Method for sub-rotational angle calibration of noise equivalent irradiance of infrared spectrometers

Abstract

This invention provides a rotation-angle-based calibration method for the noise equivalent radiance of an infrared spectrometer, belonging to the field of optoelectronic information acquisition and processing. The method includes: acquiring spectral data corresponding to multiple blackbody temperature points at a target ambient temperature; acquiring calibration datasets corresponding to each rotation angle based on the spectral data corresponding to the multiple blackbody temperature points; acquiring the noise spectrum DN value of the rotating interferometric infrared spectrometer based on the spectral data corresponding to each blackbody temperature point; calculating the system response gain of one or more first blackbody temperature points at each rotation angle based on the calibration datasets corresponding to each rotation angle; and calibrating the noise equivalent radiance corresponding to each first blackbody temperature point at each rotation angle based on the noise spectrum DN value and the system response gain corresponding to each first blackbody temperature point. By calibrating the noise equivalent radiance by rotation angle, measurement data can be corrected more accurately.

Description

Technical Field

[0001] This invention belongs to the field of optoelectronic information acquisition and processing, and more specifically, relates to a method for calibrating the rotation angle of the noise equivalent radiance of an infrared spectrometer. Background Technology

[0002] An infrared Fourier transform spectrometer is a spectral information acquisition and analysis device based on interferometric measurement. Compared with traditional dispersive infrared spectrometers, it has advantages such as high spectral recognition accuracy and high signal-to-noise ratio, and is widely used in environmental gas monitoring, fire protection, and explosive detection. When using a Fourier transform spectrometer to remotely sense a target, the infrared radiation of the target undergoes absorption and scattering after passing through the atmosphere. The environmental background signal and the instrument's own radiation signal are loaded into the weak target signal, significantly affecting the accuracy of target signal feature extraction and identification. For the detection of more distant and weaker targets, reducing the instrument's equivalent noise or accurately characterizing and correcting the instrument's equivalent noise to improve the signal-to-noise ratio is currently the most important technical approach. Noise Equivalent Spectral Radiance (NESR) is an important parameter of this type of instrument, measuring the limit of the Fourier transform spectrometer's ability to detect target signals. Accurate NESR calibration helps improve the equipment's development level and the quantitative application of remote sensing data, determining the usability of the measurement results.

[0003] Figure 1 This is a schematic diagram of a rotating interferometric optical structure provided by existing technology. The spectral detector can use a high-speed Fourier transform infrared spectrometer (a type of rotating interferometric infrared spectrometer). This spectrometer employs a rotating interferometer (its optical structure is shown below). Figure 1 As shown in the figure, it contains a rotating refractive mirror R.

[0004] The incident light enters through the field stop FS, passes through the correction lens LC, and is projected onto the beam splitter (beam splitter) BS (semi-reflective and semi-transparent). Half of the light is reflected to the top mirror M, then passes through the rotating refracting mirror R, and finally is reflected by the bottom mirror ME, and then reflected back to the beam splitter BS along the original path.

[0005] The other half of the light rays incident on the beam splitter BS are reflected by the bottom mirror M, transmitted by the rotating refracting mirror R, reflected by the top mirror ME, and then reflected back to the beam splitter BS along the original path.

[0006] The two reflected rays are recombined (phase interference) at the beam splitter BS, and then focused by the focusing lens LF onto the spectral detector D.

[0007] The interfering light is converted into a voltage signal by the detector. As the rotating refracting mirror R rotates, different optical path differences (OPD) are formed. One rotation produces four points with an optical path difference of 0, so a total of four frames of interference spectrum are collected for each rotation.

[0008] Traditional Fourier transform infrared (FTIR) spectrometers typically employ one-point or multi-point linear calibration methods for equipment noise calibration, which largely enhance the instrument's accuracy. However, in practical applications, the rotational nature of Fourier transform infrared spectrometers leads to variations in spectral data obtained at different angles. The problem persists due to systematic errors that may arise at specific angles, resulting in reduced accuracy of measurement results and significant errors in existing calibration methods. Summary of the Invention

[0009] This invention provides a method for calibrating the noise equivalent radiance of an infrared spectrometer by rotation angle, which solves the defect in the prior art where systematic errors at a specific angle lead to a decrease in the accuracy of the measurement results. It realizes calibration by rotation angle and can select the corresponding calibration curve according to the current measurement angle, thereby correcting the measurement data more accurately.

[0010] To achieve the above objectives, in a first aspect, the present invention provides a method for calibrating the rotation angle of the noise equivalent radiance of an infrared spectrometer, comprising:

[0011] At the target ambient temperature, the spectra of blackbodies at different temperatures are collected using a rotating interferometric infrared spectrometer to obtain spectral data corresponding to multiple blackbodies at different temperature points.

[0012] Based on the spectral data corresponding to multiple blackbody temperature points, the spectral data are classified according to multiple rotation angles of the rotating interferometric infrared spectrometer to obtain calibration datasets corresponding to each rotation angle. The calibration datasets include the spectral data corresponding to multiple blackbody temperature points at the corresponding rotation angles.

[0013] Based on the spectral data corresponding to each blackbody temperature point, the noise spectrum digital signal value DN of the rotating interferometric infrared spectrometer is obtained by wavelet transform.

[0014] Based on the calibration dataset corresponding to each rotation angle, calculate the system response gain corresponding to one or more first blackbody temperature points under each rotation angle;

[0015] For each rotation angle, based on the noise spectrum DN value and the system response gain corresponding to each of the first blackbody temperature points, the noise equivalent radiance corresponding to each of the first blackbody temperature points is calibrated at each rotation angle.

[0016] Optionally, calculating the system response gain corresponding to one or more first blackbody temperature points at each rotation angle based on the calibration dataset corresponding to each rotation angle includes:

[0017] Based on the calibration dataset corresponding to the target rotation angle, extract the spectral data corresponding to the second blackbody temperature point and the third blackbody temperature point at the target rotation angle. The target rotation angle is any one of the multiple rotation angles. The second blackbody temperature point is lower than the first blackbody temperature point, and the third blackbody temperature point is higher than the first blackbody temperature point.

[0018] Based on the spectral data corresponding to the second blackbody temperature point and the third blackbody temperature point at the target rotation angle, the system response gain corresponding to the first blackbody temperature point at the target rotation angle is determined by two-point linear calibration.

[0019] Optionally, the determination of the system response gain corresponding to the first blackbody temperature point at the target rotation angle based on the spectral data corresponding to the second and third blackbody temperature points at the target rotation angle through two-point linear calibration specifically includes calculating the system response gain using the following formula:

[0020]

[0021] Where Gain(λ) represents the system response gain, DN L (λ) represents the spectral data corresponding to the second blackbody temperature point, S L (λ) represents the preset theoretical blackbody radiance corresponding to the second blackbody temperature point, DN H (λ) represents the spectral data corresponding to the third blackbody temperature point, S H (λ) represents the theoretical radiance of the blackbody corresponding to the third blackbody temperature point, and λ represents the wavelength.

[0022] Optionally, after extracting the spectral data corresponding to the second blackbody temperature point and the third blackbody temperature point at the target rotation angle from the calibration dataset corresponding to the target rotation angle, the method further includes:

[0023] Based on the spectral data corresponding to the second blackbody temperature point and the third blackbody temperature point at the target rotation angle, the radiation bias corresponding to the first blackbody temperature point at the target rotation angle is determined by two-point linear calibration.

[0024] Based on the radiation bias corresponding to the first blackbody temperature point under the target rotation angle, Gaussian filtering is performed to obtain the filtered radiation bias corresponding to the first blackbody temperature point under the target rotation angle.

[0025] Optionally, the step of determining the radiation bias corresponding to the first blackbody temperature point at the target rotation angle based on the spectral data corresponding to the second and third blackbody temperature points at the target rotation angle through two-point linear calibration specifically includes calculating the radiation bias using the following formula:

[0026]

[0027] Where offset(λ) represents the radiation bias, DN L (λ) represents the spectral data corresponding to the second blackbody temperature point, S L (λ) represents the preset theoretical blackbody radiance corresponding to the second blackbody temperature point, DN H (λ) represents the spectral data corresponding to the third blackbody temperature point, S H (λ) represents the theoretical radiance of the blackbody corresponding to the third blackbody temperature point, and λ represents the wavelength.

[0028] Optionally, for each of the rotation angles, the calibration of the noise equivalent radiance corresponding to each of the first blackbody temperature points at each rotation angle, based on the noise spectrum DN value and the system response gain corresponding to each of the first blackbody temperature points, specifically includes calibrating the noise equivalent radiance using the following formula:

[0029]

[0030] Among them, S input_noise (λ) represents the noise equivalent radiance, DN noise (λ) represents the DN value at the noise wavelength λ, and k(λ) represents the system response gain at the wavelength λ.

[0031] Optionally, the spectral data corresponding to each blackbody temperature point covers the following bands: near-infrared band, short-wave band, mid-wave band, and long-wave band. Correspondingly, the noise equivalent radiance corresponding to the first blackbody temperature point includes: noise equivalent radiance of the near-infrared band, noise equivalent radiance of the short-wave band, noise equivalent radiance of the mid-wave band, and noise equivalent radiance of the long-wave band.

[0032] Secondly, the present invention also provides a calibration device for the rotation angle of the noise equivalent radiance of an infrared spectrometer, comprising:

[0033] The acquisition module is used to acquire the spectrum of a blackbody at different temperatures using a rotating interferometric infrared spectrometer under the target ambient temperature, and obtain the spectral data corresponding to multiple blackbody temperature points respectively;

[0034] The calibration dataset acquisition module is used to classify the spectral data according to the multiple rotation angles of the rotating interferometric infrared spectrometer based on the spectral data corresponding to multiple blackbody temperature points, and to acquire the calibration dataset corresponding to each rotation angle. The calibration dataset includes the spectral data corresponding to multiple blackbody temperature points at the corresponding rotation angle.

[0035] The wavelet transform module is used to obtain the noise spectrum digital signal value DN of the rotating interferometric infrared spectrometer based on the spectral data corresponding to each blackbody temperature point through wavelet transform.

[0036] The system response gain calculation module is used to calculate the system response gain corresponding to one or more first blackbody temperature points at each rotation angle based on the calibration dataset corresponding to each rotation angle.

[0037] The calibration module is used to calibrate the noise equivalent radiance corresponding to each of the first blackbody temperature points at each of the rotation angles, based on the noise spectrum DN value and the system response gain corresponding to each of the first blackbody temperature points.

[0038] Thirdly, the present invention provides an electronic device comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation thereof.

[0039] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0040] It is understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0041] In summary, the technical solutions conceived by this invention have the following beneficial effects compared with the prior art:

[0042] By classifying spectral data according to rotation angles, calibration datasets corresponding to each rotation angle can be obtained. Then, the system response gain can be calculated according to rotation angle to determine the system response gain corresponding to one or more blackbody temperature points at each rotation angle. Furthermore, by combining the noise spectrum DN value obtained by wavelet transform, the noise equivalent radiance corresponding to the blackbody temperature point can be calibrated according to rotation angle. This not only considers the overall linearity of the spectrometer but also makes full use of the similarity at different angles. This allows for the selection of the appropriate calibration curve based on the current measurement angle in actual measurements, thereby more accurately correcting the measurement data. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0044] Figure 1 This is a schematic diagram of a rotational interference optical structure provided by existing technology;

[0045] Figure 2 This is a flowchart illustrating the method for calibrating the noise equivalent radiance of an infrared spectrometer by rotation angle provided by the present invention.

[0046] Figure 3 This is a schematic diagram of the device noise signal after wavelet decomposition provided by the present invention;

[0047] Figure 4 This is a schematic diagram of the near-infrared band noise curve after wavelet decomposition provided by the present invention;

[0048] Figure 5 This is a schematic diagram of the shortwave band noise curve after wavelet decomposition provided by the present invention;

[0049] Figure 6 This is a schematic diagram of the mid-wave band noise curve after wavelet decomposition provided by the present invention;

[0050] Figure 7 This is a schematic diagram of the long-wave band noise curve after wavelet decomposition provided by the present invention;

[0051] Figure 8 This is a schematic diagram of the system gain curves calculated from the blackbody temperature of 0℃ and 50℃ at the first angle provided by the present invention;

[0052] Figure 9 This is one of the first schematic diagrams of angle gain band sub-band provided by the present invention;

[0053] Figure 10This is the second schematic diagram of the first angle gain band sub-curve provided by the present invention;

[0054] Figure 11 This is the third schematic diagram of the first angle gain band sub-curve provided by the present invention;

[0055] Figure 12 This is the fourth schematic diagram of the first angle gain band sub-band curve provided by the present invention;

[0056] Figure 13 This is a schematic diagram of the system radiation bias obtained by calculating the blackbody temperature at 0℃ and 50℃ at the first angle provided by the present invention;

[0057] Figure 14 This is a schematic diagram of the system gain curves calculated from the blackbody temperature of 0℃ and 50℃ at the second angle provided by the present invention;

[0058] Figure 15 This is a schematic diagram of the system radiation bias calculated from the blackbody temperature of 0℃ and 50℃ at the second angle provided by the present invention;

[0059] Figure 16 This is a schematic diagram of the system gain curves calculated from the blackbody temperature of 0℃ and 50℃ at the third angle provided by the present invention;

[0060] Figure 17 This is a schematic diagram of the system radiation bias calculated from the blackbody temperature of 0℃ and 50℃ at the third angle provided by the present invention;

[0061] Figure 18 This is a schematic diagram of the system gain curves calculated from the blackbody temperature of 0℃ and 50℃ at the fourth angle provided by the present invention.

[0062] Figure 19 This is a schematic diagram of the system radiation bias obtained from the calculation of the blackbody temperature at 0℃ and 50℃ at the fourth angle provided by the present invention.

[0063] Figure 20 This is a schematic diagram of the noise equivalent amplitude brightness curve at the first angle provided by the present invention;

[0064] Figure 21 This is a schematic diagram of the equivalent amplitude brightness curve of near-infrared noise at the first angle provided by the present invention;

[0065] Figure 22 This is a schematic diagram of the equivalent amplitude and brightness curve of shortwave noise at the first angle provided by the present invention;

[0066] Figure 23 This is a schematic diagram of the equivalent amplitude brightness curve of medium wave noise at the first angle provided by the present invention;

[0067] Figure 24This is a schematic diagram of the equivalent amplitude brightness curve of long-wave noise at the first angle provided by the present invention;

[0068] Figure 25 This is a schematic diagram of the noise equivalent amplitude brightness curve at the second angle provided by the present invention;

[0069] Figure 26 This is a schematic diagram of the noise equivalent amplitude brightness curve at the third angle provided by the present invention;

[0070] Figure 27 This is a schematic diagram of the noise equivalent amplitude brightness curve at the fourth angle provided by the present invention;

[0071] Figure 28 This is a comparison chart of the DN values ​​of a 30℃ blackbody and the equipment noise spectrum;

[0072] Figure 29 This is a comparison chart of the spectral radiance of a 30℃ blackbody and the equipment noise spectrum;

[0073] Figure 30 This is a schematic diagram of the structure of the rotation angle calibration device for the noise equivalent radiance of the infrared spectrometer provided by the present invention. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0075] The terms "first" and "second," etc., used in the specification and claims herein are used to distinguish different objects, not to describe a specific order of objects. For example, "first blackbody temperature point" and "second blackbody temperature point," etc., are used to distinguish different blackbody temperature points, not to describe a specific order of blackbody temperature points.

[0076] In embodiments of the present invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0077] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more, for example, multiple blackbody temperature points means two or more blackbody temperature points, etc.; multiple rotation angles means two or more rotation angles, etc.

[0078] Next, the technical solutions provided in the embodiments of the present invention will be introduced.

[0079] Figure 2 This is a flowchart illustrating the method for calibrating the noise equivalent radiance of an infrared spectrometer by rotation angle provided by the present invention. Figure 2 As shown, the subject executing this method can be an electronic device, such as a server. The method includes steps S101, S102, S103, S104, and S105.

[0080] Step S101: Under the target ambient temperature, the spectra of blackbodies at different temperatures are collected by a rotating interferometric infrared spectrometer to obtain spectral data corresponding to multiple blackbodies at different temperature points.

[0081] Specifically, in order to achieve the noise equivalent radiance of the calibrated rotating interferometric infrared spectrometer, infrared spectral data of blackbodies at different temperatures can be acquired under the same target ambient temperature, with multiple spectral data points collected at each blackbodies temperature point.

[0082] For example, spectral data were collected at four blackbody temperature points: 0℃, 30℃, 50℃, and 80℃, under a target ambient temperature of -1℃. A total of 1024 spectra were collected at each blackbody temperature point. The number of digital signal values ​​(DN) for each spectral sample in the wavelength range of 1.67µm to 13.95µm was N = 1782. This calibration experiment provides important support for subsequent analysis.

[0083] Step S102: Based on the spectral data corresponding to multiple blackbody temperature points, classify the spectral data according to multiple rotation angles of the rotating interferometric infrared spectrometer, and obtain the calibration dataset corresponding to each rotation angle. The calibration dataset includes the spectral data corresponding to multiple blackbody temperature points at the corresponding rotation angle.

[0084] Specifically, a Fourier transform infrared spectrometer generates multiple frames of spectrum with each frame corresponding to an angle.

[0085] For example, a Fourier transform infrared spectrometer generates four frames of spectrum in one rotation. The four angles form a circle. The first frame of spectrum is generated at the first angle, the second frame at the second angle, the third frame at the third angle, and the fourth frame at the fourth angle.

[0086] The spectra generated from the four angles are classified and processed according to file number. The file number of each file is parsed to extract the angle information contained therein. The files are classified according to the first angle, the second angle, the third angle, and the fourth angle. The spectral data within the same angle range are integrated into a single dataset (i.e., the calibration dataset mentioned above). Each dataset includes the spectral data corresponding to multiple blackbody temperature points at the corresponding rotation angle, which facilitates subsequent processing.

[0087] Step S103: Based on the spectral data corresponding to each blackbody temperature point, the noise spectrum digital signal value DN of the rotating interferometric infrared spectrometer is obtained through wavelet transform.

[0088] Specifically, wavelet transform is applied to the acquired spectral data to obtain the high-frequency component, which is the device noise spectrum DN value. To obtain the device noise spectrum, wavelet transform is performed on the acquired blackbody data. Wavelet transform (WT) is a transform analysis method whose main characteristic is its ability to highlight specific features of a problem through transformation, achieving localized analysis of temporal (or spatial) frequencies. This method processes the signal (or function) through progressive multi-scale refinement, using scaling and translation operations, ultimately decomposing it into high-frequency and low-frequency signals. The high-frequency signal reflects the detailed parts of the signal, i.e., the device noise signal, while the low-frequency signal reflects the general outline of the signal.

[0089] Optionally, Dobesi wavelet (DB wavelet function) can be used for wavelet decomposition. The Dobesi wavelet is mainly applied to discrete wavelet transforms, and its classification is based on the value A of the vanishing momentum. As A increases, the smoothness of the adjustment function (low-pass filtering) and the wavelet function (high-pass filtering) also increases. For example, in this process of using the Dobesi wavelet for wavelet decomposition, the present invention selects A=4 and performs wavelet decomposition on the blackbody-calibrated data.

[0090] The results of wavelet decomposition of the calibration curve with a target ambient temperature of -1℃ and a blackbody temperature of 30℃ are as follows: Figure 3 As shown, Figure 3 This is a schematic diagram of the device noise signal after wavelet decomposition provided by the present invention.

[0091] Understandably, electromagnetic bands from near-infrared to long-wave exhibit different characteristics in terrestrial scenarios, thus necessitating band-specific calibration. This is because the signal sources and characteristics differ across bands, making accurate measurement and interpretation of these signals crucial.

[0092] In the mid-wave and long-wave ranges, the signal strength of ground scenes is relatively high, mainly due to the influence of the ground's own radiation. This means that the signals received by equipment in these bands mainly come from the radiation emitted by ground objects themselves, thus requiring corresponding mid-wave and long-wave band calibration to accurately reflect the radiation characteristics of the ground scene.

[0093] In contrast, in the near-infrared and shortwave ranges, signals from ground-based scenes are primarily affected by the reflection of solar radiation. Therefore, in these bands, the calibration process needs to pay closer attention to the signal reflection characteristics to correctly interpret the response of ground objects to solar radiation.

[0094] By performing band-specific calibration, this invention can more accurately understand the radiation characteristics of ground scenes in different bands, which helps to avoid errors caused by differences between bands and improves the accuracy and reliability of remote sensing equipment in ground observation.

[0095] The target ambient temperature is -1℃, and the blackbody temperature is 30℃. After wavelet decomposition of the calibration curve, the results after band division are as follows: Figure 4-7 As shown, Figure 4 This is a schematic diagram of the near-infrared band noise curve after wavelet decomposition provided by the present invention. Figure 5 This is a schematic diagram of the shortwave band noise curve after wavelet decomposition provided by the present invention. Figure 6 This is a schematic diagram of the mid-wave band noise curve after wavelet decomposition provided by the present invention. Figure 7 This is a schematic diagram of the long-wave band noise curve after wavelet decomposition provided by the present invention.

[0096] Step S104: Based on the calibration dataset corresponding to each rotation angle, calculate the system response gain corresponding to one or more first blackbody temperature points at each rotation angle.

[0097] The first blackbody temperature point is one of multiple blackbody temperature points, for example, the first blackbody temperature point is the 30℃ blackbody temperature point.

[0098] Specifically, based on the calibration dataset corresponding to the target rotation angle, the spectral data corresponding to the second blackbody temperature point and the third blackbody temperature point are extracted at the target rotation angle. The target rotation angle is any one of multiple rotation angles. The second blackbody temperature point is lower than the first blackbody temperature point, and the third blackbody temperature point is higher than the first blackbody temperature point.

[0099] Based on the spectral data corresponding to the second and third blackbody temperature points at the target rotation angle, the system response gain corresponding to the first blackbody temperature point at the target rotation angle is determined through two-point linear calibration.

[0100] The second and third blackbody temperature points are two of the aforementioned blackbody temperature points. For example, if the first blackbody temperature point is 30 degrees Celsius, the second blackbody temperature point is 0 degrees Celsius, and the third blackbody temperature point is 50 degrees Celsius.

[0101] Optionally, after extracting the spectral data corresponding to the second blackbody temperature point and the third blackbody temperature point at the target rotation angle from the calibration dataset based on the target rotation angle, the method further includes:

[0102] Based on the spectral data corresponding to the second blackbody temperature point and the third blackbody temperature point under the target rotation angle, the radiation bias corresponding to the first blackbody temperature point under the target rotation angle is determined by two-point linear calibration.

[0103] Based on the radiation bias corresponding to the first blackbody temperature point under the target rotation angle, Gaussian filtering is performed to obtain the filtered radiation bias corresponding to the first blackbody temperature point under the target rotation angle.

[0104] Radiation bias can be used to calibrate the actual measured DN value of a blackbody to obtain the corresponding blackbody radiance.

[0105] For example, using blackbody spectral data, the system response gain and radiation bias at different blackbody temperatures under the same ambient temperature are calculated based on the rotation angle.

[0106] The two-point linear calibration method is based on the premise that the system radiation has a linear response within the dynamic range. It calibrates the output response according to a uniformly calibrated output response and obtains the calibration coefficients. The two-point calibration method is shown in formula (1) below:

[0107] DN(λ)=Gain(λ)·S input (λ)+offset(λ) (1);

[0108] In the formula, DN(λ) is the system calibration dataset, and S input (λ) represents the system calibration test input corresponding to the calibration dataset, Gain(λ) is the system gain response function, offset(λ) is the system radiation bias, and λ is the wavelength in μm.

[0109] From equation (1) above, it can be seen that the gain response function Gain(λ) and the radiation offset (λ) of the remote sensing system need to be calculated under at least two input conditions: high temperature blackbody measurement (corresponding to the measurement of the third blackbody temperature point mentioned above) and low temperature blackbody measurement (corresponding to the measurement of the second blackbody temperature point mentioned above).

[0110] DN H (λ)=Gain(λ)·S H (λ)+offset(λ) (2);

[0111] DN L (λ)=Gain(λ)·S L (λ)+offset(λ) (3);

[0112] Where Gain(λ) represents the system response gain, offset(λ) represents the radiation bias, and DN L (λ) represents the spectral data corresponding to the second blackbody temperature point, S L (λ) represents the preset theoretical blackbody radiance corresponding to the second blackbody temperature point, DN H (λ) represents the spectral data corresponding to the third blackbody temperature point, S H(λ) represents the theoretical radiance of the blackbody corresponding to the third blackbody temperature point, and λ represents the wavelength.

[0113] Combining equations (2) and (3), we can obtain Gain(λ) and offset(λ):

[0114]

[0115]

[0116] Using a blackbody at -1℃ and a blackbody at 50℃, the two-point calibration method based on the rotation angle is used to calculate the gain and bias of the equipment by substituting into equations (4) and (5). The calculated gain curve based on the rotation angle is then fitted, and the calculated gain curve is processed by band.

[0117] In the testing environment, the radiation behavior of various gas molecules and incomplete vacuuming of the spectral correlation equipment can both affect the experiment. Gas molecules (atmosphere, carbon dioxide molecules) emit infrared radiation during the test, generating background signals in the spectral data, especially in infrared spectroscopy experiments, which can lead to signal aliasing and interference. The radiation characteristics of different gas molecules also affect the shape and intensity of the spectrum.

[0118] On the other hand, incomplete vacuuming of the spectral correlation equipment may result in residual gases in the testing environment, whose molecules also participate in infrared radiation. The presence of these residual gases may interfere with the accurate measurement of the object under test. In addition, incomplete vacuuming may also introduce different temperature and pressure conditions, negatively impacting the repeatability and accuracy of the experimental results.

[0119] To reduce these effects, a Gaussian filter is applied to the calculated bias curve. This effectively removes the radiation from various gas molecules in the test environment and the effects of incomplete vacuuming of the spectral correlation equipment, thereby improving the accuracy and reliability of the experimental results.

[0120] Optionally, the spectral data corresponding to each blackbody temperature point covers the following bands: near-infrared band, short-wave band, mid-wave band, and long-wave band. Correspondingly, the system response gain corresponding to the first blackbody temperature point includes: system response gain of the near-infrared band, system response gain of the short-wave band, system response gain of the mid-wave band, and system response gain of the long-wave band.

[0121] like Figure 8-13 As shown, Figure 8 This is a schematic diagram of the system gain curves calculated from the blackbody temperature of 0°C and 50°C at the first angle provided by the present invention. Figure 9 This is one of the first schematic diagrams of angle gain band sub-band curves provided by this invention. Figure 10This is the second schematic diagram of the first angle gain band sub-curve provided by the present invention. Figure 11 This is the third schematic diagram of the first angle gain band sub-curve provided by this invention. Figure 12 This is the fourth schematic diagram of the first angle gain band sub-curve provided by this invention. Figure 13 This is a schematic diagram of the system radiation bias obtained by calculating the blackbody temperature at 0°C and 50°C at the first angle provided by the present invention.

[0122] like Figure 14 and 15 As shown, Figure 14 This is a schematic diagram of the system gain curves calculated from the blackbody temperature of 0℃ and 50℃ at the second angle provided by the present invention. Figure 15 This is a schematic diagram of the system radiation bias calculated from the blackbody temperature of 0℃ and 50℃ at the second angle provided by the present invention.

[0123] like Figure 16 and 17 As shown, Figure 16 This is a schematic diagram of the system gain curves calculated from the blackbody temperature of 0℃ and 50℃ at the third angle provided by the present invention. Figure 17 This is a schematic diagram of the system radiation bias calculated from the blackbody temperature of 0℃ and 50℃ at the third angle provided by the present invention.

[0124] like Figure 18 and 19 As shown, Figure 18 This is a schematic diagram of the system gain curves calculated from the blackbody temperature of 0℃ and 50℃ at the fourth angle provided by the present invention. Figure 19 This is a schematic diagram of the system radiation bias calculated from the blackbody temperature of 0℃ and 50℃ at the fourth angle provided by the present invention.

[0125] Step S105: For each rotation angle, based on the noise spectrum DN value and the system response gain corresponding to each first blackbody temperature point, calibrate the noise equivalent radiance corresponding to each first blackbody temperature point at each rotation angle.

[0126] Specifically, the noise equivalent radiance of the equipment noise is obtained by inverting the noise spectrum of the corresponding type using the gain and bias calculated by the blackbody at the corresponding angle and temperature range.

[0127] For example, the gain and bias obtained by solving for the four angles of the 0°C blackbody and the 50°C blackbody are used to invert the blackbody noise spectrum at four different angles of 30°C.

[0128] The traditional formula for calculating noise equivalent radiance is as follows:

[0129]

[0130] Among them, DNnoise (λ) represents the digital signal value at the noise wavelength λ, and k(λ) and b(λ) represent the system response gain and system radiation bias at wavelength λ, respectively; S input_noise (λ) represents the equivalent image radiance of noise with wavelength λ.

[0131] Because this invention uses wavelet decomposition to obtain the noise spectrum of the device, the high-frequency component has been successfully debiased. Therefore, the noise radiance obtained by this invention is unaffected by the bias. This means that in noise analysis, this invention can more accurately obtain the true noise characteristics generated by the device without being interfered with by the bias. This is an important technological innovation for the accurate measurement and analysis of device noise, providing a more reliable data foundation for research and applications in related fields.

[0132] Furthermore, by successfully removing the bias in the high-frequency range, the noise analysis of this invention exhibits higher sensitivity and resolution. This enables the invention to capture more subtle changes and features in the noise spectrum, resulting in more accurate measurements of noise equivalent radiance compared to traditional methods, and providing more reliable data support for engineering and scientific fields.

[0133] This technological innovation is of great significance for noise control and equipment optimization. By accurately acquiring information on the frequency distribution and intensity of noise, engineers and researchers can take targeted measures to improve equipment performance, reduce noise levels, and thus increase equipment reliability and lifespan.

[0134] Therefore, the formula for calculating the noise equivalent radiance of this invention is as follows:

[0135]

[0136] Among them, DN noise (λ) represents the digital signal value (DN value) at the noise wavelength λ, and k(λ) represents the system response gain at the wavelength λ; S input_noise (λ) represents the equivalent image radiance of noise with wavelength λ.

[0137] Optionally, the spectral data corresponding to each blackbody temperature point covers the following bands: near-infrared band, short-wave band, mid-wave band, and long-wave band. Correspondingly, the noise equivalent radiance corresponding to the first blackbody temperature point includes: noise equivalent radiance of the near-infrared band, noise equivalent radiance of the short-wave band, noise equivalent radiance of the mid-wave band, and noise equivalent radiance of the long-wave band.

[0138] like Figure 20-24 As shown, Figure 20 This is a schematic diagram of the noise equivalent amplitude brightness curve at the first angle provided by the present invention. Figure 21This is a schematic diagram of the equivalent amplitude brightness curve of near-infrared noise at the first angle provided by the present invention. Figure 22 This is a schematic diagram of the equivalent amplitude and brightness curve of shortwave noise at the first angle provided by the present invention. Figure 23 This is a schematic diagram of the equivalent amplitude and brightness curve of medium-wave noise at the first angle provided by the present invention. Figure 24 This is a schematic diagram of the equivalent amplitude brightness curve of long-wave noise at the first angle provided by the present invention.

[0139] like Figure 25-27 As shown, Figure 25 This is a schematic diagram of the noise equivalent amplitude brightness curve at the second angle provided by the present invention. Figure 26 This is a schematic diagram of the noise equivalent amplitude brightness curve at the third angle provided by the present invention. Figure 27 This is a schematic diagram of the noise equivalent amplitude brightness curve at the fourth angle provided by the present invention.

[0140] Figure 28 This is a comparison of the DN values ​​of a 30℃ blackbody and the equipment noise spectrum. Figure 29 It is a comparison of the radiance of a 30℃ blackbody and the noise spectrum of the equipment.

[0141] By acquiring standard spectral data at each rotation angle of the Fourier transform infrared spectrometer and classifying the spectral data according to the rotation angle, calibration datasets corresponding to each rotation angle can be obtained. Then, (using a two-point linear calibration method) the system response gain can be calculated for each rotation angle to determine the system response gain corresponding to one or more blackbody temperature points at each rotation angle. Furthermore, by combining the noise spectrum DN value obtained by wavelet transform, the noise equivalent radiance corresponding to the blackbody temperature point can be calibrated for each rotation angle. This not only considers the overall linear relationship of the spectrometer but also makes full use of the similarity at different angles. This allows for the selection of the appropriate calibration curve based on the current measurement angle in actual measurements, thereby more accurately correcting the measurement data.

[0142] The device noise calibration method provided by this invention not only improves measurement accuracy but also reduces the impact of systematic errors by considering spectral similarity. This provides a more reliable calibration scheme for Fourier transform infrared spectrometers at various rotation angles, thus enabling them to play a greater role in fields such as target recognition.

[0143] The rotation angle calibration device provided by the present invention is described below. The rotation angle calibration device described below and the rotation angle calibration method described above can be referred to in correspondence.

[0144] Figure 30 This is a schematic diagram of the structure of the rotation angle calibration device for the noise equivalent radiance of the infrared spectrometer provided by the present invention, as shown below. Figure 30As shown, the device includes: an acquisition module 10, a calibration dataset acquisition module 20, a wavelet transform module 30, a system response gain calculation module 40, and a calibration module 50. Wherein:

[0145] Acquisition module 10 is used to acquire the spectrum of a blackbody at different temperatures using a rotating interferometric infrared spectrometer under the target ambient temperature, and obtain spectral data corresponding to multiple blackbody temperature points respectively.

[0146] The calibration dataset acquisition module 20 is used to classify the spectral data according to the multiple rotation angles of the rotating interferometric infrared spectrometer based on the spectral data corresponding to multiple blackbody temperature points, and to acquire the calibration dataset corresponding to each rotation angle. The calibration dataset includes the spectral data corresponding to multiple blackbody temperature points at the corresponding rotation angle.

[0147] Wavelet transform module 30 is used to obtain the noise spectrum digital signal value DN of the rotating interferometric infrared spectrometer by wavelet transform based on the spectral data corresponding to each blackbody temperature point.

[0148] The system response gain calculation module 40 is used to calculate the system response gain corresponding to one or more first blackbody temperature points at each rotation angle based on the calibration dataset corresponding to each rotation angle.

[0149] The calibration module 50 is used to calibrate the noise equivalent radiance of each first blackbody temperature point at each rotation angle based on the noise spectrum DN value and the system response gain corresponding to each first blackbody temperature point.

[0150] It should be understood that the above-described device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.

[0151] Based on the methods described in the above embodiments, this invention provides an electronic device. The device may include at least one memory for storing a program and at least one processor for executing the program stored in the memory. When the program stored in the memory is executed, the processor performs the methods described in the above embodiments.

[0152] Based on the methods in the above embodiments, this embodiment of the invention provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0153] Based on the methods in the above embodiments, this embodiment of the invention provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0154] It is understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0155] The method steps in these embodiments of the invention can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0156] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0157] It is understood that the various numerical designations used in the embodiments of the present invention are merely for the convenience of description and are not intended to limit the scope of the embodiments of the present invention.

[0158] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for calibrating the rotation angle of the noise equivalent radiance of an infrared spectrometer, characterized in that, include: At the target ambient temperature, the spectra of blackbodies at different temperatures are collected using a rotating interferometric infrared spectrometer to obtain spectral data corresponding to multiple blackbodies at different temperature points. Based on the spectral data corresponding to multiple blackbody temperature points, the spectral data are classified according to multiple rotation angles of the rotating interferometric infrared spectrometer to obtain calibration datasets corresponding to each rotation angle. The calibration datasets include the spectral data corresponding to multiple blackbody temperature points at the corresponding rotation angles. Based on the spectral data corresponding to each blackbody temperature point, the noise spectrum digital signal value DN of the rotating interferometric infrared spectrometer is obtained by wavelet transform. Based on the calibration dataset corresponding to each rotation angle, calculate the system response gain corresponding to one or more first blackbody temperature points under each rotation angle; For each rotation angle, based on the noise spectrum DN value and the system response gain corresponding to each of the first blackbody temperature points, the noise equivalent radiance corresponding to each of the first blackbody temperature points is calibrated at each rotation angle.

2. The method for calibrating the rotation angle of the noise equivalent radiance of an infrared spectrometer according to claim 1, characterized in that, The step of calculating the system response gain corresponding to one or more first blackbody temperature points at each rotation angle based on the calibration dataset corresponding to each rotation angle includes: Based on the calibration dataset corresponding to the target rotation angle, extract the spectral data corresponding to the second blackbody temperature point and the third blackbody temperature point at the target rotation angle. The target rotation angle is any one of the multiple rotation angles. The second blackbody temperature point is lower than the first blackbody temperature point, and the third blackbody temperature point is higher than the first blackbody temperature point. Based on the spectral data corresponding to the second blackbody temperature point and the third blackbody temperature point at the target rotation angle, the system response gain corresponding to the first blackbody temperature point at the target rotation angle is determined by two-point linear calibration.

3. The method for calibrating the rotation angle of the equivalent radiance of an infrared spectrometer noise according to claim 2, characterized in that, The system response gain corresponding to the first blackbody temperature point at the target rotation angle is determined by two-point linear calibration based on the spectral data corresponding to the second and third blackbody temperature points at the target rotation angle. Specifically, this includes calculating the system response gain using the following formula: Where Gain(λ) represents the system response gain, DN L (λ) represents the spectral data corresponding to the second blackbody temperature point, S L (λ) represents the preset theoretical blackbody radiance corresponding to the second blackbody temperature point, DN H (λ) represents the spectral data corresponding to the third blackbody temperature point, S H (λ) represents the theoretical radiance of the blackbody corresponding to the third blackbody temperature point, and λ represents the wavelength.

4. The method for calibrating the rotation angle of the equivalent radiance of an infrared spectrometer noise according to claim 2, characterized in that, After extracting the spectral data corresponding to the second blackbody temperature point and the third blackbody temperature point at the target rotation angle from the calibration dataset based on the target rotation angle, the method further includes: Based on the spectral data corresponding to the second blackbody temperature point and the third blackbody temperature point at the target rotation angle, the radiation bias corresponding to the first blackbody temperature point at the target rotation angle is determined by two-point linear calibration. Based on the radiation bias corresponding to the first blackbody temperature point under the target rotation angle, Gaussian filtering is performed to obtain the filtered radiation bias corresponding to the first blackbody temperature point under the target rotation angle.

5. The method for calibrating the rotation angle of the noise equivalent radiance of an infrared spectrometer according to claim 4, characterized in that, The radiation bias corresponding to the first blackbody temperature point at the target rotation angle is determined by two-point linear calibration based on the spectral data corresponding to the second and third blackbody temperature points at the target rotation angle. Specifically, the radiation bias is calculated using the following formula: Where offset(λ) represents the radiation bias, DN L (λ) represents the spectral data corresponding to the second blackbody temperature point, S L (λ) represents the preset theoretical blackbody radiance corresponding to the second blackbody temperature point, DN H (λ) represents the spectral data corresponding to the third blackbody temperature point, S H (λ) represents the theoretical radiance of the blackbody corresponding to the third blackbody temperature point, and λ represents the wavelength.

6. The method for calibrating the rotation angle of the noise equivalent radiance of an infrared spectrometer according to claim 1, characterized in that, For each of the rotation angles, based on the noise spectrum DN value and the system response gain corresponding to each of the first blackbody temperature points, the noise equivalent radiance corresponding to each of the rotation angles is calibrated. Specifically, this includes calibrating the noise equivalent radiance using the following formula: Among them, S input_noise (λ) represents the noise equivalent radiance, DN noise (λ) represents the DN value at the noise wavelength λ, and k(λ) represents the system response gain at the wavelength λ.

7. The method for calibrating the rotation angle of the noise equivalent radiance of an infrared spectrometer according to any one of claims 1-6, characterized in that, The spectral data corresponding to each blackbody temperature point covers the following bands: near-infrared band, short-wave band, mid-wave band, and long-wave band. Correspondingly, the noise equivalent radiance corresponding to the first blackbody temperature point includes the noise equivalent radiance of the near-infrared band, the noise equivalent radiance of the short-wave band, the noise equivalent radiance of the mid-wave band, and the noise equivalent radiance of the long-wave band.

8. A rotation angle calibration device for the noise equivalent radiance of an infrared spectrometer, characterized in that, include: The acquisition module is used to acquire the spectrum of a blackbody at different temperatures using a rotating interferometric infrared spectrometer under the target ambient temperature, and obtain the spectral data corresponding to multiple blackbody temperature points respectively; The calibration dataset acquisition module is used to classify the spectral data according to the multiple rotation angles of the rotating interferometric infrared spectrometer based on the spectral data corresponding to multiple blackbody temperature points, and to acquire the calibration dataset corresponding to each rotation angle. The calibration dataset includes the spectral data corresponding to multiple blackbody temperature points at the corresponding rotation angle. The wavelet transform module is used to obtain the noise spectrum digital signal value DN of the rotating interferometric infrared spectrometer based on the spectral data corresponding to each blackbody temperature point through wavelet transform. The system response gain calculation module is used to calculate the system response gain corresponding to one or more first blackbody temperature points at each rotation angle based on the calibration dataset corresponding to each rotation angle. The calibration module is used to calibrate the noise equivalent radiance corresponding to each of the first blackbody temperature points at each of the rotation angles, based on the noise spectrum DN value and the system response gain corresponding to each of the first blackbody temperature points.

9. An electronic device, characterized in that, include: At least one memory for storing programs; At least one processor is configured to execute a program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to perform the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing a computer program, characterized in that, When the computer program is run on the processor, it causes the processor to perform the method as described in any one of claims 1-7.

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