Spectrogram generation apparatus and dispersion measurement method, apparatus

Abstract

The application relates to a spectrum generation device. The device comprises a light source, a collection device, a fiber processor, a time delay device and a reflection device; the fiber processor is used for splitting the light emitted by the light source into a first light beam and a second light beam, transmitting the first light beam to the time delay device, and transmitting the second light beam to the reflection device; the time delay device is used for processing the first light beam to obtain a third light beam, and reflecting the third light beam to the fiber processor; the reflection device is used for reflecting the second light beam to obtain a fourth light beam, and reflecting the fourth light beam to the fiber processor; and the fiber processor is used for combining the third light beam and the fourth light beam to obtain a fifth light beam, and transmitting the fifth light beam to the collection device, so that the collection device outputs a first interference spectrum of the fifth light beam. The device has a relatively simple structure and meets the requirements of interference spectrum generation.

Description

Technical Field

[0001] This application relates to the field of dispersion measurement technology, and in particular to a spectrum generation device and a dispersion measurement method and apparatus. Background Technology

[0002] Second-order and higher-order dispersion plays a crucial role in the generation, chirp compensation, propagation, and optical nonlinear processes of optical ultrashort pulses. Among existing dispersion measurement methods, time-domain pulse delay-based or frequency-domain spectral interferometry is the most widely used, but the required spectral generation equipment is complex and costly. Summary of the Invention

[0003] Therefore, it is necessary to provide a relatively simple and low-cost spectrometer generation device and dispersion measurement method and device to address the above-mentioned technical problems.

[0004] In a first aspect, this application provides a spectrum generation device. The device includes a light source, a data acquisition device, an optical fiber processor, a delay device, and a reflection device;

[0005] The fiber optic processor is used to split the light emitted by the light source into a first beam and a second beam, and to transmit the first beam to the delay device and the second beam to the reflection device.

[0006] The delay device is used to process the first beam to obtain a third beam and reflect the third beam to the fiber optic processor;

[0007] The reflecting device is used to reflect the second beam to obtain a fourth beam, and then reflect the fourth beam to the fiber optic processor.

[0008] The fiber optic processor is used to combine the third beam and the fourth beam to obtain a fifth beam, and transmit the fifth beam to the acquisition device so that the acquisition device can output the first interference spectrum of the fifth beam.

[0009] In one embodiment, the reflecting device includes any one of a first reflecting device, a second reflecting device, a third reflecting device, and a fourth reflecting device. The first reflecting device includes a first transmission component, a first fiber collimator, and a first planar reflector. The first transmission component is disposed between the fiber processor and the first fiber collimator, and the planar reflector is located away from the first transmission component.

[0010] The second reflection device includes a second transmission component and a reflective optical fiber, the reflective optical fiber being connected to the optical fiber processor through the second transmission component;

[0011] The third reflection device includes a third transmission component, a second fiber collimator, a transmission mirror, and a second plane mirror. The second fiber collimator is connected to the fiber processor through the third transmission component, and the transmission mirror is disposed between the second fiber collimator and the second plane mirror.

[0012] The fourth reflection device includes a fourth transmission component, a third fiber collimator, and a reflection element. The third fiber collimator is connected to the fiber processor through the fourth transmission component.

[0013] In one embodiment, the delay device includes an optical fiber collimator and a plane mirror, the position of which is adjustable.

[0014] Secondly, this application also provides a dispersion measurement device. The device includes a processing unit and the spectrum generation device described in any one of the first aspects;

[0015] The acquisition device is used to send the first interference spectrum to the processing device;

[0016] The processing device is used to obtain the phase at each frequency point in the first interference spectrum based on the first interference spectrum and the Bayesian parameter estimation method;

[0017] The processing device is used to obtain the second-order and third-order dispersion of the first interference spectrum based on the phase at each frequency point in the first interference spectrum and a preset dispersion solution algorithm.

[0018] In one embodiment, the processing device is used to remove the background spectrum from the first interference spectrum to obtain a second interference spectrum;

[0019] The processing device is configured to, if the preset Bayesian parameter estimation method is an extended Kalman filter method, obtain the state quantities at each frequency point in the first interference spectrum based on the first state transition equation, the first measurement equation, and the second interference spectrum; the first state transition equation characterizes the numerical relationship between the state quantities at each frequency point in the second interference spectrum and the state quantities at the next frequency point, and the first measurement equation characterizes the numerical relationship between the state quantities, noise terms, and measured values ​​at each frequency point in the second interference spectrum;

[0020] The processing device is used to obtain the phase at each frequency point of the first interference spectrum based on the state quantities and corresponding equations at each frequency point in the first interference spectrum.

[0021] In one embodiment, the processing device is configured to perform an unscented transformation on the first state variable in the first state transition equation to obtain a second state variable if the preset Bayesian parameter estimation method is an unscented Kalman filtering method.

[0022] The processing device is used to process the second state quantity to obtain the third state quantity;

[0023] The processing device is used to obtain the state quantities at each frequency point in the first interference spectrum based on the third state quantity, the first state transition equation, and the first measurement equation.

[0024] The processing device is used to obtain the phase at each frequency point of the first interference spectrum based on the state quantities at each frequency point in the first interference spectrum and the corresponding equation set.

[0025] In one embodiment, the processing device is configured to perform Taylor series expansion on the phase at each frequency point in the first interference spectrum if the preset dispersion solution algorithm is a polynomial fitting algorithm, thereby obtaining the second-order and third-order dispersion of the first interference spectrum.

[0026] In one embodiment, the processing device is configured to, if the preset dispersion solving algorithm is a Kalman filter algorithm, solve the Kalman filter equation set used for dispersion calculation according to the second state transition equation and the second measurement equation to obtain the second-order dispersion and third-order dispersion of the first interference spectrum; the second state transition equation characterizes the numerical relationship between the phase at each frequency point in the first interference spectrum and the phase at the next frequency point, and the second measurement equation characterizes the numerical relationship between the phase, noise term and measured value at each frequency point in the first interference spectrum.

[0027] Thirdly, this application also provides a dispersion measurement method, the method comprising:

[0028] Acquire the first interference spectrum sent by the acquisition device;

[0029] Based on the first interference spectrum and the Bayesian parameter estimation method, the phase at each frequency point in the first interference spectrum is obtained; the Bayesian parameter estimation method is either the unscented Kalman filter method or the extended Kalman filter method.

[0030] Based on the phase at each frequency point in the first interference spectrum and the preset dispersion solution algorithm, the second-order and third-order dispersions of the first interference spectrum are obtained; the preset dispersion solution algorithm is any one of the polynomial fitting algorithm and the Kalman filtering algorithm.

[0031] Fourthly, this application also provides a dispersion measurement device, which is disposed in a processing device, and the device includes:

[0032] The spectrum acquisition module is used to acquire the first interference spectrum sent by the acquisition device;

[0033] The phase calculation module is used to obtain the phase at each frequency point in the first interference spectrum based on the first interference spectrum and the Bayesian parameter estimation method; the Bayesian parameter estimation method is either the unscented Kalman filtering method or the extended Kalman filtering method.

[0034] The dispersion measurement module is used to obtain the second-order and third-order dispersion of the first interference spectrum based on the phase at each frequency point in the first interference spectrum and a preset dispersion solving algorithm; the preset dispersion solving algorithm can be either a polynomial fitting algorithm or a Kalman filtering algorithm.

[0035] The aforementioned spectrum generation device requires low-cost light sources and acquisition equipment. It can be completed by simply combining multiple components such as light sources, acquisition equipment, fiber optic processors, delay devices, and reflection devices. The structure is relatively simple and meets the requirements for interference spectrum generation.

[0036] The aforementioned dispersion measurement device uses the Bayesian parameter estimation method to solve for the phase at each frequency point in the first interference spectrum. The solved phase is relatively accurate. Based on the relatively accurate phase and the preset dispersion solution algorithm, the second-order and third-order dispersion of the first interference spectrum are also relatively accurate. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of a spectrum generating apparatus including a first reflection device in one embodiment;

[0038] Figure 2 This is a schematic diagram of a spectrum generating apparatus including a second reflection device in one embodiment;

[0039] Figure 3 This is a schematic diagram of a spectrum generating apparatus including a third reflection device in one embodiment;

[0040] Figure 4 This is a schematic diagram of a spectrum generating apparatus including a fourth reflection device in one embodiment;

[0041] Figure 5 A flowchart illustrating the steps performed by the processing device in one exemplary embodiment;

[0042] Figure 6 A flowchart of a dispersion measurement method in one embodiment;

[0043] Figure 7This is a schematic diagram of a dispersion measurement device in one embodiment. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0045] This application provides a spectrum generation device, which includes a light source, a data acquisition device, an optical fiber processor, a delay device, and a reflection device. The optical fiber processor is used to split the light emitted by the light source into a first beam and a second beam, and transmit the first beam to the delay device and the second beam to the reflection device. The delay device is used to process the first beam to obtain a third beam and reflect the third beam to the optical fiber processor. The reflection device is used to reflect the second beam to obtain a fourth beam and reflect the fourth beam to the optical fiber processor. The optical fiber processor is used to combine the third beam and the fourth beam to obtain a fifth beam and transmit the fifth beam to the data acquisition device so that the data acquisition device can output a first interference spectrum of the fifth beam.

[0046] The basic working process of a spectrum generation device is as follows:

[0047] The first step involves the light emitted by the light source passing through an optical fiber processor, which splits the light into a first beam and a second beam.

[0048] In the second step, the fiber optic processor transmits the first beam to the delay device and the second beam to the reflection device.

[0049] The third step involves the delay device processing the first beam to obtain the third beam, which is then reflected to the fiber optic processor. The reflection device then reflects the second beam to obtain the fourth beam, which is also reflected to the fiber optic processor.

[0050] The fourth step involves the fiber optic processor combining the third and fourth beams to obtain the fifth beam, which is then transmitted to the acquisition device.

[0051] Fifth step: The acquisition device outputs the first interference spectrum of the fifth beam.

[0052] The light source may include, but is not limited to, tunable light sources and broadband light sources, and the acquisition equipment may include, but is not limited to, spectrometers, monochromators, gratings, CCDs (charge-coupled devices), detectors, and oscilloscopes.

[0053] The spectral generation device in this embodiment requires a low-cost light source and acquisition equipment. It can be completed by simply combining multiple components such as the light source, acquisition equipment, fiber optic processor, delay device and reflection device. The structure is relatively simple and meets the requirements for interference spectrum generation.

[0054] In one embodiment, a spectrum generating apparatus is provided, wherein the reflection device includes any one of a first reflection device, a second reflection device, a third reflection device, and a fourth reflection device.

[0055] like Figure 1 The spectrum generation device shown includes a first reflection device 160, a light source 100, a spectrum acquisition device 110, an optical fiber processor 120, an optical fiber 130, and a delay device 150. The delay device 150 includes an optical fiber collimator 151 and a plane mirror 152. The first reflection device 160 includes a first transmission component 161, a first optical fiber collimator 162, and a first plane mirror 163. The first transmission component 161 is disposed between the optical fiber processor 120 and the first optical fiber collimator 162, and the first plane mirror 163 is located away from the first transmission component 161.

[0056] like Figure 2 The spectrum generation device shown includes a second reflection device 260, a light source 200, a spectrum acquisition device 210, an optical fiber processor 220, an optical fiber 230, and a delay device 250. The delay device 250 includes an optical fiber collimator 251 and a plane mirror 252. The second reflection device 260 includes a second transmission component 261 and a reflective optical fiber 262. The reflective optical fiber 262 is connected to the optical fiber processor 220 through the second transmission component 261.

[0057] like Figure 3 The spectrum generation device shown includes a third reflection device 360, a light source 300, a spectrum acquisition device 310, an optical fiber processor 320, an optical fiber 330, and a delay device 350. The delay device 350 includes an optical fiber collimator 351 and a plane mirror 352. The third reflection device 360 ​​includes a third transmission component 361, a second optical fiber collimator 362, a transmission mirror 363, and a second plane mirror 364. The second optical fiber collimator 362 is connected to the optical fiber processor 320 through the third transmission component 361. The transmission mirror 363 is disposed between the second optical fiber collimator 362 and the second plane mirror 364.

[0058] like Figure 4The spectrum generation device shown includes a fourth reflection device 460, a light source 400, a spectrum acquisition device 410, an optical fiber processor 420, an optical fiber 430, and a delay device 450. The delay device 450 includes an optical fiber collimator 451 and a plane mirror 452. The fourth reflection device 460 includes a fourth transmission component 461, a third optical fiber collimator 462, and a reflection element 463. The third optical fiber collimator 462 is connected to the optical fiber processor 420 through the fourth transmission component 461.

[0059] The reflection device in this embodiment obtains different spectral generation devices through a simple combination of various reflection elements and transmission elements. The implementation method is simple, and the structure of the obtained spectral generation device is also relatively simple.

[0060] In one embodiment, a spectrum generation device is provided, the delay device of which includes an optical fiber collimator and a plane mirror, the position of which is adjustable. By adjusting the position of the plane mirror, the optical path lengths of the two arms of the interference are made approximately equal, so that a fifth beam with better interference fringes can be obtained under the condition of limited light source coherence. Consequently, based on the fifth beam with better interference fringes, the first interference spectrum of the fifth beam output by the acquisition device is also better.

[0061] In one embodiment, a dispersion measurement device is provided, the device including a processing device and a spectrum generation device according to any of the above embodiments.

[0062] The acquisition device is used to send the first interference spectrum to the processing device.

[0063] The acquisition equipment may include, but is not limited to, spectrometers, monochromators, gratings, CCDs (charge-coupled devices), detectors, and oscilloscopes.

[0064] The processing device is used to obtain the phase at each frequency point in the first interference spectrum based on the first interference spectrum and the Bayesian parameter estimation method.

[0065] The processing device can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, and smart in-vehicle systems. Portable wearable devices can include smartwatches, smart bracelets, and head-mounted devices. The processing device can also be implemented using a standalone server or a server cluster consisting of multiple servers.

[0066] Optionally, the phase at each frequency point in the first interference spectrum can be calculated based on the extended Kalman filter method in the first interference spectrum and the Bayesian parameter estimation method.

[0067] The processing device is used to obtain the second-order and third-order dispersion of the first interference spectrum based on the phase at each frequency point in the first interference spectrum and a preset dispersion solution algorithm.

[0068] Optionally, the second-order and third-order dispersions of the first interference spectrum are obtained by calculating based on the phase at each frequency point in the first interference spectrum and the polynomial fitting algorithm in the preset dispersion solution algorithm.

[0069] The dispersion measurement device in this embodiment uses the Bayesian parameter estimation method to solve the phase at each frequency point in the first interference spectrum. The solved phase is relatively accurate. Based on the relatively accurate phase and the preset dispersion solution algorithm, the second-order and third-order dispersion of the first interference spectrum are also relatively accurate.

[0070] In one embodiment, a processing device is used to remove the background spectrum from the first interference spectrum to obtain a second interference spectrum.

[0071] Optionally, the first interference spectrum satisfies formula (1).

[0072] (1)

[0073] In formula (1), These are the spectra of the third beam and the fourth beam, respectively. It is the mid-frequency point of the first interference spectrum. The position of the location It is the angular frequency of the fifth beam. This refers to the unbalanced delay of the third and fourth beams.

[0074] right Background spectrum in The background spectrum is removed to obtain the second interference spectrum, which satisfies formula (2).

[0075] (2)

[0076] In formula (2), It is the residual spectral modulation factor in the first interference spectrum. This is the residual spectral bias in the first interference spectrum. Ideally, after removing the background spectrum, Equals 1, It equals 0. However, considering the imperfections of the experiment, generally... It is not a constant.

[0077] Optionally, formula (2) can be written as formula (3).

[0078] (3)

[0079] In formula (3), A0, A c T d and These are the bias, amplitude, frequency, and phase of a quasi-sine wave. It is Gaussian white noise with zero mean.

[0080] The processing device, if the preset Bayesian parameter estimation method is the extended Kalman filter method, obtains the state variables at each frequency point in the first interferometric spectrum based on the first state transition equation, the first measurement equation, and the second interferometric spectrum. The first state transition equation characterizes the numerical relationship between the state variables at each frequency point in the second interferometric spectrum and the state variables at the next frequency point. The first measurement equation characterizes the numerical relationship between the state variables, noise term, and measured values ​​at each frequency point in the second interferometric spectrum. Assuming the state variables... ,in, , , , Represent the cosine function respectively Conjugate sine function Frequency T d With bias A0.

[0081] Then, the first state transition equations corresponding to the state variables are shown in Equation (4) and Equation (5).

[0082] (4)

[0083] (5)

[0084] In formula (4), State variables The corresponding function is expressed as shown in formula (5).

[0085] In formula (5), x1, x2, x3, and x4 are respectively , , , The first measurement equation is shown in formula (6).

[0086] (6)

[0087] In formula (6), White noise introduced into the measurement process includes electronic noise and optical path noise, etc., where H is the observation matrix. Represents the cosine function , This is the measured value corresponding to the first measurement equation at frequency point v.

[0088] Optionally, if the preset Bayesian parameter estimation method is the extended Kalman filter method, it is necessary to linearize the operator according to the local expansion of f(x) in formula (5), thereby realizing the extended Kalman filter. Define the Jacobian matrix of f(x) as shown in formula (7).

[0089] (7)

[0090] The formulas for calculating the state quantities at each frequency point in the first interference spectrum are shown in formulas (8), (9), and (10).

[0091] (8)

[0092] (9)

[0093] (10)

[0094] In formula (8), State variables The corresponding function is calculated using formula (5). Let H be the measured value corresponding to the first measurement equation, and its calculation formula is shown in formula (6). H is the observation matrix. Let v be the gain at frequency v, and its calculation formula is shown in formula (9).

[0095] In formula (9), H is the observation matrix. The transpose of the observation matrix H, Let be the covariance matrix at frequency point v, and its calculation formula is shown in formula (10). r is the covariance matrix of the measurement noise, which is a 1*1 matrix. Let v be the gain at frequency v.

[0096] In formula (10), Let f(x) be the Jacobian matrix. Let v be the covariance matrix at frequency point v. Let r be the gain at frequency v, H be the observation matrix, and Q be the covariance matrix of the process noise. In this embodiment, Q is set as a diagonal matrix, Q = Diagonal[0, 0, q1, q2]. During calculation, the values ​​of r, q1, and q2 need to be set according to the actual situation. P(0) is the identity matrix, i.e., P(0) = Diagonal[1,1,1,1].

[0097] The processing device is used to obtain the phase at each frequency point of the first interference spectrum based on the equation set corresponding to the state quantities and phases at each frequency point in the first interference spectrum.

[0098] Optionally, the formula for calculating the phase at each frequency point in the first interference spectrum is shown in formula (11).

[0099] (11)

[0100] In formula (11), For frequency point The position of the location , , , Represent the cosine function respectively Conjugate sine function Frequency T d With the bias A0, I is the imaginary unit.

[0101] Based on formulas (8), (9) and (10), the state variables at each frequency point in the first interference spectrum can be obtained. And thus obtain , , , Then, based on formula (11), the phase at each frequency point in the first interference spectrum is obtained.

[0102] In this embodiment, the processing device is used to remove the background spectrum from the first interference spectrum to obtain the second interference spectrum; the processing device is used to obtain the state variables at each frequency point in the first interference spectrum based on the first state transition equation, the first measurement equation, and the second interference spectrum, if the preset Bayesian parameter estimation method is the extended Kalman filter method; the processing device is used to obtain the phase at each frequency point in the first interference spectrum based on the state variables at each frequency point in the first interference spectrum and the corresponding equation set. The extended Kalman filter method is suitable for the calculation of nonlinear systems. When solving the nonlinear first state transition equation, first measurement equation, and phase equation set, the computational load is small, and the calculation results are relatively accurate. Therefore, the phase at each frequency point in the final obtained first interference spectrum is also relatively accurate.

[0103] In one embodiment, the processing device is configured to perform an unscented transformation on the first state variable in the first state transition equation to obtain the second state variable if the preset Bayesian parameter estimation method is an unscented Kalman filter method.

[0104] Optionally, the first state transition equation is shown in equations (4) and (5), and the first state variables in the first state transition equation are... A traceless transformation is performed to obtain the second state variables, which include the variables shown in formulas (12), (13), (14), (15), (16), (17), and (18). .

[0105] (12)

[0106] (13)

[0107] (14)

[0108] (15)

[0109] (16)

[0110] (17)

[0111] (18)

[0112] In formulas (12), (13), and (14), All are intermediate variables. This is the initial value for the intermediate variable.

[0113] In formula (12), This is the state variable at frequency point v-1.

[0114] In formula (13), N is the state variable. The number of elements in the middle, in this embodiment N=4. For parameters, ,in, The value is generally in the range of 10. -3 Between 1 and 1 Let v be the covariance matrix at frequency v-1. For matrix The i-th column.

[0115] In formula (14), N is the state variable. The number of elements in the middle, in this embodiment N=4. For parameters, ,in, The value is generally in the range of 10. -3 Between 1 and 1 Let v be the covariance matrix at frequency v-1. For matrix The i-th column.

[0116] In formula (15), Let v be the covariance matrix at frequency v-1. For matrix The i-th column is For matrix The transpose of .

[0117] In formulas (16), (17), (18), All are weighting coefficients.

[0118] In formula (16), N is the state variable. The number of elements in the middle, in this embodiment N=4. For parameters, .

[0119] In formula (17), N is the state variable. The number of elements in the middle, in this embodiment N=4. For parameters, , The value is generally in the range of 10. -3 Between 1 and 1.

[0120] In formula (18), N is the state variable. The number of elements in the middle, in this embodiment N=4. For parameters, .

[0121] The processing device is used to process the second state quantity to obtain the third state quantity.

[0122] Optionally, the variables shown in formulas (12), (13), (14), (15), (16), (17), and (18) are... After processing, the third state variables are obtained, which include the states shown in formulas (19), (20), (21), (22), (23), (24), and (25).

[0123] (19)

[0124] (20)

[0125] (twenty one)

[0126] (twenty two)

[0127] (twenty three)

[0128] (twenty four)

[0129] (25)

[0130] In formula (19), As an intermediate variable, intermediate variables The corresponding function.

[0131] In formula (20), As an intermediate variable, These are the weighting coefficients. For the state variables at frequency v-1, the state variables at frequency v are solved by the transition matrix.

[0132] In formula (21), These are the weighting coefficients. As an intermediate variable, Let r be the state variable at frequency v-1, and q1 be the state variable at frequency v obtained by solving the transition matrix. Let Q be the covariance matrix of the process noise. In this embodiment, Q is set as a diagonal matrix: Q = Diagonal[0, 0, q1, q2]. During calculation, the values ​​of r, q1, and q2 need to be set according to the actual situation. It is an intermediate variable.

[0133] In formula (22), As an intermediate variable, For function h The corresponding function value.

[0134] In formula (23), These are the weighting coefficients. For function h The corresponding function value, This represents the transfer of the function value corresponding to the first measurement equation at frequency v relative to the function value corresponding to the first measurement equation at frequency v-1.

[0135] In formula (24), For function h The corresponding function value, The first measurement equation at frequency v is the function value at frequency v, obtained by solving the first measurement equation at frequency v-1 using the transition matrix. Here, r represents the weighting coefficients, and r is the covariance matrix of the measurement noise, which is a 1x1 matrix. It is an intermediate variable.

[0136] In formula (25), As an intermediate variable, For function h The corresponding function value, This represents the transition amount of the state variable at frequency v relative to the state variable at frequency v-1. These are the weighting coefficients. These are the measured values ​​corresponding to the first measurement equation at frequency v-1, and the function values ​​corresponding to the first measurement equation at frequency v, solved using the transition matrix. It is an intermediate variable.

[0137] The processing device is used to solve the equations of the phase based on the third state quantity, the first state transition equation, and the first measurement equation to obtain the state quantity at each frequency point in the first interference spectrum.

[0138] Optionally, the formulas for calculating the state quantities at each frequency point in the first interference spectrum are shown in formulas (26), (27), and (28).

[0139] (26)

[0140] (27)

[0141] (28)

[0142] In formula (26), For intermediate variables, where, The calculation formula is shown in formula (25). The calculation formula is shown in formula (24), where K is the gain matrix.

[0143] In formula (27), As an intermediate variable, its calculation formula is shown in formula (25), where K is the gain matrix. This is the transpose of the gain matrix K. Let v be the covariance matrix at frequency point v. As an intermediate variable, its calculation formula is shown in formula (21).

[0144] In formula (28), K is the gain matrix. Based on the state variables at frequency v-1, and the state variables at frequency v calculated using the transition matrix, The first measurement equation at frequency v-1 is the measured value, and the second measurement equation at frequency v is the measured value obtained by solving the first measurement equation at frequency v using the transition matrix. This is the measured value corresponding to the first measurement equation at frequency point v.

[0145] The processing device is used to obtain the phase at each frequency point of the first interference spectrum based on the equation set corresponding to the state quantities and phases at each frequency point in the first interference spectrum.

[0146] Optionally, the phase equations are solved according to formulas (12), (13), (14), (15), (16), (17), and (18) to obtain the state variables at each frequency point in the first interference spectrum. And thus obtain Then, based on formula (11), the phase at each frequency point in the first interference spectrum is obtained.

[0147] Alternatively, it is assumed that the first interference spectrum includes frequency points. The phase at each frequency point in the first interference spectrum obtained based on formula (11) In this embodiment, the processing device is used to perform an unscented transformation on the first state variable in the first state transition equation to obtain a second state variable if the preset Bayesian parameter estimation method is the unscented Kalman filtering method; the processing device is used to process the second state variable to obtain a third state variable; the processing device is used to obtain the state variable at each frequency point in the first interference spectrum based on the third state variable, the first state transition equation, and the first measurement equation; the processing device is used to obtain the phase at each frequency point in the first interference spectrum based on the state variable at each frequency point in the first interference spectrum and the corresponding equation set. The unscented Kalman filtering method, while adapting to nonlinear system calculations, can better handle noise in the system. Therefore, when solving the phase equation set using the unscented Kalman filtering method, the error is smaller and the accuracy is higher.

[0148] In one embodiment, the processing device is configured to perform Taylor series expansion on the phase at each frequency point in the first interference spectrum if the preset dispersion solution algorithm is a polynomial fitting algorithm, thereby obtaining the second-order dispersion and third-order dispersion of the first interference spectrum.

[0149] Optionally, the phase of the mid-frequency point in the first interference spectrum At frequency Perform a Taylor series expansion at the point, and the expanded result is shown in formula (29).

[0150] (29)

[0151] In formula (29), and That is, frequency point The second and third order dispersions at that point. It is a constant. Phase At frequency The first derivative at that point, Phase At frequency The second derivative at that point, Phase At frequency The third derivative at point , and so on.

[0152] Optionally, the phase at each frequency point in the first interference spectrum is expanded using a Taylor series to obtain the second-order dispersion and third-order dispersion at each frequency point in the first interference spectrum. Based on the second-order dispersion and third-order dispersion at each frequency point in the first interference spectrum, the second-order dispersion and third-order dispersion of the first interference spectrum are obtained.

[0153] In this embodiment, the processing device is used to perform Taylor series expansion on the phase at each frequency point in the first interference spectrum if the preset dispersion solution algorithm is a polynomial fitting algorithm, thereby obtaining the second-order and third-order dispersion of the first interference spectrum. Taylor series expansion is relatively simple and straightforward, and the accuracy of the second-order and third-order dispersions of the first interference spectrum obtained based on Taylor series is also relatively high.

[0154] In one embodiment, the processing device is configured to, if the preset dispersion solving algorithm is a Kalman filter algorithm, solve the Kalman filter equation set used for dispersion calculation according to the second state transition equation and the second measurement equation to obtain the second-order dispersion and third-order dispersion of the first interference spectrum; the second state transition equation characterizes the numerical relationship between the phase at each frequency point in the first interference spectrum and the phase at the next frequency point, and the second measurement equation characterizes the numerical relationship between the phase, noise term and measured value at each frequency point in the first interference spectrum.

[0155] Optionally, the second measurement equation is shown in formula (30).

[0156] y(k)=Φ(k)+n(k) (30)

[0157] In formula (30), n(k) represents the noise term of the first interference spectrum, Φ(k) represents the phase of the first interference spectrum at frequency k, and y(k) represents the phase measurement value corresponding to the second measurement equation at frequency k.

[0158] Optionally, assume that the state variable x(k) = [Φ(k), Φ'(k), Φ''(k), Φ'''(k)], and that the components Φ(k), Φ'(k), Φ''(k), Φ'''(k) in x(k) satisfy formulas (31), (32), (33), and (34).

[0159] Φ(k+1)=Φ(k)+Φ'(k)+1 / 2!*Φ''(k)+1 / 3!*Φ'''(k) (31)

[0160] Φ'(k+1)=Φ'(k)+Φ''(k)+1 / 2!*Φ'''[k] (32)

[0161] Φ''(k+1)=Φ''(k)+Φ''(k) (33)

[0162] Φ'''(k+1)=Φ'''(k) (34)

[0163] Wherein, Φ(k) represents the phase of the first interference spectrum at frequency k, Φ'(k) represents the first derivative of the phase of the first interference spectrum at frequency k, Φ''(k) represents the second derivative of the phase of the first interference spectrum at frequency k, and Φ'''(k) represents the third derivative of the phase of the first interference spectrum at frequency k.

[0164] The second state transition equation is shown in equations (35) and (36).

[0165] (35)

[0166] (36)

[0167] In formula (35), T is a preset matrix. The state variables at frequency k-1 are the state variables at position k calculated using the transition matrix. These are the state variables based on the k-1 frequency point, calculated using the transition matrix at the k-1 position.

[0168] In formula (36), T is a preset matrix.

[0169] Optionally, the Kalman filter equations used for dispersion calculation are shown in equations (37), (38), and (39).

[0170] (37)

[0171] (38)

[0172] (39)

[0173] In formula (37), H is the observation matrix. Here, A represents the state variables at frequency k-1, calculated using the transition matrix, and A is the observation matrix. Let y(k) be the gain at frequency k, y(k) be the function value corresponding to the second measurement equation at frequency k, and H be the observation matrix. The state variable at frequency k is calculated from the transition matrix.

[0174] In formula (38), The gain at frequency k, Let H be the covariance matrix at frequency point k, and H be the observation matrix. The transpose of the observation matrix H is r, which is the covariance matrix of the measurement noise. In this embodiment, r is a 1*1 matrix.

[0175] In formula (39), Let A be the covariance matrix at frequency point k+1, and let A be the observation matrix. The gain at frequency k, Let k be the covariance matrix at frequency point k. Q is the transpose of the observation matrix A, and Q is the covariance matrix of the process noise, as shown in formula (40).

[0176] (40)

[0177] Based on the second state transition equation and the second measurement equation, the Kalman filter equations used for dispersion calculation are solved to obtain the state variables at each frequency point in the first interference spectrum. Then, Φ''(k) and Φ'''(k) at each frequency point in the first interference spectrum are obtained. Φ''(k) at each frequency point in the first interference spectrum is taken as the second-order dispersion at that frequency point, and Φ'''(k) at each frequency point in the first interference spectrum is taken as the third-order dispersion at that frequency point.

[0178] Based on the second-order and third-order dispersions at each frequency point in the first interference spectrum, the second-order and third-order dispersions of the first interference spectrum are obtained.

[0179] In this embodiment, the processing device, if the preset dispersion solving algorithm is the Kalman filter algorithm, solves the Kalman filter equations used for dispersion calculation according to the second state transition equation and the second measurement equation to obtain the second-order and third-order dispersion of the first interference spectrum. The second state transition equation characterizes the numerical relationship between the phase at each frequency point in the first interference spectrum and the phase at the next frequency point, and the second measurement equation characterizes the numerical relationship between the phase, noise term, and measured value at each frequency point in the first interference spectrum. The Kalman filter algorithm considers the influence of the noise term and is computationally efficient; the second-order and third-order dispersion of the first interference spectrum obtained by the Kalman filter algorithm are relatively accurate.

[0180] In one exemplary embodiment, a dispersion measurement apparatus is provided, the apparatus including a processing device and a spectrum generation apparatus of any of the embodiments described above.

[0181] The acquisition device is used to send the first interference spectrum to the processing device.

[0182] Processing equipment, used to perform the following steps, the process flow is as follows: Figure 5 As shown:

[0183] Step 501: If the preset Bayesian parameter estimation method is the extended Kalman filter method, then based on the first state transition equation, the first measurement equation, and the second interference spectrum, obtain the state variables at each frequency point in the first interference spectrum. Then execute step 505.

[0184] Step 502: If the preset Bayesian parameter estimation method is the unscented Kalman filter method, then the first state variable in the first state transition equation is subjected to an unscented transformation to obtain the second state variable.

[0185] Step 503: Process the second state variable to obtain the third state variable.

[0186] Step 504: Solve the equations for the phase based on the third state variable, the first state transition equation, and the first measurement equation to obtain the phase at each frequency point in the first interference spectrum.

[0187] Step 505: Based on the state variables and phase-corresponding equations at each frequency point in the first interference spectrum, obtain the phase at each frequency point in the first interference spectrum.

[0188] Step 506: If the preset dispersion solution algorithm is a polynomial fitting algorithm, then the phase at each frequency point in the first interference spectrum is expanded by Taylor series to obtain the second-order dispersion and third-order dispersion of the first interference spectrum.

[0189] Step 507: If the preset dispersion solution algorithm is the Kalman filter algorithm, then according to the second state transition equation and the second measurement equation, the Kalman filter equation set used for dispersion calculation is solved to obtain the second-order dispersion and third-order dispersion of the first interference spectrum; the second state transition equation characterizes the numerical relationship between the phase at each frequency point in the first interference spectrum and the phase at the next frequency point, and the second measurement equation characterizes the numerical relationship between the phase, noise term and measurement value at each frequency point in the first interference spectrum.

[0190] In one embodiment, a dispersion measurement method is provided, the process of which is as follows: Figure 6 As shown, the method includes:

[0191] Step 602: Obtain the first interference spectrum sent by the acquisition device.

[0192] The acquisition equipment may include, but is not limited to, spectrometers, monochromators, gratings, CCDs (charge-coupled devices), detectors, and oscilloscopes.

[0193] Optionally, the first interference spectrum sent by the acquisition device can be acquired via computer equipment.

[0194] Step 604: Based on the first interference spectrum and the Bayesian parameter estimation method, obtain the phase at each frequency point in the first interference spectrum; the Bayesian parameter estimation method is either the unscented Kalman filter method or the extended Kalman filter method.

[0195] Optionally, the background spectrum in the first interference spectrum is removed by computer equipment to obtain the second interference spectrum; the first state transition equation is obtained based on the second interference spectrum, and the first measurement equation is obtained based on the second interference spectrum and the noise term; if the preset Bayesian parameter estimation method is the extended Kalman filter method, the equations for the phase are solved based on the first state transition equation and the first measurement equation to obtain the phase at each frequency point in the first interference spectrum.

[0196] Step 606: Based on the phase at each frequency point in the first interference spectrum and the preset dispersion solution algorithm, obtain the second-order and third-order dispersion of the first interference spectrum; the preset dispersion solution algorithm is any one of the polynomial fitting algorithm and the Kalman filtering algorithm.

[0197] Optionally, if the preset dispersion solution algorithm is a polynomial fitting algorithm, then the phase at each frequency point in the first interference spectrum is expanded by Taylor series to obtain the second-order and third-order dispersion of the first interference spectrum.

[0198] In this embodiment, a first interference spectrum transmitted by a data acquisition device is acquired. Based on the first interference spectrum and a Bayesian parameter estimation method, the phase at each frequency point in the first interference spectrum is obtained. Based on the phase at each frequency point in the first interference spectrum and a preset dispersion solving algorithm, the second-order and third-order dispersions of the first interference spectrum are obtained. The phase at each frequency point in the first interference spectrum is calculated using the Bayesian parameter estimation method, resulting in relatively accurate values. Based on these relatively accurate phases, the second-order and third-order dispersions of the first interference spectrum obtained by combining them with the preset dispersion solving algorithm are also relatively accurate.

[0199] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0200] Based on the same inventive concept, this application also provides a dispersion measuring device for implementing the dispersion measuring method described above. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations in one or more dispersion measuring device embodiments provided below can be found in the limitations of the dispersion measuring method described above, and will not be repeated here.

[0201] In one embodiment, such as Figure 7 As shown, a dispersion measurement device 700 is provided, which is disposed in a processing device and includes: a spectrum acquisition module 720, a phase calculation module 740, and a dispersion measurement module 760, wherein:

[0202] The spectrum acquisition module 720 is used to acquire the first interference spectrum sent by the acquisition device.

[0203] Phase calculation module 740 is used to obtain the phase at each frequency point in the first interference spectrum based on the first interference spectrum and the Bayesian parameter estimation method; the Bayesian parameter estimation method is either the unscented Kalman filtering method or the extended Kalman filtering method.

[0204] The dispersion measurement module 760 is used to obtain the second-order and third-order dispersion of the first interference spectrum based on the phase at each frequency point in the first interference spectrum and the preset dispersion solution algorithm; the preset dispersion solution algorithm is any one of the polynomial fitting algorithm and the Kalman filtering algorithm.

[0205] Each module in the aforementioned dispersion measurement device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0206] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0207] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0208] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A dispersion measuring device, characterized in that, The device includes a processing unit and a spectrum generation unit; the spectrum generation unit includes a data acquisition unit. The acquisition device is used to send a first interference spectrum to the processing device; The processing device is used to obtain the phase at each frequency point in the first interference spectrum based on the first interference spectrum and the Bayesian parameter estimation method; The processing device is used to obtain the second-order and third-order dispersion of the first interference spectrum based on the phase at each frequency point in the first interference spectrum and a preset dispersion solution algorithm. The processing device is used to remove the background spectrum from the first interference spectrum to obtain the second interference spectrum; The processing device is used to obtain the state variables at each frequency point in the first interference spectrum based on the first state transition equation, the first measurement equation, and the second interference spectrum if the preset Bayesian parameter estimation method is the extended Kalman filter method. The first state transition equation characterizes the numerical relationship between the state quantities at each frequency point in the second interference spectrum and the state quantities at the next frequency point, and the first measurement equation characterizes the numerical relationship between the state quantities, noise terms and measured values ​​at each frequency point in the second interference spectrum. The processing device is used to obtain the phase at each frequency point of the first interference spectrum based on the state quantities and phase corresponding equations at each frequency point in the first interference spectrum; The spectrum generation device also includes a light source, an optical fiber processor, a delay device, and a reflection device; The fiber optic processor is used to split the light emitted by the light source into a first beam and a second beam, and to transmit the first beam to the delay device and the second beam to the reflection device. The delay device is used to process the first beam to obtain a third beam and reflect the third beam to the fiber optic processor; The reflecting device is used to reflect the second beam to obtain a fourth beam, and then reflect the fourth beam to the fiber optic processor. The fiber optic processor is used to combine the third beam and the fourth beam to obtain a fifth beam, and transmit the fifth beam to the acquisition device so that the acquisition device can output the first interference spectrum of the fifth beam.

2. The apparatus according to claim 1, characterized in that, The reflection device includes any one of a first reflection device, a second reflection device, a third reflection device, and a fourth reflection device. The first reflection device includes a first transmission component, a first fiber collimator, and a first planar reflector. The first transmission component is disposed between the fiber processor and the first fiber collimator, and the first planar reflector is away from the first transmission component. The second reflection device includes a second transmission component and a reflective optical fiber, the reflective optical fiber being connected to the optical fiber processor through the second transmission component; The third reflection device includes a third transmission component, a second fiber collimator, a transmission mirror, and a second plane mirror. The second fiber collimator is connected to the fiber processor through the third transmission component, and the transmission mirror is disposed between the second fiber collimator and the second plane mirror. The fourth reflection device includes a fourth transmission component, a third fiber collimator, and a reflection element. The third fiber collimator is connected to the fiber processor through the fourth transmission component.

3. The apparatus according to claim 1, characterized in that, The delay device includes an optical fiber collimator and a plane mirror, the position of which is adjustable.

4. The apparatus according to claim 1, characterized in that, The processing device is used to perform Taylor series expansion on the phase at each frequency point in the first interference spectrum if the preset dispersion solution algorithm is a polynomial fitting algorithm, so as to obtain the second-order dispersion and third-order dispersion of the first interference spectrum.

5. The apparatus according to claim 1, characterized in that, The processing device is used to solve the Kalman filter equation set for dispersion calculation according to the second state transition equation and the second measurement equation if the preset dispersion solution algorithm is a Kalman filter algorithm, so as to obtain the second-order dispersion and third-order dispersion of the first interference spectrum. The second state transition equation characterizes the numerical relationship between the phase at each frequency point in the first interference spectrum and the phase at the next frequency point. The second measurement equation characterizes the numerical relationship between the phase, noise term, and measured value at each frequency point in the first interference spectrum.

6. A dispersion measurement method, characterized in that, Applied to the apparatus of claim 1, the method comprises: Acquire the first interference spectrum sent by the acquisition device; Based on the first interference spectrum and the Bayesian parameter estimation method, the phase at each frequency point in the first interference spectrum is obtained; the Bayesian parameter estimation method is either the unscented Kalman filter method or the extended Kalman filter method. Based on the phase at each frequency point in the first interference spectrum and the preset dispersion solution algorithm, the second-order and third-order dispersions of the first interference spectrum are obtained; the preset dispersion solution algorithm is any one of the polynomial fitting algorithm and the Kalman filtering algorithm.

Images