A method for measuring radiation intensity of an infrared radiation simulation source system

Abstract

The present invention discloses a method for measuring the radiation intensity of an infrared radiation simulation source system, which belongs to the technical field of infrared radiation characteristic measurement. First, a radiation calibration operation is performed on the infrared radiation simulation source system and the infrared detector; a medium-wave infrared optical system and an infrared detector are used to receive the infrared radiation signal, and the continuous radiation is converted into an alternating signal through optical chopper modulation to improve the measurement signal-to-noise ratio. The collected alternating signal is processed by a filter gain amplifier circuit to enhance signal stability. Subsequently, a Kalman filter algorithm is used to perform dynamic denoising and state estimation on the measurement data, and a medium-wave infrared radiation coefficient correction algorithm is combined to perform nonlinear error compensation, thereby obtaining a high-precision radiation intensity correction coefficient. Compared with traditional methods, the present invention combines adaptive filtering and radiation intensity correction technology to improve measurement accuracy and real-time performance, and can be widely used in infrared optical testing, target characteristic analysis and radiation metrology.

Description

Technical Field

[0001] The invention belongs to the technical field of infrared radiation characteristic measurement, and in particular 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 areas of infrared radiation measurement technology are expanding. In the civilian sector, infrared technology has been widely used in numerous sectors, including 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 support and monitoring tools for production safety and product quality at all stages of product development, production, and testing. In the military, measuring the infrared radiation intensity of targets has become crucial for target tracking and identification. Compared with traditional image feature recognition techniques, infrared radiation characteristic measurement technology offers significant advantages in passive detection and strong anti-interference capabilities. Combined with computer technology, we can establish a database of radiation characteristics of typical targets and their characteristic locations, providing a more reliable and effective reference for applications such as missile air defense warning and weapon stealth effectiveness evaluation. As a core indicator of target infrared radiation characteristics, accurate measurement of infrared radiation intensity is of great significance for advancing the application of infrared technology as a whole.

[0003] The HgCdTe infrared detector mentioned in this invention is an infrared mercury cadmium telluride (MCT) detector. It is a semiconductor-based infrared detector primarily composed of a mercury cadmium telluride alloy. Its components typically include an active detection layer, electrodes, packaging, and a cooling system (such as a liquid nitrogen cooler). MCT detectors are characterized by their wide wavelength response range (approximately 1µm to 20µm), high sensitivity, and low noise performance. By adjusting the ratio of Hg to Cd, the detector's response wavelength can be optimized. Furthermore, MCT detectors operate at low temperatures, significantly improving detection accuracy. Applications include thermal imaging, night vision equipment, gas detection, remote sensing, scientific research, military reconnaissance, and security technology. Their excellent sensitivity and responsiveness make them important for various infrared detection applications. With technological advancements, the integration and miniaturization of MCT detectors have led to their increasing use in portable devices.

[0004] The infrared radiation source to be measured specifically refers to a device that radiates infrared energy by heating a radiator. This device includes an optical system consisting of a heater, parabolic reflector, and filter assembly, a control system consisting of a voice coil motor and shutters, a power supply system, and supporting structures. The infrared radiation characteristics of various military targets can be simulated based on scenario or experimental requirements.

[0005] Infrared radiation intensity is an indirect physical quantity that cannot be directly measured using instruments. However, it is both a key physical quantity describing the infrared radiation characteristics of a target and a core parameter for various infrared radiation simulation sources. Therefore, accurately measuring this parameter using existing advanced instruments combined with solid theoretical derivation is of great significance. However, currently, relevant measurement technologies and methods in China are limited or lack practicality and accuracy, making them unable to fully meet the factory acceptance requirements of some infrared radiation simulation source equipment and making it difficult to understand the usage status and needs of related products. Summary of the Invention

[0006] This paper proposes a measurement method that combines a Kalman filter algorithm with a medium-wave infrared radiation coefficient correction algorithm. This method accurately establishes a nonlinear mapping relationship between detection voltage and infrared radiation intensity, thereby achieving high-precision inversion and assessment of the infrared radiation source's radiation intensity. The method first heats the infrared radiation source to a stable operating state. A medium-wave infrared optical system then focuses its radiation energy onto the calibrated photosensitive surface of an infrared detector. The detector receives the radiation energy and converts it into a corresponding electrical signal. The collected electrical signal then undergoes preliminary processing via a filter-gain amplifier circuit to improve the signal-to-noise ratio and suppress various types of noise interference. Furthermore, a Kalman filter algorithm dynamically estimates and filters the signal sequence. Combined with medium-wave infrared radiation coefficient correction and atmospheric transmittance correction, a nonlinear response model is established between the detector output voltage and the actual radiation intensity. Finally, a computation module integrated into the host computer software solves the processed signal and outputs the radiation intensity value of the infrared radiation source within a specified wavelength band.

[0007] The measurement method provided by the present invention not only realizes the accurate acquisition of infrared radiation intensity in the medium-wave infrared band, and has good adaptability and stability, but can also be used for factory performance verification, working status 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 comprises the following steps:

[0009] Step 1: Radiation Calibration: First, the infrared detector is calibrated across all wavelengths using a standard blackbody to obtain the radiation intensity response relationship between the blackbody and the infrared detector. Secondary calibration is then performed in two mid-wave infrared bands using the simulated infrared radiation source to establish the response relationship between the infrared detector output signal and the infrared radiation intensity in both bands.

[0010] Step 2: Photoelectric conversion: The infrared radiation simulation source to be measured is preheated to a stable working state. After being focused by the medium-wave infrared optical system, the radiated energy is received by the calibrated infrared detector and converted into a corresponding electrical signal.

[0011] 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;

[0012] Step 4: Algorithm correction: Use the Kalman filter algorithm to perform dynamic signal optimization on the high signal-to-noise ratio signal obtained in step 3, and combine it with the medium-wave infrared radiation coefficient correction algorithm to compensate for nonlinear errors and establish a mapping relationship between detection voltage and radiation intensity;

[0013] Step 5: Data output: The infrared radiation intensity parameters calculated by the mapping relationship between the detection voltage and radiation intensity in step 4 are uploaded to the host computer software of the measurement system to display and store the measurement results in real time.

[0014] Furthermore, the infrared detector is a HgCdTe infrared detector; the medium-wave infrared optical system adopts a Cooke three-piece structure design, including two biconvex mirrors on both sides and a biconcave mirror located between the two biconvex mirrors; wherein the effective focal length of the two biconvex mirrors is 50 mm; the focal length of the middle biconcave mirror is -30 mm; the three lenses together form a composite lens system with a reduced focal length of approximately 71 mm, and the total length of the system is approximately 120 mm;

[0015] The infrared radiation simulation source to be measured operates in two medium-wave infrared bands of 3.5μm to 4μm and 4.5μm to 4.8μm. During the radiation calibration process, mid- and far-infrared bandpass filters with matching bands are selected respectively, and their transmission ranges are 3550–4150nm and 4500–5000nm.

[0016] Furthermore, the specific method of step 1 is:

[0017] First, a standard surface blackbody source is used to calibrate the infrared detector across the entire wavelength range to obtain the radiation intensity response relationship between the blackbody and the infrared detector. The specific method is as follows:

[0018] The standard surface source blackbody is used as a benchmark. First, the close-range extended source method is used to calibrate the full band. During calibration, the surface source blackbody is placed in front of the medium-wave infrared optical system, and an optical chopper is set between the medium-wave infrared optical system and the infrared detector to focus the infrared radiation from the surface source blackbody on the photosensitive surface of the infrared detector. By controlling the light source temperature of the surface source blackbody, a temperature of The radiation intensity response relationship between the surface source blackbody and the infrared detector is as follows:

[0019] ,

[0020] in, is an infinitesimal change in wavelength, is an infinitesimal area element on the detector, is the infrared radiation intensity of the surface blackbody, is the operating 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 medium-wave infrared optical system, is the gain of the circuit, The temperature is The radiation brightness of the surface source black body is is the area of ​​the element on the detection surface;

[0021] Collect the voltage output by the surface source blackbody at different working temperatures, and calculate the corresponding radiation intensity using the above formula to calculate ;

[0022] Then, a secondary calibration is performed in two medium-wave infrared bands of the infrared radiation simulation source to be measured to establish the response relationship between the infrared detector output signal and the infrared radiation intensity in the dual bands. The specific method is as follows:

[0023] A mid-infrared bandpass filter with a transmission range of 3550–4150nm and 4500–5000nm is added in front of the infrared detector. By switching between two different mid-infrared bandpass filters, the infrared radiation simulation source to be measured can be operated in the two mid-wave infrared bands of 3.5μm to 4μm and 4.5μm to 4.8μm.

[0024] Based on the measurement band, the temperature in the 3.5μm~4μm and 4.5μm~4.8μm bands is The radiation intensity response relationship between the surface source blackbody and the infrared detector is as follows:

[0025] ,

[0026] ,

[0027] and They represent the infrared radiation intensity of surface source blackbody in the 3.5μm~4μm and 4.5μm~4.8μm bands respectively. and are the spectral transmittance of the two bands respectively, and The response rate of the detector to the target radiation intensity in the two bands respectively.

[0028] Furthermore, the signal processing module used in the signal preprocessing in step 3 includes a two-stage circuit structure, wherein 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 realize preliminary gain amplification; the second stage is a filter gain amplifier circuit, including a prefilter and an amplifier 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, GR noise and 1 / f noise, and input the alternating voltage signal into the analog-to-digital converter A / D after optimizing the signal quality, and the STM32 controller collects the peak-to-peak voltage and converts it into a digital signal for subsequent processing.

[0029] Furthermore, in step 4, the Kalman filter algorithm is used to perform dynamic signal optimization on the high signal-to-noise ratio signal obtained in step 3. The specific method is as follows:

[0030] Assume that the measured voltage and radiation intensity satisfy the function , the circuit noise satisfies the function , state quantity is the infrared radiation intensity, state quantity is the noise voltage, and the observed quantity is the output voltage , then there is a state-observation model for Kalman filter estimation:

[0031] ,

[0032] in, Indicates the A priori estimate of the time, Indicates the A priori estimate of the time, Represents the state transfer matrix; then for the prior estimate covariance calculation, first set , ,in, Indicates the The prior state covariance matrix at time t reflects the uncertainty of the system state estimation before this observation; Indicates the The prior state covariance matrix at the moment reflects the estimated error distribution after the last observation correction. Respectively represent the variance estimation of infrared radiation intensity and noise voltage at the current moment, Represent the covariance between two state quantities, and satisfy ; Respectively represent the variance estimation of infrared radiation intensity and noise voltage at the previous moment, denote the covariance between two state variables respectively;

[0033] Construct the covariance matrix of the two state quantities. It is known that the two state quantities are independent of each other, so we have:

[0034] ,

[0035] in, Represents the system process noise covariance matrix, which is used to quantify the uncertainty in the state transition process;

[0036] The covariance formula of the prior estimate is:

[0037] ,

[0038] in, represents the process noise variance of the radiation intensity, The process noise variance represents the circuit noise;

[0039] Expanding the above formula gives the specific expression of the covariance matrix:

[0040] ,

[0041] Due to the set state Including voltage measurement noise, the system measurement equation is established as , the measurement matrix , and the Kalman gain is obtained:

[0042] ,

[0043] in, 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 measurement noise. Indicates the The observed value at the moment, that is, the actual measured voltage, Indicates the response coefficient of the infrared detector output voltage and radiation intensity, is the radiated noise;

[0044] Next, calculate the current best estimate:

[0045] ,

[0046] in, represents the prior estimate 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, ;

[0047] Finally update the covariance matrix:

[0048] ,

[0049] Represents the variance of the radiation intensity and itself in the updated covariance matrix; represents the covariance between the radiation intensity and the circuit noise after the update; represents the covariance between circuit noise and radiation intensity after updating; represents the variance of the circuit noise and itself in the updated covariance matrix;

[0050] After the above process, one filtering cycle is completed.

[0051] Furthermore, in step 4, the medium-wave infrared radiation coefficient correction algorithm is combined to compensate for nonlinear errors and establish a mapping relationship between detection voltage and radiation intensity. The specific method is as follows:

[0052] When the measurement system estimates the target radiation intensity within each specific measurement band, the detector spectral response is regarded as a constant. Based on this, the radiation intensity correction coefficient is defined. ,Will It is regarded as a constant for calculation, thereby correcting the signal measured by the system and improving the accuracy of radiation intensity inversion;

[0053] Radiation intensity correction factor for:

[0054] ,

[0055] Therefore, the infrared radiation intensity after the medium-wave infrared radiation coefficient is corrected and The calculation formula is:

[0056] ,

[0057] ,

[0058] for The linear calibration model is used to solve the problem. The spectral response rate 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. , calculate the target radiation intensity;

[0059] Therefore, the final calibration model for:

[0060] ,

[0061] in is the spectral transmittance of the specified band, The dual-band measurement system responds to the target radiation intensity by only considering the filter spectral transmittance. 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:

[0062] ,

[0063] Then the equivalent radiation intensity of the blackbody radiation intensity after adding the filter is obtained after the radiation intensity correction coefficient is corrected. :

[0064] ,

[0065] Substituting into:

[0066] ,

[0067] The equivalent radiation intensity after the above correction 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.

[0068] Beneficial effects

[0069] The present invention constructs a nonlinear mapping relationship between infrared radiation intensity and detector output voltage, combines the Kalman filtering algorithm and the medium-wave infrared radiation coefficient correction algorithm, and realizes dynamic denoising and adaptive correction of infrared radiation signals, greatly improving measurement accuracy and anti-interference ability, and ensuring the accuracy and stability of the output results.

[0070] The system integrates a switchable medium-wave infrared bandpass filter and a dedicated optical design (Cook three-piece structure), with high-efficiency selective transmission capabilities in the 3.5μm-4μm and 4.5μm-4.8μm bands. By introducing a dual-band correction factor, it achieves high-precision measurement and calibration of infrared radiation intensity within a specified band, adapting to the needs of infrared radiation source detection in complex scenarios.

[0071] The infrared radiation intensity measurement system constructed by the present invention integrates a high-gain, low-noise signal acquisition circuit, a filtering and processing module, and an intelligent host computer platform, realizing full-process automation of radiation source control, signal acquisition and processing, radiation intensity calculation, and visualization. It has the advantages of high measurement efficiency, convenient operation, and strong repeatability, and can be widely used in factory inspection, performance evaluation, and on-site monitoring of infrared simulation source equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a flow chart of radiation calibration of the infrared detector of the present invention;

[0073] Figure 2It is a working flow chart of the infrared radiation intensity measurement method of the present invention;

[0074] Figure 3 is a beam envelope diagram during the simulation process of the medium-wave infrared optical system of the present invention;

[0075] Figure 4 is a point diagram during the simulation process of the medium-wave infrared optical system of the present invention;

[0076] Figure 5 It is the field of view analysis during the simulation process of the medium-wave infrared optical system of the present invention;

[0077] Figure 6 This is a flow chart of the filter gain amplifier circuit of the present invention;

[0078] Figure 7 This is a schematic diagram of the Kalman filter algorithm used in the present invention. DETAILED DESCRIPTION

[0079] The following is a clear and comprehensive description of the technical solution of the present invention with reference to the accompanying drawings. It can be seen that the description only shows some embodiments of the present invention, not all embodiments. All other embodiments derived by a person of ordinary skill in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. The technical solution of the present invention can be clearly demonstrated by combining the accompanying drawings.

[0080] The present invention will be further described below with reference to the accompanying drawings and examples.

[0081] See also Figure 1 , a schematic flow chart of a method for measuring radiation intensity of an infrared radiation simulation source system of the present invention.

[0082] First, the infrared radiation source to be measured is preheated to a thermally stable state to ensure the stability of its radiation output. The radiation energy is then focused through a medium-wave infrared optical system and irradiated onto the photosensitive surface of an infrared detector calibrated with a standard blackbody. The detector receives the radiation energy and converts it into an electrical signal. This signal is processed through signal conditioning modules such as transimpedance amplification and filter gain to produce a stable alternating voltage signal. The Kalman filter algorithm is used to dynamically denoise the signal. Combined with a medium-wave infrared radiation coefficient correction algorithm, a nonlinear mapping relationship between the output voltage and infrared radiation intensity is established. Finally, the host computer software's embedded algorithm module completes data inversion and calculation, outputting the numerical results of the radiation intensity of the target infrared radiation source within the specified band.

[0083] The HgCdTe infrared detector used in this invention is an infrared mercury cadmium telluride (MCT) detector. It is a semiconductor-based infrared detector primarily composed of a mercury cadmium telluride alloy. Its components typically include an active detection layer, electrodes, packaging, and a cooling system (such as a liquid nitrogen cooler). MCT detectors are characterized by their wide wavelength response range (approximately 1µm to 20µm), high sensitivity, and low noise performance. By adjusting the ratio of Hg to Cd, the wavelength to which the detector responds can be optimized. Furthermore, MCT detectors operate at low temperatures, significantly improving detection accuracy. Applications include thermal imaging, night vision equipment, gas detection, remote sensing, scientific research, military reconnaissance, and security technology. Their excellent sensitivity and responsiveness make them important for various infrared detection applications, making them the preferred choice.

[0084] The TIA (Transimpedance Amplifier) ​​used in this invention is an amplifier that converts current signals into voltage signals. Its basic structure consists of a photodetector (such as a photodiode or photomultiplier tube) at the input and an operational amplifier with negative feedback. The photodetector converts the light signal into a current signal, and 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 a TIA are high bandwidth, low noise, and high gain, enabling it to effectively amplify low-current signals while maintaining signal integrity. After careful consideration and analysis, Maxim Integrated's MAX4000 series TIA was selected.

[0085] See also Figure 2 , shows a flowchart of the workflow of a method for measuring radiation intensity of an infrared radiation simulation source system of the present invention.

[0086] As an example, the method includes the following specific steps:

[0087] Step 1: Radiation Calibration: First, the infrared detector is calibrated across all wavelengths using a standard blackbody to obtain the radiation intensity response relationship between the blackbody and the infrared detector. Secondary calibration is then performed in two mid-wave infrared bands using the simulated infrared radiation source to establish the response relationship between the infrared detector output signal and the infrared radiation intensity in both bands.

[0088] Step 2: Photoelectric conversion: The infrared radiation simulation source to be measured is preheated to a stable working state. After being focused by the medium-wave infrared optical system, the radiated energy is received by the calibrated infrared detector and converted into a corresponding electrical signal.

[0089] 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;

[0090] Step 4: Algorithm correction: Use the Kalman filter algorithm to perform dynamic signal optimization on the high signal-to-noise ratio signal obtained in step 3, and combine it with the medium-wave infrared radiation coefficient correction algorithm to compensate for nonlinear errors and establish a mapping relationship between detection voltage and radiation intensity;

[0091] Step 5: Data output: The infrared radiation intensity parameters calculated through the mapping relationship between the detection voltage and radiation intensity in step 4 are uploaded to the host computer software of the measurement system, and the measurement results are displayed and stored in real time to achieve visualization and analysis of high-precision measurement data.

[0092] First, a standard surface blackbody source is used to calibrate the infrared detector across the entire wavelength range to obtain the radiation intensity response relationship between the blackbody and the infrared detector. The specific method is as follows:

[0093] The standard surface source blackbody is used as a benchmark. First, the close-range extended source method is used to calibrate the full band. During calibration, the surface source blackbody is placed in front of the medium-wave infrared optical system, and an optical chopper is set between the medium-wave infrared optical system and the infrared detector to focus the infrared radiation from the surface source blackbody on the photosensitive surface of the infrared detector. By controlling the light source temperature of the surface source blackbody, a temperature of The radiation intensity response relationship between the surface source blackbody and the infrared detector is as follows:

[0094] ,

[0095] in, is an infinitesimal change in wavelength, is an infinitesimal area element on the detector, is the infrared radiation intensity of the surface blackbody, is the operating 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 medium-wave infrared optical system, is the gain of the circuit, The temperature is The radiation brightness of the surface black body is is the area of ​​the element on the detection surface;

[0096] Collect the voltage output by the surface source blackbody at different working temperatures, and calculate the corresponding radiation intensity using the above formula to calculate ;

[0097] A mid-infrared bandpass filter with a transmission range of 3550–4150nm and 4500–5000nm is added in front of the infrared detector. By switching between two different mid-infrared bandpass filters, the infrared radiation simulation source to be measured can be operated in the two mid-wave infrared bands of 3.5μm to 4μm and 4.5μm to 4.8μm.

[0098] Based on the measurement band, the temperature in the 3.5μm~4μm and 4.5μm~4.8μm bands is The radiation intensity response relationship between the surface source blackbody and the infrared detector is as follows:

[0099] ,

[0100] ,

[0101] and They represent the infrared radiation intensity of surface source blackbody in the 3.5μm~4μm and 4.5μm~4.8μm bands respectively. and are the spectral transmittance of the two bands respectively, and The above process realizes the radiation calibration of infrared detectors in the full band and specific bands, and collects the spectral response rate of blackbody output at different operating temperatures to improve measurement consistency and accuracy.

[0102] Furthermore, the specific method for photoelectric conversion in step 2 involves preheating the infrared radiation source to achieve thermal stability. The host computer software then sets the radiation intensity output value, precisely controlling the temperature within a specified range to ensure radiation intensity stability. After being focused by the medium-wave infrared optical system, the infrared radiation is converted into an alternating signal by an optical chopper. The radiation energy is then received by a calibrated detector for subsequent signal processing.

[0103] 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:

[0104] a) Optical system: including reflectors (primary and secondary), filters, etc.

[0105] b) Infrared radiation source control system: including radiator (silicon molybdenum rod), driving power supply, temperature control system and heat dissipation system;

[0106] c) Radiation intensity control system: including blinds and transmission system, motor drive and control computer;

[0107] d) Auxiliary components: including outer shell, support and connecting structural parts.

[0108] The medium-wave infrared optical system in this embodiment adopts a Cooke three-piece infrared optical imaging structure, such as Figure 5 As shown, this structure achieves efficient focusing and detection of infrared radiation energy. It consists of two biconvex lenses and one biconcave lens, arranged in the following order: biconvex lens L1, biconcave lens L2, and biconvex lens L3. Both biconvex lenses are standard United Optoelectronics product model 110148, each with an effective focal length of 50 mm; the middle biconcave lens is model 130076, with a focal length of -30 mm. Together, these three lenses form a composite lens system with a reduced focal length of approximately 71 mm and a total length of approximately 120 mm. This structure exhibits excellent aberration correction within the mid-wave infrared (MWIR) band (3.5–5 μm), enabling the majority of radiation energy to be focused onto a 1 mm × 1 mm HgCdTe infrared detector while effectively controlling the field of view within a 0°–0.4° range. This improves measurement accuracy and suppresses stray background light interference. This MWIR optical system boasts a simple structure and easy fabrication and assembly. It offers advantages such as rational design, stable performance, and manageable cost, making it suitable for the efficient focusing and transmission of MWIR radiation signals. In addition, an optical chopper needs to be set up to convert the continuous signal output by the infrared radiation source into an alternating signal.

[0109] Because the infrared radiation simulation source system to be measured operates in two medium-wave infrared bands, 3.5μm~4μm and 4.5μm~4.8μm, mid- and far-infrared bandpass filters with matching bands are selected during the system calibration process. Their transmittance ranges are 3550–4150nm and 4500–5000nm, and their average transmittance is greater than 90%, effectively ensuring the band selectivity and energy transmission efficiency during the calibration process.

[0110] Infrared optical system simulation experiment:

[0111] This paper uses optical simulation to verify the focusing performance and anti-interference capabilities of the designed mid-wave infrared optical system. In the simulation experiment, typical mid-wave infrared wavelengths of 3500nm, 3750nm, 4000nm, 4500nm, 4650nm, and 4800nm ​​were selected for beam envelope analysis (diffraction performance) and spot diagram analysis (geometric imaging performance):

[0112] from Figure 3 According to the beam envelope analysis results, nearly 90% of the energy of the six wavelengths on the focal plane is concentrated in a circular area with a diameter of about 0.7 mm, ensuring that the infrared radiation energy is effectively focused on the photosensitive surface of the HgCdTe infrared detector, thereby improving the receiving efficiency.

[0113] The results of the spot diagram analysis are as follows Figure 4It shows that the average RMS spot diameter of the focus corresponding to the six wavelengths is 0.678mm, which is much smaller than the target surface size of the detector (1mm×1mm), meeting the design requirements and verifying the good imaging consistency of the system.

[0114] In this embodiment, the field of view angle simulation analysis is performed. Figure 5 As shown in the figure, with a detector focal length (EFL) of 71.0347mm, the maximum receiving field of view is approximately 0.4°. At this extreme field of view, the green light beam just hits the edge of the detector. However, when the field of view angle reaches 5° (blue light), the beam deviates from the detector. This smaller receiving field of view effectively suppresses interference from background stray light, significantly improving the accuracy of infrared radiation measurements.

[0115] 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 signal stability and signal-to-noise ratio, and facilitate subsequent filtering, gain amplification and synchronous detection processing.

[0116] The signal processing module in step 3 of this embodiment includes a two-stage circuit structure, specifically as follows: Figure 6 As shown in the figure, 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 amplifier circuit, including a prefilter and an amplifier 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, GR noise and 1 / f noise, and input the alternating voltage signal into an analog-to-digital converter (A / D) after optimizing the signal quality. The STM32 controller collects the peak-to-peak value of the voltage and converts it into a digital signal for subsequent processing.

[0117] The transimpedance amplifier (TIA) in the first-stage amplification circuit adopts a low-noise operational amplifier design with high input impedance and wide bandwidth characteristics. It can achieve stable conversion and linear amplification of pA-level current signals, and has high-gain and low-drift signal conversion capabilities, making it suitable for front-end amplification processing of weak infrared signals.

[0118] The second-stage pre-filter gain amplifier circuit adopts an active filter structure, combined with a low-pass or band-pass filter network to suppress high-frequency noise and perform targeted enhancement of signals in different frequency bands through an adjustable gain operational amplifier circuit, thereby improving the signal-to-noise ratio and dynamic range of the signal.

[0119] Furthermore, in order to improve the measurement stability of the system in complex environments, the Kalman filter algorithm based on STM32 is used in step 4 to filter and correct the output signal of the infrared detector. Considering that the response of the HgCdTe detector fluctuates and the system is significantly affected by factors such as temperature and stray light when it is outdoors, it is difficult for traditional filtering methods to effectively suppress noise. The Kalman filter estimates the current radiation intensity and noise state in real time by establishing state equations and observation equations, and dynamically updates the filter gain to achieve high-precision correction of the measured voltage, thereby improving the system's anti-interference ability and measurement accuracy in non-steady-state environments. The specific principles are as follows: Figure 7 .

[0120] Assume that the measured voltage and radiation intensity satisfy the function , the circuit noise satisfies the function , state quantity is the infrared radiation intensity, state quantity is the noise voltage, and the observed quantity is the output voltage , then there is a state-observation model for Kalman filter estimation:

[0121] ,

[0122] in, Indicates the A priori estimate of the time, Indicates the A priori estimate of the time, Represents the state transfer matrix; then for the prior estimate covariance calculation, first set , ,in, Indicates the The prior state covariance matrix at time t reflects the uncertainty of the system state estimation before this observation; Indicates the The prior state covariance matrix at the moment reflects the estimated error distribution after the last observation correction. Respectively represent the variance estimation of infrared radiation intensity and noise voltage at the current moment, Represent the covariance between two state quantities, and satisfy ; Respectively represent the variance estimation of infrared radiation intensity and noise voltage at the previous moment, denote the covariance between two state variables respectively;

[0123] Construct the covariance matrix of the two state quantities. It is known that the two state quantities are independent of each other, so we have:

[0124] ,

[0125] in, Represents the system process noise covariance matrix, which is used to quantify the uncertainty in the state transition process;

[0126] The covariance formula of the prior estimate is:

[0127] ,

[0128] in, represents the process noise variance of the radiation intensity, The process noise variance represents the circuit noise;

[0129] Expanding the above formula gives the specific expression of the covariance matrix:

[0130] ,

[0131] Due to the set state Including voltage measurement noise, the system measurement equation is established as , the measurement matrix , and the Kalman gain is obtained:

[0132] ,

[0133] in, 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 measurement noise. Indicates the The observed value at the moment, that is, the actual measured voltage, Indicates the response coefficient of the infrared detector output voltage and radiation intensity, is the radiated noise;

[0134] Next, calculate the current best estimate:

[0135] ,

[0136] in, represents the prior estimate 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, ;

[0137] Finally update the covariance matrix:

[0138] ,

[0139] Represents the variance of the radiation intensity and itself in the updated covariance matrix; represents the covariance between the radiation intensity and the circuit noise after the update; represents the covariance between circuit noise and radiation intensity after updating; represents the variance of the circuit noise and itself in the updated covariance matrix;

[0140] After the above process, one filtering cycle is completed.

[0141] Furthermore, in step 4, the medium-wave infrared radiation coefficient correction algorithm is combined to compensate for nonlinear errors and establish a mapping relationship between detection voltage and radiation intensity. The specific method is as follows:

[0142] The radiation intensity response of a specific band after passing through the medium-wave infrared optical system and circuit is shown in formula (2). In the actual radiation calibration process, the spectral response of the infrared detector is not given for a narrow band, but shows a certain change in a wider band, making To simplify the calculation and improve the feasibility of measurement, 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. ,Will It is regarded as a constant for calculation, thereby correcting the signal measured by the system and improving the accuracy of radiation intensity inversion;

[0143] Radiation intensity correction factor for:

[0144] ,

[0145] Therefore, the infrared radiation intensity after the medium-wave infrared radiation coefficient is corrected and The calculation formula is:

[0146] ,

[0147] ,

[0148] for The linear calibration model is used to solve the problem. The spectral response rate 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. , calculate the target radiation intensity;

[0149] Therefore, the final calibration model for:

[0150] ,

[0151] in is the spectral transmittance of the specified band, The dual-band measurement system responds to the target radiation intensity by only considering the filter spectral transmittance. 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:

[0152] ,

[0153] Then the equivalent radiation intensity of the blackbody radiation intensity after adding the filter is obtained after the radiation intensity correction coefficient is corrected. :

[0154] ,

[0155] Substituting into:

[0156] ,

[0157] The equivalent radiation intensity after the above correction 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 coefficient within each specified band can be determined, providing an accurate calibration basis for the system's subsequent radiation intensity measurement and inversion.

[0158] The method for measuring the radiation intensity of an infrared radiation simulation source system, the output result in step 5 refers to the alternating electrical signal after signal processing being introduced into the calculation module, the radiation intensity value of the target infrared radiation source within the set band is obtained through system calculation, and the result is output to the host computer software of the measurement system for display and recording, and finally the measurement data within the controllable threshold value of the radiation intensity fluctuation range is obtained.

[0159] The host computer software system has the functions of data reception, real-time display and historical record. It can perform curve fitting, fluctuation analysis and data storage on the output infrared radiation intensity data, and supports setting fluctuation thresholds to realize abnormal identification and prompts of measurement results, further improving the stability and reliability of system measurements.

[0160] Data is transmitted between the host computer software system and the measuring device via a serial communication interface, which includes but is not limited to USB, RS-232, CAN or Ethernet protocols to ensure the real-time and stability of data interaction.

[0161] In the present invention, features described and / or exemplified for one embodiment may also be used in the same or similar manner in one or more other embodiments, and may be combined with or replace 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 comprises the following steps: Step 1: Radiation Calibration: First, the infrared detector is calibrated across all wavelengths using a standard blackbody to obtain the radiation intensity response relationship between the blackbody and the infrared detector. Secondary calibration is then performed in two mid-wave infrared bands using the simulated infrared radiation source to establish the response relationship between the infrared detector output signal and the infrared radiation intensity in both bands. Step 2: Photoelectric conversion: The infrared radiation simulation source to be measured is preheated to a stable working state. After being focused by the medium-wave infrared optical system, the radiated energy is received by the calibrated infrared detector and converted 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 perform dynamic signal optimization on the high signal-to-noise ratio signal obtained in step 3, and combine it with the medium-wave infrared radiation coefficient correction algorithm to compensate for nonlinear errors and establish a mapping relationship between detection voltage and radiation intensity; Step 5: Data output: The infrared radiation intensity parameters calculated by the mapping relationship between the detection voltage and radiation intensity in step 4 are uploaded to the host computer software of the measurement system, and the measurement results are displayed and stored in real time; In step 4, the medium-wave infrared radiation coefficient correction algorithm is combined to compensate for nonlinear errors and establish a mapping relationship between detection voltage and radiation intensity. The specific method is as follows: When the measurement system estimates the target radiation intensity within each given measurement band, the detector spectral response is regarded as a constant. Based on this, the radiation intensity correction coefficient C is defined. det , R det (λ) is regarded as a constant for calculation, thereby correcting the signal measured by the system and improving the accuracy of radiation intensity inversion; Radiation intensity correction factor C det for: C det =R det (l)·t opt (11) Therefore, the infrared radiation intensity after the medium-wave infrared radiation coefficient is corrected and The calculation formula is: For C det To solve the problem, a linear calibration model is used. The spectral response rate of the detector is not considered for the time being. Only the spectral transmittance of the filter and the gain K of the circuit are considered to calculate the target radiation intensity. Therefore, the final calibration model V * for: where τ fil is the spectral transmittance of the specified band; G fil The dual-band measurement system responds to the target radiation intensity considering only the filter spectral transmittance. 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 G fil , through the following formula: Then we can get the equivalent radiation intensity I of the blackbody radiation intensity after adding the filter after the radiation intensity correction coefficient is corrected. BN (λ,T b ): Substituting into: Where dλ is an infinitesimal change in wavelength, ds is an infinitesimal area element on the detector, and V b is the infrared radiation intensity of the surface blackbody, T b is the operating temperature of the surface source blackbody, G A is the response rate of the infrared detector to the incident radiation intensity, △λ is the response range of the infrared detector, R det is the spectral response of the infrared detector, τ opt is the transmittance of the medium-wave infrared optical system, K is the gain of the circuit, and L b (λ,T b ) is the temperature T b When the radiation brightness of the surface source black body is , △s is the surface element area on the detection surface; V b1 and V b2 Respectively represent the surface source blackbody infrared radiation intensity in the 3.5μm~4μm and 4.5μm~4.8μm bands, τ fil1 and τ fil2 are the spectral transmittance of the two bands respectively, and are the response rates of the detector to the target radiation intensity in the two bands respectively; The equivalent radiation intensity V BN 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.

2. The method for measuring the radiation intensity of an infrared radiation simulation source system according to claim 1, wherein: The infrared detector is a HgCdTe infrared detector; the medium-wave infrared optical system adopts a Cooke three-piece structure design, including two biconvex mirrors on both sides and a biconcave mirror located between the two biconvex mirrors; wherein, the effective focal length of the two biconvex mirrors is 50 mm; the focal length of the middle biconcave mirror is -30 mm; the three lenses together form a composite lens system with a reduced focal length of 71 mm, and the total length of the system is 120 mm; The infrared radiation simulation source to be measured operates in two medium-wave infrared bands of 3.5μm to 4μm and 4.5μm to 4.8μm. During the radiation calibration process, mid- and far-infrared bandpass 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, a standard surface blackbody source is used to calibrate the infrared detector across the entire wavelength range 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 benchmark, the close-range extended source method is first used to calibrate the full band. During calibration, the surface source blackbody is placed in front of the medium-wave infrared optical system, and an optical chopper is set between the medium-wave infrared optical system and the infrared detector to focus the infrared radiation from the surface source blackbody on the photosensitive surface of the infrared detector. By controlling the light source temperature of the surface source blackbody, a temperature of T is established. b The radiation intensity response relationship between the surface source blackbody and the infrared detector is as follows: Collect the voltage output by the surface source blackbody at different working temperatures, and calculate the corresponding radiation intensity through the above formula to calculate G A ; Then, a secondary calibration is performed in two medium-wave infrared bands of the infrared radiation simulation source to be measured to establish the response relationship between the infrared detector output signal and the infrared radiation intensity in the dual bands. The specific method is as follows: A mid-infrared bandpass filter with a transmission range of 3550–4150nm and 4500–5000nm is added in front of the infrared detector. By switching between two different mid-infrared bandpass filters, the infrared radiation simulation source to be measured can be operated in the two mid-wave infrared bands of 3.5μm to 4μm and 4.5μm to 4.8μm. Based on the measurement band, the temperature in the 3.5μm~4μm and 4.5μm~4.8μm bands is T b The radiation intensity response relationship between the surface source blackbody and the infrared detector is as follows: V b1 and V b2 They represent the infrared radiation intensity of surface source blackbody in the 3.5μm~4μm and 4.5μm~4.8μm bands respectively.

4. The method for measuring the radiation intensity of an infrared radiation simulation source system according to claim 1, wherein: The signal processing module used in the signal preprocessing in step 3 includes a two-stage circuit structure, wherein 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 realize preliminary gain amplification; the second stage is a filter gain amplifier circuit, including a prefilter and an amplifier 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, GR noise and 1 / f noise, and input the alternating voltage signal into the analog-to-digital converter A / D after optimizing the signal quality, 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, wherein: In step 4, the Kalman filter algorithm is used to perform dynamic signal optimization on 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 f(V), and the circuit noise satisfies the function The state quantity x is the infrared radiation intensity, the state quantity w is the noise voltage, and the observation quantity is the output voltage V. Then there is a state-observation model for Kalman filter estimation: in, represents the prior estimate at the kth moment, represents the prior estimate at the k-1th moment, represents the state transition matrix; Then for the prior estimate covariance calculation, first assume Among them, P k represents the prior state covariance matrix at the kth moment, reflecting the uncertainty of the system state estimation before this observation; P k-1 represents the prior state covariance matrix at the k-1th moment, reflecting the estimated error distribution after the last observation correction, a k d k They represent the variance estimation of infrared radiation intensity and noise voltage at the current moment, b k 、c k Represent the covariance between two state quantities, and satisfy b k =c k ;a k-1 d k-1 They represent the variance estimation of infrared radiation intensity and noise voltage at the previous moment, b k-1 、c k-1 denote the covariance between two state variables respectively; Construct the covariance matrix of the two state quantities. It is known that the two state quantities are independent of each other, so we have: Where Q represents the system process noise covariance matrix, which is used to quantify the uncertainty in the state transition process; The covariance formula of the prior estimate is: Where D(x) represents the process noise variance of the radiation intensity, and D(w) 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 w contains voltage measurement noise, the system measurement equation is established as Measurement Matrix The Kalman gain is obtained from this: Among them, K0 represents the Kalman gain of the radiation intensity state, which is used to adjust the predicted radiation intensity; K1 represents the Kalman gain of the circuit noise state, which is used to estimate and suppress the error caused by the measurement noise, and Z k represents the observed value at the kth moment, that is, the actual measured voltage, f represents the response coefficient of the infrared detector output voltage and radiation intensity, and R is the radiation noise; Next, calculate the current best estimate: in, represents the prior estimate 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, K0; Finally update the covariance matrix: Represents the variance of the radiation intensity and itself in the updated covariance matrix; represents the covariance between the radiation intensity and the circuit noise after the update; represents the covariance between circuit noise and radiation intensity after updating; represents the variance of the circuit noise and itself in the updated covariance matrix; After the above process, one filtering cycle is completed.

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