Method for measuring radiation intensity of infrared radiation simulation source system

Abstract

The invention discloses a radiation intensity measurement method of an infrared radiation simulation source system, and belongs to the technical field of infrared radiation characteristic measurement. The method comprises the following steps: firstly, performing radiometric calibration operation on an infrared radiation simulation source system and an infrared detector; a medium-wave infrared optical system and an infrared detector are used for receiving infrared radiation signals, and continuous radiation is converted into alternating signals through modulation of an optical chopper, so that the measurement signal-to-noise ratio is increased. Collected alternating signals are processed by the filtering gain amplification circuit so as to enhance signal stability. And then dynamic denoising and state estimation are carried out on measured data by adopting a Kalman filtering algorithm, and nonlinear error compensation is carried out in combination with a medium-wave infrared radiation coefficient correction algorithm, so that a high-precision radiation intensity correction coefficient is obtained. Compared with a traditional method, the self-adaptive filtering and radiation intensity correction technology is combined, the measurement precision and the real-time performance are improved, and the method can be widely applied to the fields of infrared optical testing, target characteristic analysis and radiation measurement.

Description

Technical Field

[0001] The present invention belongs to the technical field of infrared radiation characteristic measurement, and particularly relates to a method for measuring the radiation intensity of an infrared radiation simulation source system. Background Art

[0002] With the rapid development of infrared physics and related technologies, the application fields of infrared radiation measurement technology are expanding day by day. In the civilian field, infrared technology has been widely applied in many industries such as industry, agriculture, medicine, and transportation. By measuring the infrared radiation characteristics of different targets and backgrounds, we can not only predict the feasibility of various technical application solutions, but also provide powerful auxiliary and monitoring means for safety production and product quality at all stages of product development, production, and detection. In the military field, the measurement of the infrared radiation intensity of targets has become the key to target tracking and recognition. Compared with traditional image feature recognition technologies, infrared radiation characteristic measurement technology has the significant advantages of passive detection and strong anti-interference ability. Combining computer technology, we can establish a radiation characteristic database of typical targets and their characteristic parts, providing a more reliable and effective reference basis for fields such as missile air defense warning and weapon stealth effect evaluation. And the infrared radiation intensity, as the core index characterizing the infrared radiation characteristics of targets, its accurate measurement is of great significance for improving the application level of the entire infrared technology.

[0003] The HgCdTe infrared detector mentioned in the present invention is an infrared mercury cadmium telluride detector (HgCdTe, MCT), which is an infrared detector based on semiconductor materials and mainly composed of mercury cadmium telluride alloy. Its composition usually includes an active detection layer, electrodes, packaging, and a cooling system (such as a liquid nitrogen cooler). The characteristics of MCT detectors lie in their wide wavelength response range (about 1 µm to 20 µm), high sensitivity, and low noise performance. By adjusting the ratio of Hg to Cd, the wavelength of the detector response can be optimized. In addition, MCT detectors operate at low temperatures, which can significantly improve the detection accuracy. The application fields include thermal imaging, night vision equipment, gas detection, remote sensing, scientific research, military reconnaissance, and security technology, etc. Its excellent sensitivity and response ability play an important role in various infrared detection requirements. With the development of technology, the integration and miniaturization of MCT detectors have increased their application in portable devices.

[0004] The infrared radiation source to be measured specifically refers to a device that emits infrared energy by heating a radiator, and specifically includes a heating body, an optical system composed of a parabolic mirror and a filter set, a control system composed of a voice coil motor, a shutter, etc., a power supply system, and a support structure member, etc. It can simulate the infrared radiation characteristics of various military targets according to the scene requirements or experimental requirements.

[0005] Infrared radiation intensity, as an indirect physical quantity, cannot be directly obtained by instruments. However, it is not only an important physical quantity for describing the infrared radiation characteristics of targets but also an important core index parameter for various infrared radiation simulation sources. Therefore, it is of certain significance to accurately measure this parameter by using various existing advanced instruments and combining solid theoretical derivations. At present, relevant domestic measurement technologies and means are few, lack practicality and accuracy, and cannot fully meet the factory acceptance requirements of some infrared radiation simulation source devices, and it is difficult to know the usage status and requirements of related products. Summary of the Invention

[0006] The present invention proposes a measurement method combining the Kalman filtering algorithm and the mid-wave infrared radiation coefficient correction algorithm, which can accurately construct the non-linear mapping relationship between the detected voltage and the infrared radiation intensity, so as to realize the high-precision inversion and evaluation of the radiation intensity of the infrared radiation source. This method first heats the infrared radiation source to be measured to a stable working state, focuses its radiation energy through a mid-wave infrared optical system and irradiates it onto the photosensitive surface of the calibrated infrared detector; the detector receives the radiation energy and converts it into a corresponding electrical signal. Subsequently, the collected electrical signal is preliminarily processed by a filter gain amplification circuit to improve the signal-to-noise ratio and suppress various types of noise interference. On this basis, the Kalman filtering algorithm is used to dynamically estimate and filter-correct the signal sequence, and combined with the mid-wave infrared radiation coefficient correction and the atmospheric transmittance correction, a non-linear response model between the output voltage of the detector and the actual radiation intensity is established. Finally, the calculation module integrated in the upper computer software calculates the processed signal and outputs the radiation intensity value of the infrared radiation source to be measured within the specified wavelength band.

[0007] The measurement method provided by the present invention not only realizes the accurate acquisition of infrared radiation intensity in the mid-wave infrared band, has good adaptability and stability, but also can be used for the factory performance verification, working state evaluation and parameter calibration of infrared radiation simulation source products, and has wide engineering application value.

[0008] A method for measuring the radiation intensity of an infrared radiation simulation source system of the present invention includes the following steps: Step 1: Radiation calibration: First, use a standard surface source blackbody to calibrate the infrared detector in the full wavelength band to obtain the radiation intensity response relationship between the surface source blackbody and the infrared detector; then perform secondary calibration in two mid-wave infrared bands of the infrared radiation simulation source to be measured to establish the response relationship between the output signal of the infrared detector and the infrared radiation intensity in the dual wavelength bands; Step 2: Photoelectric conversion: Preheat the infrared radiation simulation source to be measured to a stable working state, focus it through a mid-wave infrared optical system, and then the calibrated infrared detector receives the radiation energy and converts it into a corresponding electrical signal; Step 3: Signal preprocessing: Filter, amplify with gain, and modulate the electrical signals obtained in Step 2 to obtain signals with high signal-to-noise ratio. Step 4: Algorithm correction: Use the Kalman filtering algorithm to optimize the dynamic signals of the high signal-to-noise ratio signals obtained in Step 3, and combine with the mid-wave infrared radiation coefficient correction algorithm to compensate for the nonlinear error and establish the mapping relationship between the detection voltage and the radiation intensity. Step 5: Data output: Calculate the infrared radiation intensity parameters obtained through the mapping relationship between the detection voltage and the radiation intensity in Step 4 and upload them to the upper computer software of the measurement system to display and store the measurement results in real time.

[0009] Further, the infrared detector uses a HgCdTe infrared detector; the mid-wave infrared optical system adopts a Cook triplet structure design, including two double convex lenses on both sides and a double concave lens between the two double convex lenses; among them, the effective focal lengths of the two double convex lenses are both 50 mm; the focal length of the middle double concave lens is -30 mm; the three lenses together form a compound lens system with a reduced focal length of about 71 mm, and the total system length is about 120 mm. The infrared radiation simulation source to be measured operates in two mid-wave infrared bands of 3.5 μm - 4 μm and 4.5 μm - 4.8 μm. During the radiation calibration process, mid-wave and far-infrared band-pass filters matching the bands are selected respectively, and their transmission ranges are 3550 - 4150 nm and 4500 - 5000 nm.

[0010] Further, the specific method of Step 1 is as follows: First, use a standard surface source blackbody to calibrate the infrared detector over the full band to obtain the radiation intensity response relationship between the blackbody and the infrared detector. The specific method is as follows: Taking the standard surface source blackbody as the reference, first use the near-field extended source method to calibrate over the full band. During calibration, place the surface source blackbody in front of the mid-wave infrared optical system, and set an optical chopper between the mid-wave infrared optical system and the infrared detector to make the infrared radiation from the surface source blackbody focus on the photosensitive surface of the infrared detector. By controlling the light source temperature of the surface source blackbody, establish the radiation intensity response relationship between the surface source blackbody with temperature and the infrared detector as follows: , where is an infinitesimal change in wavelength, is an infinitesimal area element on the detector, is the infrared radiation intensity of the surface source blackbody, is the working temperature of the surface source blackbody, is the response rate of the infrared detector to the incident radiation intensity, is the response range of the infrared detector. is the spectral response of the infrared detector, is the transmittance of the mid-wave infrared optical system, is the gain of the circuit, is the temperature at when the radiant emittance of the area source blackbody is is the area of the surface element on the detection surface; Collect the voltages output by the area source blackbody at different operating temperatures, and calculate the corresponding radiant intensity through the above formula to calculate ; Subsequently, perform secondary calibration under two mid-wave infrared bands of the infrared radiation simulation source to be measured, and establish the response relationship between the output signal of the infrared detector and the infrared radiation intensity under the two bands. The specific method is as follows: Add a mid-wave infrared band-pass filter with a transmission range of 3550–4150 nm and 4500–5000 nm in front of the infrared detector. By switching between two different mid-wave infrared band-pass filters, the infrared radiation simulation source to be measured can work in two mid-wave infrared bands of 3.5 μm to 4 μm and 4.5 μm to 4.8 μm; Establish the radiant intensity response relationship between the area source blackbody and the infrared detector at a temperature of within the measurement bands, that is, in the bands of 3.5 μm to 4 μm and 4.5 μm to 4.8 μm, specifically as follows: , , and represent the infrared radiant intensity of the area source blackbody under the bands of 3.5 μm to 4 μm and 4.5 μm to 4.8 μm respectively, and are the spectral transmittances of the two bands respectively, and are the responsivities of the detector to the target radiant intensity at the two bands respectively.

[0011] Furthermore, the signal processing module used in the signal preprocessing in step 3 includes a two-stage circuit structure. The first stage is a transimpedance amplifier circuit, that is, a TIA, which is used to convert the weak current signal output by the infrared detector into a voltage signal and achieve preliminary gain amplification; the second stage is a filter gain amplification circuit, including a pre-filter and an amplifier circuit, which is used to analyze the spectral characteristics of the signal output by the TIA, suppress high and low frequency interference signals including thermal noise, white noise, G-R noise, and 1 / f noise, optimize the signal quality, input the alternating voltage signal into the analog-to-digital converter A / D, and the STM32 controller collects the peak-to-peak voltage and converts it into a digital signal for subsequent processing.

[0012] Further, in step 4, the Kalman filter algorithm is used to optimize the high signal-to-noise ratio signal obtained in step 3. The specific method is as follows: Assume that the measured voltage and radiation intensity satisfy the function , and the circuit noise satisfies the function , the state variable is the infrared radiation intensity, and the state variable is the noise voltage. The observed variable is the output voltage . Then there is a state-observation model for Kalman filter estimation: , where represents the prior estimate at the th moment, represents the prior estimate at the th moment, represents the state transition matrix; then for the calculation of the prior estimate covariance, first set , , where represents the prior state covariance matrix at the th moment, reflecting the uncertainty of the system state estimate before this observation; represents the prior state covariance matrix at the th moment, reflecting the estimated error distribution after correction by the previous observation, respectively represent the variance estimates of the infrared radiation intensity and noise voltage at the current moment, respectively represent the covariance between the two state variables, and satisfy ; respectively represent the variance estimates of the infrared radiation intensity and noise voltage at the previous moment, respectively represent the covariance between the two state variables; Construct the covariance matrix of the two state variables. Since the two state variables are independent of each other, there is: , where represents the system process noise covariance matrix, used to quantify the uncertainty in the state transition process; Thus, the covariance formula for the prior estimate is obtained as: , where represents the process noise variance of the radiation intensity, represents the process noise variance of the circuit noise; Expand the above formula to obtain the specific expression of the covariance matrix: , Since the set state quantity contains voltage measurement noise, the system measurement equation is established as , and the measurement matrix . From this, the Kalman gain is obtained: , where represents the Kalman gain of the radiation intensity state and is used to adjust the predicted radiation intensity; represents the Kalman gain of the circuit noise state and is used to estimate and suppress the error caused by the measurement noise, represents the observation value at time , that is, the actual measured voltage, represents the response coefficient between the output voltage of the infrared detector and the radiation intensity, is the radiation noise; , where represents the prior estimate value of the radiation intensity at the current time, that is, the predicted value, represents the gain term corresponding to the radiation intensity state in the Kalman gain, that is ; Finally, update the covariance matrix: , represents the variance of the radiation intensity with itself in the updated covariance matrix; represents the covariance between the radiation intensity and the circuit noise after update; represents the covariance between the circuit noise and the radiation intensity after update; represents the variance of the circuit noise with itself in the updated covariance matrix; After the above process, one filtering cycle is completed.

[0013] Furthermore, in step 4, the mid-wave infrared radiation coefficient correction algorithm is combined to compensate for the nonlinear error and establish the mapping relationship between the detected voltage and the radiation intensity. The specific method is as follows: When the measurement system estimates the target radiation intensity in each specific measurement band, the detector spectral response is regarded as a constant. Based on this, the radiation intensity correction coefficient is defined, and is regarded as a constant for calculation, so as to correct the signal measured by the system and improve the accuracy of radiation intensity inversion; The radiation intensity correction coefficient is: , Therefore, the infrared radiation intensity after the correction of the medium-wave infrared radiation coefficient and The calculation formula is: , , For solving, a linear calibration model is adopted. The spectral responsivity of the detector is not considered for the time being, and only the spectral transmittance of the filter and the gain of the circuit are considered , and the target radiation intensity is calculated; Therefore, the final calibration model is: , where is the spectral transmittance of the specified band, is the response of the dual-band measurement system to the target radiation intensity considering only the spectral transmittance of the filter. Here, the infrared radiation intensity is theoretically calculated at different temperatures, and the voltage output by the detector is obtained. The two are fitted to obtain , through the following formula: , Furthermore, the equivalent radiation intensity of the blackbody radiation intensity after adding the filter after the correction of the radiation intensity correction coefficient is obtained : , Substituting it in, we get: , Through the calculation formula of the equivalent radiation intensity after the above correction , it is further used to verify the accuracy of the radiation intensity calibration curve of the target radiation source in each measurement band.

[0014] Beneficial effects

[0015] By constructing a non-linear mapping relationship between the infrared radiation intensity and the output voltage of the detector, combining the Kalman filtering algorithm and the medium-wave infrared radiation coefficient correction algorithm, the present invention realizes the dynamic denoising and adaptive correction of the infrared radiation signal, greatly improves the measurement accuracy and anti-interference ability, and ensures the accuracy and stability of the output result.

[0016] The system integrates a switchable medium-wave infrared band-pass filter and a dedicated optical design (Cook triplet structure), has the ability of efficient selective transmission in the bands of 3.5μm - 4μm and 4.5μm - 4.8μm, and realizes the high-precision measurement and calibration of the infrared radiation intensity within the specified band through the introduction of the dual-band correction factor, meeting the detection requirements of infrared radiation sources in complex scenarios.

[0017] The infrared radiation intensity measurement system constructed by the present invention integrates a high-gain and low-noise signal acquisition circuit, a filtering and processing module, and an intelligent upper computer platform, realizing the full-process automation of radiation source control, signal acquisition and processing, radiation intensity calculation, and visualization display. It has the advantages of high measurement efficiency, convenient operation, and strong repeatability, and can be widely used for the factory inspection, performance evaluation, and in-situ monitoring of infrared simulation source devices. Description of the Drawings

[0018] Figure 1 is the radiation calibration flow chart of the infrared detector of the present invention; Figure 2 is the working flow chart of the infrared radiation intensity measurement method of the present invention; Figure 3 is the beam envelope diagram during the simulation process of the mid-wave infrared optical system of the present invention; Figure 4 is the spot diagram during the simulation process of the mid-wave infrared optical system of the present invention; Figure 5 is the field of view analysis during the simulation process of the mid-wave infrared optical system of the present invention; Figure 6 is the flow block diagram of the filtering gain amplification circuit of the present invention; Figure 7 is the schematic diagram of the Kalman filtering algorithm used in the present invention. Detailed Embodiments

[0019] The technical solutions of the present invention will be clearly and comprehensively described below with reference to the accompanying drawings. It can be seen that the described are some embodiments of the present invention, rather than all embodiments. According to the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope protected by the present invention. By combining the drawings, the technical solutions of the present invention can be clearly shown.

[0020] The present invention will be further described below with reference to the drawings and embodiments.

[0021] Please refer to Figure 1 , the schematic flow chart of the radiation intensity measurement method of an infrared radiation simulation source system of the present invention.

[0022] First, preheat the infrared radiation source to be measured to a thermal steady state to ensure the stability of its radiation output. Subsequently, focus and irradiate the radiation energy onto the photosensitive surface of the infrared detector calibrated by a standard blackbody through a mid-wave infrared optical system. After the detector receives the radiation energy and converts it into an electrical signal, the signal is processed by signal conditioning modules such as transimpedance amplification, filtering, and gain to obtain a stable alternating voltage signal. The Kalman filter algorithm is used to perform dynamic denoising on the signal. Combining with the mid-wave infrared radiation coefficient correction algorithm, a non-linear mapping relationship between the output voltage and the infrared radiation intensity is established. Finally, the data inversion and calculation are completed through the algorithm module embedded in the host computer software, and the radiation intensity numerical result of the target infrared radiation source within the specified wavelength band is output.

[0023] The HgCdTe infrared detector used in the present invention is an infrared mercury cadmium telluride detector (HgCdTe, MCT), which is an infrared detector based on semiconductor materials and mainly composed of mercury cadmium telluride alloy. Its composition generally includes an active detection layer, electrodes, packaging, and a cooling system (such as a liquid nitrogen cooler). The characteristics of the MCT detector lie in its wide wavelength response range (about 1 µm to 20 µm), high sensitivity, and low noise performance. By adjusting the ratio of Hg to Cd, the wavelength of the detector response can be optimized. In addition, the MCT detector operates at low temperatures, which can significantly improve the detection accuracy. The application fields include thermal imaging, night vision equipment, gas detection, remote sensing, scientific research, military reconnaissance, and security technology, etc. Its excellent sensitivity and response ability play an important role in various infrared detection requirements, so this type of infrared detector is selected.

[0024] The TIA (Transimpedance Amplifier) used in the present invention is an amplifier that converts a current signal into a voltage signal. Its basic composition includes a photodetector (such as a photodiode or a photomultiplier tube) at the input end and an operational amplifier with negative feedback. The photodetector converts the optical signal into a current signal. The TIA combines the input current signal with a negative feedback resistor to output a voltage signal proportional to the input current. The main advantages of the TIA are high bandwidth, low noise, and high gain, which can effectively amplify low-current signals while maintaining signal integrity. After demonstration and analysis, the MAX4000 series TIA of Maxim Integrated is selected.

[0025] Please refer to Figure 2 , which shows the workflow block diagram of the radiation intensity measurement method of an infrared radiation simulation source system of the present invention.

[0026] As an example, the method includes the following specific steps: Step 1: Radiometric calibration: First, use a standard surface source blackbody to perform full-band calibration on the infrared detector to obtain the radiation intensity response relationship between the surface source blackbody and the infrared detector. Subsequently, perform secondary calibration in two mid-wave infrared bands of the infrared radiation simulation source to be measured, and establish the response relationship between the output signal of the infrared detector and the infrared radiation intensity in the dual-band; Step 2: Photoelectric conversion: Preheat the infrared radiation simulation source to be measured to a stable working state. After being focused by the mid-wave infrared optical system, the calibrated infrared detector receives the radiation energy and converts it into a corresponding electrical signal; Step 3: Signal preprocessing: Filter, gain amplify, and modulate the electrical signal obtained in Step 2 to obtain a high signal-to-noise ratio signal; Step 4: Algorithm correction: Use the Kalman filter algorithm to optimize the dynamic signal of the high signal-to-noise ratio signal obtained in Step 3, and combine it with the mid-wave infrared radiation coefficient correction algorithm to compensate for the nonlinear error and establish the mapping relationship between the detection voltage and the radiation intensity; Step 5: Data output: Calculate the infrared radiation intensity parameter obtained through the mapping relationship between the detection voltage and the radiation intensity in Step 4 and upload it to the upper computer software of the measurement system to display and store the measurement results in real time, realizing the visualization and analysis of high-precision measurement data.

[0027] First, use a standard surface source blackbody source to perform full-band calibration on the infrared detector to obtain the radiation intensity response relationship between the blackbody and the infrared detector. The specific method is as follows: Take the standard surface source blackbody as the benchmark. First, use the near-field extended source method to perform full-band calibration. During calibration, place the surface source blackbody in front of the mid-wave infrared optical system, and set an optical chopper between the mid-wave infrared optical system and the infrared detector to make the infrared radiation from the surface source blackbody focus on the photosensitive surface of the infrared detector. By controlling the light source temperature of the surface source blackbody, establish the radiation intensity response relationship between the surface source blackbody with temperature and the infrared detector as follows: , where, is an infinitesimal change in wavelength, is an infinitesimal area element on the detector, is the infrared radiation intensity of the surface source blackbody, is the working temperature of the surface source blackbody, is the responsivity of the infrared detector to the incident radiation intensity, is the response range of the infrared detector, is the spectral response of the infrared detector, is the transmittance of the mid-wave infrared optical system, is the gain of the circuit, is the temperature of The radiant intensity of the surface source blackbody is the area of the surface element on the detection surface; Collect the voltages output by the surface source blackbody at different working temperatures, and calculate the corresponding radiation intensity through the above formula, and calculate ; A mid-infrared band-pass filter with a transmission range of 3550–4150 nm and 4500–5000 nm is added in front of the infrared detector. By switching between two different mid-infrared band-pass filters, the infrared radiation simulation source to be measured can be made to work in two mid-wave infrared bands of 3.5 μm to 4 μm and 4.5 μm to 4.8 μm; Establish the radiation intensity response relationship between the surface source blackbody and the infrared detector at temperatures within the measurement bands, namely 3.5 μm to 4 μm and 4.5 μm to 4.8 μm, as follows: and respectively represent the infrared radiation intensity of the surface source blackbody in the bands of 3.5 μm to 4 μm and 4.5 μm to 4.8 μm, and are the spectral transmittances of the two bands respectively, and are the response rates of the detector to the target radiation intensity in the two bands respectively. The above process realizes the radiation calibration of the infrared detector in the full band and specific bands, and collects the spectral response rates output by the blackbody at different working temperatures, improving the measurement consistency and accuracy.

[0028] Furthermore, the specific method of photoelectric conversion in step 2 is to preheat the infrared radiation source to be measured to reach a thermal steady state, and set the radiation intensity output value by the upper computer software, accurately control the temperature within the specified range to ensure the stability of the radiation intensity. After the infrared radiation is focused by the mid-wave infrared optical system, it is converted into an alternating signal by the optical chopper, and then the calibrated detector receives the radiation energy for subsequent signal processing.

[0029] Furthermore, the infrared radiation source system to be measured in step 2 is a conventional infrared radiation source system in the prior art, which generally includes the following parts: a) Optical system: including mirrors (primary mirror and secondary mirror), filters, etc.; b) Infrared radiation source control system: including a radiator (silicon molybdenum rod), a drive power supply, a temperature control system and a heat dissipation system; c) Radiation intensity control system: including a shutter and a transmission system, a motor drive and a control computer; ​​​d) Auxiliary components: including the housing, support and connection structural members.

[0030] In this embodiment, the mid-wave infrared optical system adopts a Cooke triplet infrared optical imaging structure, as Figure 5 shown, to achieve efficient focusing and detection of infrared radiation energy. It is specifically composed of two double convex lenses and one double concave lens, and the arrangement order is: double convex lens L1 - double concave lens L2 - double convex lens L3. Among them, both double convex lenses are selected as the standard product model 110148 of United Optoelectronics, and the effective focal lengths are both 50 mm; the middle double concave lens is selected as model 130076, and the focal length is -30 mm. The three lenses together form a compound lens system with a reduced focal length of about 71 mm, and the total length of the system is about 120 mm. This structure has good aberration correction ability in the mid-wave infrared band (3.5 - 5 μm) range, can focus most of the radiation energy onto a 1 mm × 1 mm HgCdTe infrared detector, and effectively control the field of view within the range of 0° - 0.4°, thereby improving the measurement accuracy and suppressing the interference of stray background light. This mid-wave infrared optical system has a simple structure, is convenient for processing, installation and adjustment, and has the advantages of reasonable design, stable performance and controllable cost, and is suitable for the effective focusing and transmission of mid-wave infrared radiation signals. In addition, an optical chopper needs to be set to change the continuous signal output by the infrared radiation source into an alternating signal.

[0031] Since the infrared radiation simulation source system to be measured operates in two mid-wave infrared bands of 3.5μm - 4μm and 4.5μm - 4.8μm, in the process of system calibration, mid-wave infrared band-pass filters with matching bands are respectively selected, and their transmission ranges are 3550 - 4150nm and 4500 - 5000nm, and the average transmittance is greater than 90%, effectively ensuring the band selectivity and energy transmission efficiency during the calibration process.

[0032] Infrared optical system simulation experiment: The present invention verifies the focusing performance and anti-interference ability of the designed mid-wave infrared optical system through optical simulation. In the simulation experiment, typical mid-wave infrared bands with wavelengths of 3500nm, 3750nm, 4000nm, 4500nm, 4650nm and 4800nm are selected for beam envelope analysis (diffraction performance) and spot diagram analysis (geometric imaging performance): From Figure 3 the beam envelope analysis results, nearly 90% of the energy of the six wavelengths is concentrated in a circular area with a diameter of about 0.7mm on the focal plane, ensuring that the infrared radiation energy is effectively focused on the photosensitive surface of the HgCdTe infrared detector and improving the reception efficiency.

[0033] The spot diagram analysis results are as Figure 4The results show that the average RMS spot diameter of the foci corresponding to the six wavelengths is 0.678 mm, which is much smaller than the target surface size of the detector (1 mm × 1 mm), meeting the design requirements and verifying the good imaging consistency of the system.

[0034] In this embodiment, through the field of view angle simulation analysis as Figure 5 shown, under the condition that the detector focal length EFL is 71.0347 mm, the maximum receiving field of view is about 0.4°. When the green light hits the edge of the detector exactly at this limit field of view; while when the field of view angle reaches 5° (blue light), the light beam has deviated from the detector. The smaller receiving field of view angle effectively suppresses the interference of background stray light and significantly improves the infrared radiation measurement accuracy.

[0035] The front optical chopper is used to convert the continuous signal of infrared radiation into an alternating signal to suppress low-frequency noise and drift interference, improve the stability and signal-to-noise ratio of the signal, and facilitate subsequent filtering, gain amplification and synchronous detection processing.

[0036] The signal processing module in step 3 of this embodiment includes a two-stage circuit structure, specifically as Figure 6 shown. The first stage is a transimpedance amplifier (TIA) circuit, which is used to convert the weak current signal output by the detector into a voltage signal and achieve preliminary gain amplification; the second stage is a filter gain amplification circuit, including a pre-filter and an amplification circuit, which is used to analyze the spectral characteristics of the TIA output signal, suppress high and low frequency interference signals including thermal noise, white noise, G-R noise and 1 / f noise, optimize the signal quality and then input the alternating voltage signal into the analog-to-digital converter (A / D). The STM32 controller collects the peak-to-peak voltage and converts it into a digital signal for subsequent processing.

[0037] The transimpedance amplifier (TIA) in the first-stage amplifier circuit is designed with a low-noise operational amplifier, which has high input impedance and wide bandwidth characteristics, can realize the stable conversion and linear amplification of pA-level current signals, and has the signal conversion ability of high gain and low drift, and is suitable for the front-end amplification processing of weak infrared signals.

[0038] The second-stage pre-filter gain amplification circuit adopts an active filter structure, combines a low-pass or band-pass filter network to suppress high-frequency noise and enhances signals in different frequency bands through an adjustable gain operational amplifier circuit, improving the signal-to-noise ratio and dynamic range of the signal.

[0039] Further, to improve the measurement stability of the system in complex environments, in step 4, a Kalman filtering algorithm based on STM32 is used to filter and correct the output signal of the infrared detector. Considering the fluctuations in the response of the HgCdTe detector and the significant influence of factors such as temperature and stray light when the system is outdoors, traditional filtering methods are difficult to effectively suppress noise. Kalman filtering establishes a state equation and an observation equation, estimates the current radiation intensity and noise state in real time, and dynamically updates the filtering gain to achieve high-precision correction of the measured voltage, improving the anti-interference ability and measurement accuracy of the system in a non-steady-state environment. The specific principle is as Figure 7 .

[0040] Assume that the measured voltage and the radiation intensity satisfy the function , and the circuit noise satisfies the function . The state variable is the infrared radiation intensity, and the state variable is the noise voltage. The observed variable is the output voltage . Then there is a state-observation model for Kalman filtering estimation: , where represents the prior estimate at the th moment, represents the prior estimate at the th moment, represents the state transition matrix; then for the calculation of the prior estimate covariance, first set , , where represents the prior state covariance matrix at the th moment, reflecting the uncertainty of the system state estimate before this observation; represents the prior state covariance matrix at the th moment, reflecting the estimated error distribution after correction by the previous observation, respectively represent the variance estimates of the infrared radiation intensity and the noise voltage at the current moment, respectively represent the covariance between the two state variables, and satisfy ; respectively represent the variance estimates of the infrared radiation intensity and the noise voltage at the previous moment, respectively represent the covariance between the two state variables; Construct the covariance matrix of the two state variables. Since the two state variables are independent of each other, there is: , where represents the system process noise covariance matrix, used to quantify the uncertainty in the state transition process; The covariance formula for the a priori estimate is obtained as follows: , where represents the process noise variance of the radiation intensity, represents the process noise variance of the circuit noise; Expanding the above formula gives the specific expression of the covariance matrix: , Since the set state quantity includes the voltage measurement noise, the system measurement equation is established as , and the measurement matrix . Thus, the Kalman gain is obtained: , where represents the Kalman gain of the radiation intensity state, which is used to adjust the predicted radiation intensity; represents the Kalman gain of the circuit noise state, which is used to estimate and suppress the error caused by the measurement noise, represents the observed value at the th moment, that is, the actual measured voltage, represents the response coefficient of the output voltage of the infrared detector to the radiation intensity, is the radiation noise; Next, calculate the current optimal estimated value: , where represents the a priori estimated value of the radiation intensity at the current moment, that is, the predicted value, represents the gain term corresponding to the radiation intensity state in the Kalman gain, that is ; Finally, update the covariance matrix: , represents the variance of the radiation intensity with itself in the updated covariance matrix; represents the covariance between the radiation intensity and the circuit noise after updating; represents the covariance between the circuit noise and the radiation intensity after updating; represents the variance of the circuit noise with itself in the updated covariance matrix; After the above process, one filtering cycle is completed.

[0041] Furthermore, in step 4, the mid-wave infrared radiation coefficient correction algorithm is combined to compensate for the nonlinear error and establish the mapping relationship between the detection voltage and the radiation intensity. The specific method is as follows: The radiation intensity response of a specific wavelength band after passing through a mid-wave infrared optical system and a circuit is shown in Equation (2). However, in the actual radiation calibration process, the spectral responsivity of an infrared detector is not given for a narrow wavelength band, but shows certain variations within a relatively wide wavelength band, resulting in differences. To simplify the calculation and improve the measurement feasibility, when estimating the target radiation intensity within each specific measurement wavelength band by the measurement system, the detector spectral response pair is regarded as a constant. Based on this, the radiation intensity correction coefficient is defined, and is regarded as a constant for calculation, so as to correct the signal measured by the system and improve the accuracy of radiation intensity inversion; The radiation intensity correction coefficient is: , Therefore, the infrared radiation intensity and after correction of the mid-wave infrared radiation coefficient are calculated as: , , For the solution of , a linear calibration model is adopted, and the spectral responsivity of the detector is not considered for the time being. Only the spectral transmittance of the filter and the gain of the circuit are considered to calculate the target radiation intensity; Therefore, the final calibration model is: , where is the spectral transmittance of the specified wavelength band, is the target radiation intensity response of the dual-wavelength measurement system considering only the spectral transmittance of the filter. Here, the infrared radiation intensity is theoretically calculated at different temperatures, and the voltage output by the detector is obtained. The two are fitted to obtain , through the following formula: , Furthermore, the equivalent radiation intensity of the blackbody radiation intensity after adding the filter corrected by the radiation intensity correction coefficient is obtained: , Substituting it in, we get: , Through the above-mentioned corrected equivalent radiation intensity The calculation formula is further used to verify the accuracy of the radiation intensity calibration curve of the target radiation source in each measurement band. In summary, the radiation intensity correction coefficients within each specified band can be finally determined, providing an accurate calibration basis for subsequent radiation intensity measurement and inversion of the system.

[0042] In the described method for measuring the radiation intensity of an infrared radiation simulation source system, the output result in step 5 refers to the alternating current signal after signal processing being imported into the calculation module, and through system operation, the radiation intensity value of the target infrared radiation source within the set band is obtained, and this result is output to the upper computer software of the measurement system for display and recording, finally obtaining measurement data with the radiation intensity fluctuation range within the controllable threshold.

[0043] The upper computer software system has functions of data reception, real-time display and historical record, can perform curve fitting, fluctuation analysis and data storage on the output infrared radiation intensity data, and supports setting a fluctuation threshold to realize abnormal recognition and prompt of the measurement result, further improving the stability and reliability of the system measurement.

[0044] Data transmission between the upper computer software system and the measuring device is realized through a serial communication interface, and the communication interface includes but is not limited to USB, RS-232, CAN or Ethernet protocol to ensure the real-time and stability of data interaction.

[0045] In the present invention, features described and / or exemplified for one embodiment can also be used in the same or similar manner in one or more other embodiments, and can be combined with or replace the features of other embodiments.

Claims

1. A method for measuring the radiation intensity of an infrared radiation simulation source system, characterized in that, The method includes the following steps: Step 1: Radiometric calibration: First, use a standard surface source blackbody to perform full-band calibration on the infrared detector to obtain the radiation intensity response relationship between the surface source blackbody and the infrared detector; subsequently, perform secondary calibration in two mid-wave infrared bands of the infrared radiation simulation source to be measured, and establish the response relationship between the output signal of the infrared detector and the infrared radiation intensity in the dual bands; Step 2: Photoelectric conversion: Preheat the infrared radiation simulation source to be measured to a stable working state. After being focused by the mid-wave infrared optical system, the calibrated infrared detector receives the radiation energy and converts it into a corresponding electrical signal; Step 3: Signal preprocessing: Perform filtering, gain amplification, and modulation processing on the electrical signal obtained in Step 2 to obtain a high signal-to-noise ratio signal; Step 4: Algorithm correction: Use the Kalman filter algorithm to optimize the dynamic signal of the high signal-to-noise ratio signal obtained in Step 3, and combine with the mid-wave infrared radiation coefficient correction algorithm to compensate for the nonlinear error and establish the mapping relationship between the detection voltage and the radiation intensity; Step 5: Data output: Calculate the infrared radiation intensity parameter through the mapping relationship between the detection voltage and the radiation intensity in Step 4 and upload it to the upper computer software of the measurement system to display and store the measurement results in real time.

2. The method for measuring the radiation intensity of an infrared radiation simulation source system according to claim 1, characterized in that, The infrared detector uses a HgCdTe infrared detector; the mid-wave infrared optical system uses a Cooke triplet structure design, including two double convex lenses on both sides and a double concave lens between the two double convex lenses; among them, the effective focal lengths of the two double convex lenses are both 50 mm; the focal length of the middle double concave lens is -30 mm; the three lenses together form a compound lens system with a reduced focal length of about 71 mm and a total system length of about 120 mm; The infrared radiation simulation source to be measured operates in two mid-wave infrared bands of 3.5μm - 4μm and 4.5μm - 4.8μm. In the radiometric calibration process, mid-wave and far-infrared band-pass filters with matching bands are selected respectively, and their transmission ranges are 3550–4150nm and 4500–5000nm.

3. The method for measuring the radiation intensity of an infrared radiation simulation source system according to claim 1 or 2, characterized in that, The specific method of Step 1 is: First, use a standard surface source blackbody to perform full-band calibration on the infrared detector to obtain the radiation intensity response relationship between the blackbody and the infrared detector. The specific method is as follows: Taking the standard area source blackbody as the reference, the full-band calibration is first carried out by the near-distance extended source method. During calibration, the area source blackbody is placed in front of the mid-wave infrared optical system, and an optical chopper is set between the mid-wave infrared optical system and the infrared detector to make the infrared radiation from the area source blackbody focus on the photosensitive surface of the infrared detector. By controlling the light source temperature of the area source blackbody, the radiation intensity response relationship between the area source blackbody with a temperature of and the infrared detector is established as follows: ， wherein, is an infinitesimal change in wavelength, is an infinitesimal area element on the detector, is the infrared radiation intensity of the surface source blackbody, is the working temperature of the surface source blackbody, is the responsivity of the infrared detector to the incident radiation intensity, is the response range of the infrared detector, is the spectral response of the infrared detector, is the transmittance of the mid-wave infrared optical system, is the gain of the circuit, is at a temperature of the radiant luminance of the surface source blackbody, is the area of the surface element on the detection surface; Collect the voltages output by the collecting area source blackbody at different working temperatures, and calculate the corresponding radiation intensities through the above formula, and calculate ; Subsequently, perform secondary calibration in two mid-wave infrared bands of the infrared radiation simulation source to be measured, and establish the response relationship between the output signal of the infrared detector and the infrared radiation intensity in the dual bands. The specific method is as follows: A mid-wave and far-infrared band-pass filter with a transmission range of 3550–4150nm and 4500–5000nm is added in front of the infrared detector. By switching between the two different mid-wave and far-infrared band-pass filters, the infrared radiation simulation source to be measured can operate in two mid-wave infrared bands of 3.5μm - 4μm and 4.5μm - 4.8μm; Establish the radiation intensity response relationship between the surface source blackbody with a temperature of within the measurement wavelength bands, i.e., 3.5 μm to 4 μm and 4.5 μm to 4.8 μm, and the infrared detector, specifically as follows: ， ， and represent the infrared radiation intensity of the area source blackbody in the wavelength bands of 3.5 μm to 4 μm and 4.5 μm to 4.8 μm respectively, and are the spectral transmittances of the two wavelength bands respectively, and are the response rates of the detector to the target radiation intensity in the two wavelength bands respectively.

4. The method for measuring the radiation intensity of an infrared radiation simulation source system according to claim 1, characterized in that, The signal processing module used in the signal preprocessing in step 3 includes a two-stage circuit structure. The first stage is a transimpedance amplifier circuit, i.e., TIA, which is used to convert the weak current signal output by the infrared detector into a voltage signal and achieve preliminary gain amplification. The second stage is a filter gain amplification circuit, including a pre-filter and an amplification circuit, which is used to analyze the spectral characteristics of the TIA output signal, suppress high and low frequency interference signals including thermal noise, white noise, G-R noise and 1 / f noise, optimize the signal quality, input the alternating voltage signal into the analog-to-digital converter A / D, and the STM32 controller collects the peak-to-peak voltage and converts it into a digital signal for subsequent processing.

5. The method for measuring the radiation intensity of an infrared radiation simulation source system according to claim 1, characterized in that, In step 4, the Kalman filter algorithm is used to optimize the high signal-to-noise ratio signal obtained in step 3. The specific method is as follows: Assume that the measured voltage and the radiation intensity satisfy the function , and the circuit noise satisfies the function . The state variable is the infrared radiation intensity, and the state variable is the noise voltage. The observed quantity is the output voltage . Then there is a state-observation model for Kalman filter estimation: ， Among them, represents the prior estimate at the th moment, represents the prior estimate at the th moment; represents the state transition matrix. Then, for the calculation of the prior estimate covariance, first set , , where represents the prior state covariance matrix at the th moment, reflecting the uncertainty of the system state estimate before this observation; represents the prior state covariance matrix at the th moment, reflecting the estimated error distribution after correction by the previous observation, respectively represent the variance estimates of the infrared radiation intensity and the noise voltage at the current moment, respectively represent the covariance between two state variables, and satisfy ; respectively represent the variance estimates of the infrared radiation intensity and the noise voltage at the previous moment, respectively represent the covariance between two state variables; Construct the covariance matrix of two state variables. Since the two state variables are known to be independent of each other, we have: ， Among them, represents the system process noise covariance matrix, which is used to quantify the uncertainty in the state transition process; From this, the covariance formula for the prior estimate is obtained as: ， Among them, represents the process noise variance of the radiation intensity, represents the process noise variance of the circuit noise; Expand the above formula to obtain the specific expression of the covariance matrix: ， Since the set state quantity includes voltage measurement noise, the system measurement equation is established as , and the measurement matrix . Thus, the Kalman gain is obtained as follows: ， Among them, The Kalman gain representing the radiation intensity state is used to adjust the predicted radiation intensity; The Kalman gain representing the circuit noise state is used to estimate and suppress the error caused by the measurement noise, Indicates the Observation value at time, that is, the actual measured voltage, Represents the response coefficient between the output voltage of the infrared detector and the radiation intensity, Is the radiation noise; Next, calculate the current optimal estimate value: ， Among them, represents the prior estimated value of the radiation intensity at the current moment, that is, the predicted value, represents the gain term corresponding to the radiation intensity state in the Kalman gain, that is ; Finally, update the covariance matrix: ， Denotes the variance of the radiation intensity with itself in the updated covariance matrix; Denotes the covariance between the radiation intensity and the circuit noise after update; Denotes the covariance between the circuit noise and the radiation intensity after update; Denotes the variance of the circuit noise with itself in the updated covariance matrix; After the above process, one filtering cycle is completed.

6. The radiation intensity measurement method of an infrared radiation simulation source system according to claim 5, characterized in that, In step 4, the mid-wave infrared radiation coefficient correction algorithm is combined to compensate for the nonlinear error and establish the mapping relationship between the detection voltage and the radiation intensity. The specific method is as follows: When the measurement system estimates the target radiation intensity within each specific measurement band, the detector spectral response pair is regarded as a constant. Based on this, a radiation intensity correction coefficient is defined , and is regarded as a constant for calculation, so as to correct the signal measured by the system and improve the accuracy of radiation intensity inversion; Radiation intensity correction factor is as follows: ， Therefore, the infrared radiation intensity after correcting the mid-wave infrared radiation coefficient and The calculation formula is as follows: ， ， For the solution, a linear calibration model is adopted. Without considering the spectral responsivity of the detector for the time being, only the spectral transmittance of the filter and the gain of the circuit are considered. Calculate the target radiation intensity. Therefore, the final calibration model is as follows: ， wherein is the spectral transmittance of a specified band is the response of the dual-band measurement system to the target radiation intensity considering only the spectral transmittance of the filter. Here, the infrared radiation intensity is theoretically calculated at different temperatures, and the voltage output by the detector is obtained. The two are fitted to obtain , through the following formula: ， Furthermore, the equivalent radiation intensity of the blackbody radiation intensity after adding the filter corrected by the radiation intensity correction coefficient is obtained. : ， Substitute to get: ， The equivalent radiation intensity after the above-mentioned correction is further used to verify the accuracy of the radiation intensity calibration curve of the target radiation source in each measurement band.

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