A Design Method for Space-Based Infrared Point Target Detection and Integration Sensor

Abstract

The present invention provides a design method for a space-based infrared point target detection and inspection integrated sensor, including: determining a detection background characteristic analysis process for obtaining target body infrared radiation intensity data and motion characteristic data; according to the detection background characteristic analysis process, taking the sensor detection system as a point target imaging system, analyzing different detection backgrounds; calculating the minimum detection signal-to-clutter ratio SCR based on the discovery probability or false alarm rate requirements; constructing a model, calculating clutter fluctuations, and determining the range of comprehensive noise according to the minimum detection signal-to-clutter ratio value; determining the maximum value of spatial domain fluctuations, the sensor resolution; calculating the focal length, combining the target motion characteristics and the sensor resolution, and calculating the longest integration time of the sensor; calculating the camera noise equivalent radiation intensity, and clarifying the detection dynamic range according to the detection target characteristics; iteratively optimizing various parameters until the design requirements are met, and confirming the effectiveness of the detection and inspection integrated sensor.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing detection, and particularly to a design method for a space-based infrared point target detection and inspection integrated sensor. Background Art

[0002] Infrared imaging sensors have the advantages of strong penetration ability and all-weather operation, and are widely used in the fields of surveillance, detection, and early warning.

[0003] Space-based infrared point target detection and inspection sensors are one of the core technologies of infrared imaging sensors. The detection of infrared dim targets is a difficult problem, and the main difficulties are as follows: 1) The imaging distance is far, and the energy collection ability of the sensor is weak; 2) Space-based infrared detection is mainly for the detection of dim targets under complex terrestrial backgrounds, and there are phenomena such as low image signal-to-noise ratio or the target being submerged in background noise and unable to be detected.

[0004] At present, great progress has been made in the research on infrared point target detection, and it has been applied to the detection of targets in multiple aspects such as the spatial domain, time domain, and frequency domain. However, there is little research on the design of target detection sensors. Detection and inspection are separated from each other. Infrared point target sensors lack an integrated design of detection and inspection, and lack a comprehensive model of the influencing factors of the entire link from target detection to inspection, making it difficult to ensure the stability of the detection and inspection system. The effective detection and inspection of infrared dim targets is an urgent technical bottleneck to be solved. Summary of the Invention

[0005] In view of this, the present invention provides a design method for a space-based infrared point target detection and inspection integrated sensor, which can start from the application, comprehensively consider all link elements in sensor imaging, reasonably carry out the integrated design of sensor detection and inspection, and ensure that the sensor meets the detection and inspection requirements, including:

[0006] Step 1: Determine the detection background characteristic analysis process for obtaining the infrared radiation intensity data and motion characteristic data of the target body;

[0007] Step 2: According to the detection background characteristic analysis process, take the Earth background, limb background, and deep space background as the coverage ranges respectively, and take the sensor detection system as a point target imaging system to analyze different detection backgrounds;

[0008] Step 3: Calculate the minimum detection signal-to-clutter ratio SCR based on the discovery probability or false alarm rate requirements;

[0009] Step 4: Build a model, calculate the clutter fluctuation, and determine the range of comprehensive noise according to the minimum detection signal-to-clutter ratio SCR;

[0010] Step 5: Determine the maximum value of the spatial domain fluctuation. According to the current existing sensor resolution and the corresponding spatial domain fluctuation, the sensor resolution can be determined;

[0011] Step 6: Coarsely select spectral bands according to the target ontology, the detection background, the atmospheric transmittance, and the detector spectral band characteristics;

[0012] Step 7: Calculate the focal length, and combine the target motion characteristics and the sensor resolution to calculate the longest integration time of the sensor;

[0013] Step 8: Calculate the camera noise equivalent radiation intensity, and clarify the detection dynamic range according to the detection target characteristics;

[0014] Step 9: Repeat Steps 1 to 8, iteratively optimize various parameters until the design requirements are met, and confirm the effectiveness of the integrated detection sensor.

[0015] In particular, in Step 1, the detection background characteristic analysis process of obtaining the target body infrared radiation intensity data and the motion characteristic data through the inversion modeling method and the simulation calculation respectively is determined.

[0016] In particular, in Step 2, the earth background includes the cloud background, the land background, and the ocean background; the simulation calculation of the land background includes: establishing a three-dimensional earth geometry model, importing satellite cloud images and surface temperature distribution data into the geometry model; then setting the land cover types of each block, and setting the radiation material types of each block according to the land cover types to obtain a surface background sub-model; performing simulation calculation on the surface background sub-model to obtain the surface infrared radiation intensity data;

[0017] The simulation calculation of the land background includes establishing a three-dimensional earth geometry model, importing satellite cloud images and surface temperature distribution data into the geometry model; then setting the land cover types of each block, and setting the radiation material types of each block according to the land cover types to obtain a surface background sub-model; performing simulation calculation on the surface background sub-model to obtain the surface infrared radiation intensity data;

[0018] The simulation calculation of the ocean background includes: based on the average optical reflection characteristic data of the ocean, calculating the infrared radiation intensity data of the ocean at different observation angles by solving the radiation transfer equation of the single reflection of the ocean with the sun as the radiation source;

[0019] The cloud background, land background, and ocean background characteristics of obtaining the target body infrared radiation intensity data and the motion characteristic data through the inversion modeling method include: using the inversion modeling method, respectively using the land, ocean, and cloud as the background, and the infrared imaging measured data as the data source, and inversely calculating the respective background radiation intensity data. The above infrared imaging data is obtained by observing with a space-based infrared sensor.

[0020] In particular, in Step 3, based on the requirements of the detection probability or the false alarm rate, calculate the minimum detection signal-to-clutter ratio SCR, specifically including: calculating the minimum detection signal-to-clutter ratio SCR through the following detection signal-to-clutter ratio expression

[0021] Φ -1 (p d )-Φ -1 (P f )=SCR

[0022] Among them, P d is the target detection probability, P f is the over-threshold rate, and the expression of Φ(x) is:

[0023]

[0024] In particular, in step 4, calculating the spatial clutter fluctuation includes:

[0025] Among them

[0026] Among them, I i is the radiation intensity at the position of the i-th pixel of the detected background image; σ aera is the spatial clutter fluctuation radiation intensity in the background image with a 11×11 window centered on the i-th pixel;

[0027] Statistical probability histogram of σ aera , and the clutter fluctuation at 95% probability is the σ clutter fluctuation; according to the expression of the relationship between the signal-to-clutter ratio and the sensor,

[0028]

[0029] the range of the comprehensive noise represented by the denominator in the formula can be determined; among them, the numerator is the target energy collected by the sensor, the denominator is the comprehensive noise after time-domain detection, V is the background motion speed, f is the camera frame rate, r is the camera spatial resolution, and σ registration accuracy is the registration accuracy of two frames of images; in the above formula, σ registration accuracy, v, f, I t , τ t , η, ε are known parameters;

[0030] According to the minimum detection signal-to-clutter ratio SCR, through the above formula, the range of the comprehensive noise can be determined.

[0031] In particular, in step 5, the background spatial fluctuation noise is inversely proportional to the square of the sensor resolution. First, assume that σ camera noise = 0, and combine the formula for calculating the minimum detection signal-to-clutter ratio SCR in step 3 to determine the maximum value of the spatial fluctuation σ spatial fluctuation_max; according to the current existing sensor resolution and the corresponding spatial fluctuation, combined with the calculation formula of the sensor ground resolution GSD,

[0032]

[0033] The ground resolution GSD of the sensor can be determined, where GSD_1 is the verified ground resolution of the sensor and σ airspace fluctuation_1 is the verified airspace clutter value.

[0034] Specifically, in step 6, according to the characteristics of space-based infrared point targets, backgrounds, atmospheric transmittance, and detector spectral bands, the specific principles for roughly selecting spectral bands include: according to the target characteristics, the spectral band contains the response peak of the target, the target radiation intensity within the spectral band, the contrast between the target and the background within the spectral band, the atmospheric transmittance within the spectral band, and the spectral band should be selected in combination with the detector response and the detector's response to the spectral band.

[0035] Specifically, in step 7, according to

[0036]

[0037] Calculate the focal length, where f is the focal length, H is the orbital altitude, and d is the detector pixel pitch; combine the target motion characteristics and the sensor resolution to calculate the maximum integration time of the sensor

[0038]

[0039] where V t is the average target motion speed, and T int_max is the maximum integration time of the sensor.

[0040] Specifically, in step 8, the camera noise equivalent radiation intensity is related to its own optomechanical noise, readout circuit noise, dark current noise, processing circuit noise, detection distance, aperture, and integration time; according to different aperture and optomechanical noise controls, different camera noise equivalent radiation intensities NEI can be calculated,

[0041]

[0042] where N back is the background noise, N read is the readout circuit noise, N video is the processing circuit noise, N dark is the dark current noise, R is the detection distance, D is the optical aperture, T int is the integration time, τ opt is the optical efficiency, η is the quantum efficiency, h is Planck's constant, c is the speed of light, and λ is the wavelength.

[0043] Specifically, in step 9, confirming the effectiveness of the integrated detection sensor includes: based on the minimum detection signal-to-clutter ratio, substituting the designed sensor indicators into the comprehensive model,

[0044]

[0045] Calculating the detectable target radiation intensity can confirm the effectiveness of the integrated detection and inspection sensor.

[0046] Beneficial effects:

[0047] 1. Starting from the detection of the signal-to-clutter ratio model for detection, through this design method, a high detection probability and a low false alarm rate of the target can be achieved, ensuring the stability of the system;

[0048] 2. The design of the space-based infrared point target detection sensor considers the influencing factors in the entire process from detection to inspection, including many factors such as target characteristics, background characteristics, atmospheric transmission, clutter characteristics, and detection sensitivity, which conforms to the real detection and inspection scenario;

[0049] 3. Through the method of the present invention, the relevant key indicators of the integrated detection and inspection sensor can be calculated and determined, including: focal length, detection sensitivity, aperture, resolution, etc., which have good engineering applicability;

[0050] 4. In the present invention, the detection background characteristic analysis process of obtaining the infrared radiation intensity data and motion characteristic data of the target body through the inversion modeling method and simulation calculation respectively ensures the accuracy of the analysis process;

[0051] 5. In the present invention, the designed sensor indicators are substituted into the comprehensive model to calculate the detectable target radiation intensity, that is, the effectiveness of the integrated detection and inspection sensor, which verifies the reliability of the sensor design indicators in many aspects;

[0052] 6. According to the characteristics of space-based infrared point targets, backgrounds, atmospheric transmittance, and detector spectral bands in the present invention, the spectral band is roughly selected, and then the various indicators of the sensor are calculated, ensuring the accuracy and better applicability of the indicators. Description of the drawings

[0053] Figure 1 It is a schematic diagram of the design method of the space-based infrared point target integrated detection and inspection sensor of the present invention;

[0054] Figure 2 It is a schematic diagram of the relationship between the detection probability / false alarm rate and the detection signal-to-clutter ratio in the present invention;

[0055] Figure 3 It is a schematic diagram of the background radiation intensity calculation in the present invention;

[0056] Figure 4 It is a schematic diagram of the target characteristic analysis in the present invention;

[0057] Figure 5 It is a schematic diagram of the transmittance curve of different altitude spectral bands in the present invention;

[0058] Figure 6 It is a schematic diagram of the detection and inspection ability prediction process in the present invention. Detailed implementation manners

[0059] The present invention will be described in detail below with reference to the accompanying drawings and by way of examples.

[0060] The present invention provides a design method for a space-based infrared point target detection integrated sensor, as Figure 1 shown, including:

[0061] Step 1: Determine the detection background characteristic analysis process for obtaining the infrared radiation intensity data and motion characteristic data of the target body; the present invention analyzes the target characteristics in two different ways:

[0062] Firstly, the present invention adopts the inversion modeling method. Using the measured infrared imaging data of the detected target as the data source, the target radiation intensity data and motion characteristic data are inversely calculated. The above infrared imaging data is obtained by observing with a space-based infrared sensor.

[0063] Secondly, the present invention obtains the infrared radiation intensity data and motion characteristic data of the target body by simulation calculation. The specific simulation calculation process is as follows: First, according to the shape of the target body, the characteristic parameters of the body material, and the aerodynamic characteristic parameters of the body, a body simulation model is built. Then, the target infrared radiation intensity data I t and motion characteristic data V t .

[0064] Step 2: Detection background characteristic analysis process

[0065] The sensor detection system is a point target imaging system, and the coverage range includes the earth background, the limb background, and the deep space background; among them, the earth background includes the cloud background, the ocean background, and the land background. The sensor has a wide coverage range, and during the target detection process, the sensor will detect multiple backgrounds simultaneously. The present invention divides the detection background into: clouds, ocean, and land. Analyze different detection backgrounds, as Figure 2 shown.

[0066] Land background

[0067] Build a three-dimensional earth geometric model, and import satellite cloud maps and surface temperature distribution data into the geometric model; then set the land cover types of each block, and set the radiation material types of each block according to the land cover types to obtain a surface background sub-model; perform simulation calculation on the surface background sub-model to obtain the surface infrared radiation intensity data.

[0068] Adopt the inversion modeling method, and use the measured infrared imaging data of the land background as the data source to inversely calculate the land background radiation intensity data. The above infrared imaging data is obtained by observing with a space-based infrared sensor.

[0069] Cloud background

[0070] Based on the data of the average optical scattering characteristics of clouds, by solving the radiative transfer equation of single scattering or multiple scattering in the cloud body with the sun as the radiation source, the infrared radiation intensity data of the cloud at different observation angles are calculated.

[0071] Using the inversion modeling method, with the measured data of cloud background infrared imaging as the data source, the cloud background radiation intensity data are inversely calculated. The above infrared imaging data are obtained by observing with a space-based infrared sensor.

[0072] Ocean background

[0073] Based on the data of the average optical reflection characteristics of the ocean, by solving the radiative transfer equation of single reflection of the ocean with the sun as the radiation source, the infrared radiation intensity data of the ocean at different observation angles are calculated.

[0074] Using the inversion modeling method, with the measured data of ocean background infrared imaging as the data source, the ocean background radiation intensity data are inversely calculated. The above infrared imaging data are obtained by observing with a space-based infrared sensor.

[0075] Step 3: Based on the discovery probability / false alarm rate requirements, detect the signal-to-clutter ratio expression and calculate the minimum detectable signal-to-clutter ratio SCR, as Figure 3 shown; where the signal-to-clutter ratio expression is as follows:

[0076] Φ -1 (p d ) - Φ -1 (P f ) = SCR

[0077] where P d is the target detection probability, P f is the threshold crossing rate, and the expression of Φ(x) is:

[0078]

[0079] Step 4: Build a detection model, including key influencing factors such as target radiation intensity, earth background clutter, optomechanical noise, and sensor platform, as Figure 4 shown, for example

[0080] Calculation of spatial clutter fluctuation

[0081]

[0082] where I i is the radiation intensity at the i-th pixel position of the detected background image.

[0083]

[0084] where σ aerais the radiation intensity of the spatial clutter fluctuation in the background image with the ith pixel as the center and a window size of 11×11.

[0085] Afterwards, statistics σ aera The probability histogram of the signal-to-noise ratio is converted to the clutter fluctuation under the 95% probability as σ clutter fluctuation. And since the relationship between the signal-to-noise ratio and the sensor is as follows:

[0086]

[0087] Among them, the numerator is the target energy collected by the sensor, the denominator is the comprehensive noise after time domain detection, V is the background (cloud) motion speed, f is the camera frame rate (unit: Hz), r is the camera spatial resolution, and σ registration accuracy is the registration accuracy of the two frames of images. t , τ t , η, ε are known parameters.

[0088] According to the minimum detection signal-to-noise ratio value, the comprehensive noise (denominator) range can be determined through the above formula.

[0089] Step 5: The comprehensive noise is divided into camera noise and background spatial fluctuation related noise. In the current sensor design, camera noise is a minor item, and background spatial fluctuation noise is the main noise item. Background spatial fluctuation noise is inversely proportional to the square of the sensor resolution. Assuming σ camera noise = 0, combined with the above formula, the maximum value of spatial fluctuation σ spatial fluctuation_max can be determined; according to the current existing sensor resolution and the corresponding spatial fluctuation, the sensor resolution can be determined by combining the above formula and the following formula:

[0090]

[0091] Among them, GSD is the ground resolution, GSD_1 is the verification ground resolution, and σ空城浮_1 is the verification airspace clutter value.

[0092] Step 6: According to the target, background, atmospheric transmittance and detector spectral characteristics, the spectrum is roughly selected, such as Figure 5 As shown in the figure, it is a schematic diagram of the transmittance curve according to different height spectrum bands. The specific principles are as follows:

[0093] According to the characteristics of the target, the spectrum segment contains the response peak of the target, and the target in the spectrum segment has a large radiation intensity;

[0094] There should be a high contrast between the target and the background in the spectral band; the spectral band should have a high atmospheric transmittance; the spectral band should be combined with the detector response, and the spectral band with high detector response should be selected.

[0095] Step 7: Calculate the focal length according to the following formula:

[0096]

[0097] Among them, f is the focal length, H is the orbital altitude, and d is the detector pixel pitch.

[0098] Combined with the target motion characteristics and sensor resolution, calculate the longest integration time of the sensor

[0099]

[0100] Among them, V t is the average velocity of target motion, and T int_max is the longest integration time of the sensor.

[0101] Calculation process of the camera noise equivalent radiation intensity

[0102] The camera noise equivalent radiation intensity is related to factors such as its own optomechanical noise, readout circuit noise, dark current noise, processing circuit noise, detection distance, aperture, and integration time. According to different aperture and optomechanical noise control, different noise equivalent radiation intensities can be calculated.

[0103]

[0104] Among them, N back is the background noise, N read is the readout circuit noise, N video is the processing circuit noise, N dark is the dark current noise, R is the detection distance, D is the optical aperture, T int is the integration time, τ opt is the optical efficiency, η is the quantum efficiency, h is the Planck constant, c is the speed of light, and λ is the wavelength.

[0105] Step 8: According to the characteristics of the detection target, use the strongest radiation intensity of the target as the upper limit of the dynamic range and the camera noise equivalent radiation intensity as the lower limit of the dynamic range to clarify the detection dynamic range;

[0106] Step 9: According to the integrated detection design process, substitute the design results into the detection signal-to-noise ratio calculation formula to verify whether the design meets the detection requirements. If not, repeat Steps 1 to 8 to iteratively optimize various parameters until satisfied.

[0107] Step 10: Based on the minimum detection signal-to-noise ratio, substitute the designed sensor indicators into the comprehensive model to calculate the detectable target radiation intensity, and then the effectiveness of the integrated detection sensor can be confirmed, as Figure 6 shown.

[0108]

[0109] In summary, the above are only the preferred embodiments of the present invention and are not intended to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0110] For those skilled in the art, it is obvious that the embodiments of the present invention are not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the embodiments of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the embodiments of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the embodiments of the present invention. Any reference signs in the claims should not be regarded as limiting the claimed rights. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units, modules or devices stated in the system, apparatus or terminal claims can also be implemented by the same unit, module or device through software or hardware. First, second, etc. are used to denote names and do not denote any particular order.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention and not to limit them. Although the embodiments of the present invention have been described in detail with reference to the above preferred embodiments, those of ordinary skill in the art should understand that modifications or equivalent replacements of the technical solutions of the embodiments of the present invention should not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A design method for a space-based infrared point target detection integrated sensor, characterized in that, Including: Step 1: Determine the detection background characteristic analysis process for obtaining the infrared radiation intensity data and motion characteristic data of the target body. Step 2: According to the detection background characteristic analysis process, take the Earth background, limb background, and deep space background as the coverage ranges respectively, and take the sensor detection system as a point target imaging system to analyze different detection backgrounds. Step 3: Calculate the minimum detection signal-to-clutter ratio SCR based on the discovery probability or false alarm rate requirements. Step 4: Build a model, calculate the clutter fluctuation, and determine the range of the comprehensive noise according to the minimum detection signal-to-clutter ratio SCR. Step 5: Determine the maximum value of the spatial domain fluctuation. According to the current existing sensor resolution and the corresponding spatial domain fluctuation, the sensor resolution can be determined. Step 6: Coarsely select the spectral band according to the target body, the detection background, the atmospheric transmittance, and the detector spectral band characteristics. Step 7: Calculate the focal length, and combine the target motion characteristics and the sensor resolution to calculate the longest integration time of the sensor. Step 8: Calculate the camera noise equivalent radiation intensity, and clarify the detection dynamic range according to the detection target characteristics. Step 9: Repeat Steps 1 to 8, iteratively optimize various parameters until the design requirements are met, and confirm the effectiveness of the integrated detection and inspection sensor.

2. The design method of the space-based infrared point target detection integrated sensor according to claim 1, characterized in that, In Step 1, determine the detection background characteristic analysis process for obtaining the infrared radiation intensity data and motion characteristic data of the target body respectively through the inversion modeling method and simulation calculation.

3. The design method of the space-based infrared point target integrated detection and inspection sensor as described in Claim 2, characterized in that In Step 2, the Earth background includes the cloud background, land background, and ocean background; the simulation calculation of the land background includes: establishing a three-dimensional Earth geometric model, importing satellite cloud maps and surface temperature distribution data into the geometric model; then setting the land cover types of each block, and setting the radiation material types of each block according to the land cover types to obtain the surface background sub-model; performing simulation calculation on the surface background sub-model to obtain the surface infrared radiation intensity data. The simulation calculation of the land background includes establishing a three-dimensional Earth geometric model, importing satellite cloud maps and surface temperature distribution data into the geometric model; then setting the land cover types of each block, and setting the radiation material types of each block according to the land cover types to obtain the surface background sub-model; performing simulation calculation on the surface background sub-model to obtain the surface infrared radiation intensity data. The simulation calculation of the ocean background includes: based on the average optical reflection characteristic data of the ocean, calculating the infrared radiation intensity data of the ocean at different observation angles by solving the radiation transfer equation of the single reflection of the ocean with the sun as the radiation source. The cloud background, land background, and ocean background characteristics of the infrared radiation intensity data and motion characteristic data of the target body obtained through the inversion modeling method include: adopting the inversion modeling method, taking the land, ocean, and cloud as the backgrounds respectively, and using the infrared imaging measured data as the data source to inversely calculate the respective background radiation intensity data. The above infrared imaging data is obtained by observing with a space-based infrared sensor.

4. The design method of the space-based infrared point target detection integrated sensor according to any one of claims 1-3, characterized in that, In step 3, based on the requirements of detection probability or false alarm rate, calculate the minimum detection signal-to-clutter ratio SCR, specifically including: calculating the minimum detection signal-to-clutter ratio SCR through the following detection signal-to-clutter ratio expression, Φ -1 (p d )-Φ -1 (P f )=SCR where P d is the target detection probability, and P f is the over-threshold rate. The expression of Φ(x) is:

5. The design method of the space-based infrared point target detection integrated sensor according to claim 4, characterized in that, In step 4, calculating the spatial clutter fluctuation includes: Among them where I i is the radiation intensity at the position of the i-th pixel of the detected background image; σ aera is the fluctuating radiation intensity of the clutter in the background image with a 11×11 window centered on the i-th pixel; Statistical σ aera The probability histogram of, and the clutter fluctuation under 95% probability is converted to be σ 杂波起伏 ; According to the expression of the relationship between the signal-to-clutter ratio and the sensor, The range of the comprehensive noise represented by the denominator in the formula can be determined; where the numerator is the target energy collected by the sensor, the denominator is the comprehensive noise after time-domain detection, f is the camera frame rate, r is the camera spatial resolution, and σ 配准精度 is the registration accuracy of two frames of images; in the above formula, σ 配准精度 , f, I t , τ t , η, and ε are known parameters; According to the minimum detection signal-to-clutter ratio SCR, through the above formula, the comprehensive noise range can be determined.

6. The design method of the space-based infrared point target detection integrated sensor according to claim 4 or 5, characterized in that In step 5, the background airspace fluctuation noise is inversely proportional to the square of the sensor resolution. First, assume that σ 相机噪声 = 0. Combining with the calculation formula of the minimum detection signal-to-clutter ratio SCR in step 3, the maximum value σ 空域起伏_max of the airspace fluctuation can be determined; according to the existing sensor resolution and the corresponding airspace fluctuation, and combining with the calculation formula of the sensor ground resolution GSD, The ground resolution GSD of the sensor can be determined, where GSD_1 is the verified ground resolution of the sensor, and σ 空域起伏_1 is the verified airspace clutter value.

7. The design method of the space-based infrared point target detection integrated sensor according to claim 6, characterized in that, In step 6, according to the characteristics of space-based infrared point targets, background, atmospheric transmittance, and detector spectral band characteristics, the specific principles for rough selection of spectral bands include: according to the target characteristics, the spectral band contains the response peak of the target, the target radiation intensity within the spectral band, the contrast between the target and the background within the spectral band, the atmospheric transmittance within the spectral band, and the spectral band should be selected in combination with the detector response and the detector's response to the spectral band.

8. The design method of the space-based infrared point target detection integrated sensor according to claim 7, characterized in that, In step 7, according to Calculate the focal length, where f is the focal length, H is the orbital altitude, and d is the detector pixel pitch; combine the target motion characteristics and sensor resolution to calculate the longest integration time of the sensor where V t is the target average motion speed, and T int_max is the maximum integration time of the sensor.

9. The design method of the space-based infrared point target detection integrated sensor according to claim 8, characterized in that, In step 8, the camera noise equivalent radiation intensity is related to its own optomechanical noise, readout circuit noise, dark current noise, processing circuit noise, detection distance, aperture, and integration time; according to different aperture and optomechanical noise control, different camera noise equivalent radiation intensities NEI can be calculated. where N back is the background noise, N read is the readout circuit noise, N video is the processing circuit noise, N dark is the dark current noise, R is the detection distance, D is the optical aperture, T int is the integration time, τ opt is the optical efficiency, η is the quantum efficiency, h is Planck's constant, c is the speed of light, and λ is the wavelength.

10. The space-based infrared point target detection and inspection integrated sensor design method according to claim 9, wherein, In step 9, confirming the effectiveness of the integrated detection sensor includes: based on the minimum detection signal-to-clutter ratio, substitute the designed sensor indicators into the comprehensive model. Calculate the detectable target radiation intensity, and then the effectiveness of the integrated detection sensor can be confirmed.

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