); the weighting coefficient of P2 is R2 / (R1+R2).

[0120] Multiply Phase1 and Phase2 by the corresponding weighting coefficients respectively to obtain weighted phase signal components, and sum the weighted phase signal components to generate the final weighted average phase signal.

[0121] S4.2: Perform sliding average filtering on the weighted averaged phase signal and output a high-precision phase trajectory after eliminating fiber disturbance noise.

[0122] Specifically, the window size of the sliding average filter is set;

[0123] For example, the number of sampling points covered by the window is adjusted according to the time scale of the fiber disturbance noise.

[0124] The weighted average phase signal is sequentially input into the sliding average filter to calculate the arithmetic mean of the phase values of all sampling points in the window;

[0125] Slide the window along the time axis by one sampling point, repeatedly calculate the average value until all phase signals are covered, and output the phase signal after sliding average processing as a high-precision phase trajectory;

[0126] It should be noted that the high-precision phase trajectory has eliminated the influence of fiber disturbance noise.

[0127] S5: Based on the high-precision phase trajectory and the phase-angular velocity conversion relationship of the Sagnac effect, the Sagnac phase shift formula is applied to calculate the rotational angular velocity value and output it.

[0128] S5.1: Extract the phase change per unit time from the high-precision phase trajectory and calculate the rotational angular velocity value based on the phase-angular velocity conversion relationship of the Sagnac effect.

[0129] Specifically, the high-precision phase trajectory is numerically differentiated to calculate the instantaneous phase change rate of adjacent phase sampling points (i.e., the phase change per unit time). );

[0130] According to the phase-angular velocity conversion relationship of the Sagnac effect, the mathematical relationship between the phase change rate and the rotational angular velocity is established, and the expression is:

[0131]

[0132] Where, It represents the total phase change caused by rotation when the light wave propagates in the fiber loop. 8π represents the constant term, which is derived from the physical derivation of the Sagnac effect. U represents the number of turns of the fiber loop. A represents the effective area of a single turn of the fiber loop, that is, the area of the plane area enclosed by a single turn of fiber. λ' represents the wavelength of the optical carrier, that is, the wavelength of the light wave emitted by the light source in free space. c is the speed of light in vacuum, a universal constant. Ω represents the angular velocity of rotation, that is, the angular rate of rotation of an object around the rotation axis. Δt represents the time interval, which represents the length of the time window used to calculate the phase change.

[0133] The phase change per unit time The measured value is substituted into the mathematical relationship between the phase change rate and the rotation angular velocity to calculate the real-time rotation angular velocity value.

[0134] S5.2: Perform temperature drift compensation and device nonlinearity calibration on the rotation angular velocity value, correct the system error, and output the calibrated rotation angular velocity value.

[0135] Specifically, the internal temperature of the fiber optic gyroscope is monitored in real time through an integrated temperature sensor, and the temperature-phase error compensation rule is determined based on the corresponding relationship between temperature and phase error;

[0136] For example, the corresponding relationship is determined by using polynomial fitting or piecewise linear compensation.

[0137] Based on the nonlinear characteristic data of the device calibrated at the factory, a table lookup method or a least squares fitting method is used to generate a calibration coefficient. Temperature compensation and nonlinear correction operations are performed on the real-time rotation angular velocity value in sequence to generate a calibrated rotation angular velocity value.

[0138] The nonlinear characteristic data refers to the input-output corresponding data obtained during the factory calibration process of the device, which reflects the deviation characteristics between the actual output value of the rotation angular velocity and the ideal linear output value.

[0139] It should be noted that the real-time rotational angular velocity value is subjected to temperature compensation and nonlinear correction operations in sequence. The process is as follows: based on the internal temperature data of the fiber optic gyroscope obtained in real time by the integrated temperature sensor, according to the temperature-phase error correspondence relationship determined in advance by polynomial fitting or piecewise linear compensation method, the phase error compensation value at the current temperature is calculated and deducted from the measured rotational angular velocity value. Based on the input-output characteristic data of the device calibrated at the factory, the nonlinear calibration coefficient table generated by least squares fitting is used to find the calibration coefficient corresponding to the current rotational angular velocity value and multiply the value after deducting the temperature error to eliminate the influence of the device's inherent nonlinear error.

[0140] The calibrated rotational angular velocity value is converted into units (for example, from rad / s to ° / h) to output a final rotational angular velocity value that meets application requirements.

[0141] It should be noted that rad / s stands for radians per second, which is the unit used to measure angular velocity in the International System of Units. It describes the number of radians that an object rotates around its rotation axis per unit time (per second); ° / h stands for degrees per hour, which is an angular velocity unit commonly used in engineering practice and specific application scenarios. It describes the number of angles that an object rotates around its rotation axis per unit time (per hour). The two can be converted to each other through angle unit conversion (1 radian is approximately equal to 57.3 degrees) and time unit conversion (1 hour is equal to 3600 seconds).

[0142] This embodiment also provides a computer device suitable for the electrical signal demodulation method of a single-light-source single-axis fiber optic gyroscope, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the electrical signal demodulation method of the single-light-source single-axis fiber optic gyroscope proposed in the above embodiment.

[0143] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0144] This embodiment also provides a storage medium having a computer program stored thereon. When the program is executed by a processor, the program implements the electrical signal demodulation method for a single-light-source single-axis fiber optic gyroscope proposed in the above embodiment. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0145] In summary, by constructing a joint probability density function model of quantum fluctuation noise and using Bayesian inference to dynamically correct the measured noise parameters, this method can capture the dynamic changes in the shot noise variance and thermal noise mean in real time, providing a precise noise statistical basis for adjusting the wavelet threshold and FIR filter bandwidth parameters. Compared with traditional fixed-parameter filtering methods, this dynamic adjustment mechanism can significantly improve the targeted noise suppression. While effectively suppressing shot noise and thermal noise, it also maximizes the preservation of the key features of the phase signal, thereby significantly improving the integrity and accuracy of the phase signal after noise reduction, laying a solid foundation for the subsequent extraction of high-precision phase trajectories.

[0146] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for demodulating electrical signals of a single-light-source, single-axis fiber optic gyroscope, characterized by: include, Construct a joint probability density function model of quantum fluctuation noise, dynamically modify the measured noise parameters through Bayesian inference, and generate a noise model that includes the shot noise variance and thermal noise mean; The Y-waveguide beam splitting characteristics are used to generate multiple reference-measurement optical path pairs. The correlation coefficient of the interference signals of each optical path pair is calculated, and the two highly correlated signal pairs with the highest correlation are selected. Based on the noise model, the wavelet threshold and FIR filter bandwidth parameters are adjusted to suppress the shot noise and thermal noise in the output electrical signal of the photodetector, and the noise-reduced phase signal is output; The two groups of highly correlated signal pairs screened out are weighted averaged, with the weight being the mutual correlation coefficient of the interference signals of the corresponding optical path pair, to extract the high-precision phase trajectory after eliminating the fiber disturbance noise; According to the high-precision phase trajectory and the phase-angular velocity conversion relationship of the Sagnac effect, the rotation angular velocity value is calculated and output using the Sagnac phase shift formula.

2. The electrical signal demodulation method of a single-light-source single-axis fiber optic gyroscope according to claim 1, wherein: The specific steps of constructing the joint probability density function model of quantum fluctuation noise are as follows: Analyze the source of quantum fluctuation noise in a single-source, single-axis fiber optic gyroscope and determine the independence of shot noise and thermal noise; Based on the theory of quantum optics, the shot noise is assumed to obey the Poisson distribution and the thermal noise to obey the Gaussian distribution; Combining the statistical independence characteristics of shot noise and thermal noise, a joint probability density function model is established; Verify the fit of the joint probability density function model to the actual noisy measurement data.

3. The electrical signal demodulation method for a single-light-source, single-axis fiber optic gyroscope according to claim 2, wherein: The Bayesian inference is used to dynamically correct the measured noise parameters to generate a noise model including the shot noise variance and the thermal noise mean. The specific steps are as follows: Obtain time series data of the measured interference signal of the fiber optic gyroscope; Based on the joint probability density function model, the initial estimates of the shot noise variance and the thermal noise mean are set, and the posterior probability distribution is updated by combining the time series data using the Bayesian inference method. The modified shot noise variance and thermal noise mean are extracted from the posterior probability distribution to generate a noise model.

4. The electrical signal demodulation method of a single-light-source single-axis fiber optic gyroscope according to claim 3, wherein: The Y-waveguide beam splitting characteristic refers to the characteristic of the Y-waveguide that, based on the birefringence effect, it splits the input light into two optical signals with orthogonal polarization directions, transmits them through different paths, and then combines them for output. The specific steps of screening out the two groups of highly correlated signal pairs with the highest correlation are as follows: The Y-waveguide beam splitting characteristics are used to generate multiple sets of optical signal pairs of reference and measurement optical paths, and the cross-correlation coefficient of the interference signal of each optical path pair is calculated. Sort by mutual correlation coefficient from high to low, and select the first two groups of optical path pairs with the largest mutual correlation coefficient.

5. The electrical signal demodulation method of a single-light-source single-axis fiber optic gyroscope according to claim 4, characterized in that: The wavelet threshold refers to a critical value for selecting signal coefficients that is adaptively determined based on noise intensity and signal sparsity; the FIR filter bandwidth parameter refers to a filter boundary frequency that is set based on the frequency range of the noise to be suppressed.

6. The electrical signal demodulation method of a single-light-source single-axis fiber optic gyroscope according to claim 5, characterized in that: The specific steps of outputting the phase signal after noise reduction are as follows: The electrical signal output by the photodetector is soft-thresholded using wavelet thresholding; The electrical signal after soft threshold processing is input into the FIR filter, low-pass filtered according to the set bandwidth parameters, and the continuous phase change information is extracted through Hilbert transform, which is output as the phase signal after joint noise reduction by wavelet threshold and FIR filter.

7. The electrical signal demodulation method of a single-light-source single-axis fiber optic gyroscope according to claim 6, wherein: The specific steps of extracting the high-precision phase trajectory after eliminating the fiber disturbance noise are as follows: For the two groups of highly correlated signal pairs screened out, the phase signals after joint noise reduction are extracted respectively, and the two phase signals are weighted averaged using the mutual correlation coefficient of the two groups of signals as the weight; The phase signal after weighted average is subjected to sliding average filtering to output a high-precision phase trajectory after eliminating fiber disturbance noise.

8. The electrical signal demodulation method of a single-light-source single-axis fiber optic gyroscope according to claim 7, wherein: The Sagnac phase shift formula is used to calculate the rotation angular velocity value and output it. The specific steps are as follows: The phase change per unit time is extracted from the high-precision phase trajectory, and the rotational angular velocity value is calculated based on the phase-angular velocity conversion relationship of the Sagnac effect; The temperature drift compensation and device nonlinearity calibration are performed on the rotation angular velocity value, the system error is corrected, and the calibrated rotation angular velocity value is output.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the electrical signal demodulation method of the single-light-source single-axis fiber optic gyroscope according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the electrical signal demodulation method of the single-light-source single-axis fiber optic gyroscope according to any one of claims 1 to 8 are implemented.

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