Optimization method and system for large-scale ghost imaging search and rescue system in foggy environment

Abstract

The present invention proposes a method and system for optimizing a large-scale ghost imaging search and rescue system in a foggy environment. The method selects a light source based on ZEMAX, and places a beam splitter behind the light source to form two light beams, obtains a detection light path and a reference light path, sequentially arranges a lens, an object to be detected, and a barrel detector on the detection light path, and arranges a layer of fog simulation on the lens object to be detected, and arranges a surface detector on the reference light path, and simulates through ZEMAX, obtains the light field and light intensity of the two detectors, calculates the Gaussian point spread function width according to the relationship between the light field and the light intensity, calculates the depth of field according to the lens parameters, and optimizes to minimize the Gaussian point spread function width and maximize the depth of field as optimization targets to obtain the optimal imaging parameters. The present invention uses Mie scattering combined with the concept of depth of field in traditional imaging to optimize the parameters of the ghost imaging system, so as to achieve large-scale imaging in a foggy environment.

Description

Technical Field

[0001] The present invention relates to the field of optical imaging, and in particular to an optimization method and system for a large-scale ghost imaging search and rescue system in a foggy environment. Background Art

[0002] As the key carrier of information transmission, images play an important role in the process of information transmission, and imaging technology is the core means of obtaining these images. Traditional imaging is a point-to-point imaging method that uses a spatially distributed array detector as an image sensor, which is commonly found in mobile phone cameras and digital cameras. Although traditional imaging technology is quite mature, it still has certain limitations in complex environments or certain special fields, such as long-distance imaging. In long-distance imaging, the resolution of traditional imaging technology is limited by the optical diffraction limit. At the same time, the detection ability of the imaging system is also easily affected by bad weather such as atmospheric turbulence and smoke. In practical applications, especially in long-distance search and rescue in foggy environments, water droplets in the fog will scatter light, causing the light to be deflected during propagation, thereby reducing visibility; it will also absorb part of the spectrum, reduce the intensity of light, affect the imaging quality, reduce the contrast in the imaging, and make the image of the object unclear, making it difficult to distinguish the shape and outline of the object. Low-contrast and blurred images cause the loss of detailed information, affect recognition capabilities, and bring great trouble to search and rescue.

[0003] In view of the problem that the detailed information of the images obtained in the search and rescue in the foggy environment is difficult to identify, the present invention herein applies the ghost imaging technology with strong anti-interference ability and large depth of field to the search and rescue in the foggy environment, and proposes a ghost imaging search and rescue method, system and device in the foggy environment. Ghost imaging originated from the 1990s, when physicists studied the non-local characteristics of quantum entangled photon pairs. In the ghost imaging process, the laser passes through the rotating frosted glass to obtain a pseudothermal light field with randomly distributed speckle characteristics; a 5-5 beam splitter with no polarization characteristics is placed behind the pseudothermal light field so that the two light beams it produces have the same distribution characteristics. One of the beams of light is irradiated on the object to be imaged, which is called the signal light path (also called the detection arm), and a bucket detector without spatial resolution is placed behind it to collect the total transmitted light intensity after irradiating the object (a number is obtained, recorded as D i , i=1, 2, ..., N). Another beam of light is directly irradiated on the CCD camera with spatial resolution, which is called the reference light path (also called the reference arm). The cross section of the light path is scanned to obtain its spatial distribution information (the collected information is a matrix, denoted as I i , i=1, 2, ..., N). By properly adjusting the position of the CCD detector in the reference light path, after N measurements, D 1 , D 2 ,…D n and I1 ,I 2 …I n By performing "correlation operations" on these N sets of data, the information of the object to be imaged can be restored.

[0004] Since the bucket detector in the test optical path has no spatial resolution capability, and the CCD detector with spatial resolution capability in the reference optical path does not directly contact the object to be imaged, neither single optical path can obtain the image information of the object to be imaged, but the image of the object can be clearly restored through correlation operations. These properties are difficult to explain by traditional optical imaging theory, so it is called "ghost" imaging. Ghost imaging is an unconventional imaging mechanism that breaks through the diffraction limit of traditional optical imaging methods and has the characteristics of non-locality. In addition, this non-point-to-point imaging method has the characteristics of strong anti-interference ability, large depth of field, high detection sensitivity, and wide imaging viewing angle. It has good application potential and development prospects in the fields of military, medical, aerospace, etc. Summary of the invention

[0005] In view of the above situation, the main purpose of the present invention is to propose a method and system for optimizing a large-scale ghost imaging search and rescue system in a foggy environment to solve the above technical problems.

[0006] The present invention proposes a method for optimizing a large-scale ghost imaging search and rescue system in a foggy environment, the method comprising the following steps:

[0007] Step 1: Based on ZEMAX, select a light source and place a beam splitter behind the light source to form two light beams with the same distribution characteristics, thereby obtaining a detection light path and a reference light path;

[0008] A lens, an object to be tested and a barrel detector are sequentially arranged on the detection optical path, a layer of fog simulation is arranged on the lens object to be tested, and a surface detector is arranged on the reference optical path;

[0009] Step 2: Perform simulation using ZEMAX, use a bucket detector to record the total light intensity passing through the object to be tested, and use a surface detector to record the spatial distribution of the light intensity at the corresponding position;

[0010] Calculate the light field distribution of the bucket detector position part and the surface detector position part;

[0011] Step 3, calculating the width of the Gaussian point spread function according to the relationship between the light field and the light intensity, and calculating the depth of field according to the lens parameters;

[0012] Step 4: Optimize by minimizing the width of the Gaussian point spread function and maximizing the depth of field. After the optimization is completed, the optimal imaging parameters are obtained.

[0013] The present invention also proposes a large-scale ghost imaging search and rescue system optimization system in a foggy environment, the system applies the large-scale ghost imaging search and rescue system optimization method in a foggy environment as described above, the system comprises:

[0014] 2f system building blocks for:

[0015] Based on ZEMAX, a light source is selected, and a beam splitter is placed after the light source to form two beams with the same distribution characteristics, thereby obtaining a detection optical path and a reference optical path;

[0016] A lens, an object to be tested and a barrel detector are sequentially arranged on the detection optical path, a layer of fog simulation is arranged on the lens object to be tested, and a surface detector is arranged on the reference optical path;

[0017] Simulation operation module, used for:

[0018] Through ZEMAX simulation, the total light intensity passing through the object to be tested is recorded using a bucket detector, and the spatial distribution of the light intensity at the corresponding position is recorded using a surface detector;

[0019] Calculate the light field distribution of the bucket detector position part and the surface detector position part;

[0020] Influencing factor calculation module, used for:

[0021] The width of the Gaussian point spread function is calculated based on the relationship between the light field and the light intensity, and the depth of field is calculated based on the lens parameters;

[0022] Imaging parameter optimization module for:

[0023] The optimization is performed with minimizing the width of the Gaussian point spread function and maximizing the depth of field as the optimization objectives. After the optimization is completed, the optimal imaging parameters are obtained.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The present invention builds a ghost imaging system through ZEMAX, utilizes Mie scattering to simulate a heavy fog environment, and combines the concept of depth of field in traditional imaging to optimize the parameters of the ghost imaging system from the perspective of geometric optics regarding the imaging quality of a non-focal plane of ghost imaging. The imaging quality of the ghost imaging system in a non-focal plane can be improved, and the imaging range can be expanded by increasing the depth of field. Large-scale imaging can be achieved in a heavy fog environment, and the imaging quality of targets with unknown positions in a heavy fog environment can be improved to capture detailed information of the object to be imaged. The present invention is more suitable for applications in real environments, and is of great significance in search and rescue in harsh environments.

[0026] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description or learned through embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of the optimization method of the large-scale ghost imaging search and rescue system in a foggy environment proposed by the present invention;

[0028] Figure 2 It is a framework diagram of the optimization system of the large-scale ghost imaging search and rescue system in a foggy environment proposed by the present invention;

[0029] Figure 3 A schematic diagram of a ghost imaging system for detecting a target object with unknown position in a simulated foggy environment according to the present invention;

[0030] Figure 4 Schematic diagram of random speckle generated by a circular Gaussian light source;

[0031] Figure 5 This is a comparison chart of the effects of fog simulants on imaging in a traditional lens 2f imaging system;

[0032] Figure 6 This is a comparison chart of the imaging performed by this system and the traditional imaging system in a foggy environment;

[0033] In the figure, 101 is a light source; 102 is a beam splitter; 103 is a surface detector; 104 is a lens; 105 is a fog simulant; 106 is an object to be measured; and 107 is a barrel detector. DETAILED DESCRIPTION

[0034] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0035] These and other aspects of the embodiments of the present invention will be apparent with reference to the following description and accompanying drawings. In these descriptions and accompanying drawings, some specific implementations of the embodiments of the present invention are specifically disclosed to represent some ways of implementing the principles of the embodiments of the present invention, but it should be understood that the scope of the embodiments of the present invention is not limited thereto.

[0036] See also Figure 1 This embodiment provides a method for optimizing a large-scale ghost imaging search and rescue system in a foggy environment, the method comprising the following steps:

[0037] Step 1: Based on ZEMAX, select a light source and place a beam splitter behind the light source to form two light beams with the same distribution characteristics, thereby obtaining a detection light path and a reference light path;

[0038] A lens, an object to be tested and a barrel detector are sequentially arranged on the detection optical path, a layer of fog simulation is arranged on the lens object to be tested, and a surface detector is arranged on the reference optical path;

[0039] In this embodiment, the light source is a circular light source that complies with a first-order Gaussian distribution, the beam splitter is a 5-5 beam splitter without polarization characteristics, and the fog simulation object is constructed based on a Mie scattering model.

[0040] Step 2: Perform simulation using ZEMAX, use a bucket detector to record the total light intensity passing through the object to be tested, and use a surface detector to record the spatial distribution of the light intensity at the corresponding position;

[0041] Calculate the light field distribution of the bucket detector position part and the surface detector position part;

[0042] In this embodiment, the process of calculating the light field distribution of the bucket detector position portion corresponds to the following relationship:

[0043] ;

[0044] in, Represents the light field distribution of the part where the pseudo-bucket detector is located, They represent the impulse response functions from light source to lens, lens to object, and object to bucket detector respectively. represents the transmission function of the target to be measured, represents the bucket detector plane, Indicates the plane where the light source is located. In the process of calculating the impulse response function from the light source to the lens and from the lens to the object, according to the linear optical transfer system , the impulse response function from the light source to the lens and from the lens to the object is expressed as the impulse response function from the light source through the lens to the object. The calculation process of the impulse response function from the light source through the lens to the object has the following relationship:

[0045] ;

[0046] in, Represents the impulse response function of the light source passing through the lens to the object, Respectively represent the distance from the light source to the lens, and the distance from the lens to the object, represents the wave number, represents the imaginary unit, represents the focal length of the lens, Indicates the plane where the object is located. Indicates the increment, and the increment calculation process has the following relationship:

[0047] .

[0048] The calculation process of the impulse response function from the object to the bucket detector has the following relationship:

[0049] ;

[0050] in, Indicates the distance from the object to the bucket detector.

[0051] In this embodiment, the process of calculating the light field distribution of the portion where the surface detector is located corresponds to the following relationship:

[0052] ;

[0053] in, Represents the light field distribution of the part where the surface detector is located, represents the surface detector plane, It represents the impulse response function from the light source to the surface detector. The calculation process of the impulse response function from the light source to the surface detector has the following relationship:

[0054] ;

[0055] in, represents the impulse response function from the light source to the surface detector, represents the surface detector, Indicates the distance from the light source to the surface detector.

[0056] Step 3, calculating the width of the Gaussian point spread function according to the relationship between the light field and the light intensity, and calculating the depth of field according to the lens parameters;

[0057] In this embodiment, calculating the width of the Gaussian point spread function according to the relationship between the light field and the light intensity specifically includes the following steps:

[0058] According to the total light intensity of the target object and the spatial distribution of the light intensity at the surface detector position, the intensity correlation calculation is performed to obtain the image information of the target to be measured. The corresponding process has the following relationship:

[0059] ;

[0060] in, Indicates the number of measurements, represents the mean operation, Represents the image information of the target to be measured, Represents the total light intensity of the target object, Represents the spatial distribution of light intensity at the surface detector position;

[0061] According to the light field and light intensity I=EE * The intensity correlation calculation process is expressed as the second-order correlation function of the light field. The corresponding process has the following relationship:

[0062] ;

[0063] ;

[0064] in, represents the second-order correlation function of the light field, represent the light intensity of the reference light path and the detection light path respectively, Respectively The complex conjugate of

[0065] Substitute the impulse response function from the object to the bucket detector and the impulse response function from the light source to the object through the lens into the second-order correlation function of the light field to derive the factors affecting the quality of ghost imaging in the non-focal plane and obtain the width of the Gaussian point spread function. The corresponding process has the following relationship:

[0066] ;

[0067] in, represents the width of the Gaussian point spread function, represents the wavelength, Indicates the size of the light source.

[0068] Step 4: Optimize by minimizing the width of the Gaussian point spread function and maximizing the depth of field. After the optimization is completed, the optimal imaging parameters are obtained.

[0069] when When the minimum value is obtained, , to achieve the best imaging conditions, at this time . It can be seen that, in the optimal imaging plane, the larger the light source size, the better the imaging quality. However, in the non-focal plane, the larger the light source size, the worse the imaging quality. Therefore, the present invention should make a comprehensive consideration of the system as a whole, and by selecting a suitable light source size, a relatively clear image of the target to be measured can be obtained in the focal plane and the non-focal plane within a certain range, thereby expanding the imaging range.

[0070] The calculation process of the depth of field formula in traditional imaging is as follows:

[0071] ;

[0072] in, Indicates depth of field, Indicates the object distance, represents the radius of the circle of confusion, Indicates the aperture number, , Indicates the diameter of the lens.

[0073] In addition, according to the depth of field formula in traditional imaging, the focal length and aperture size of the lens will affect the depth of field. By selecting appropriate lens focal length and aperture parameters, the depth of field of ghost imaging can also be expanded.

[0074] Through the above-mentioned influencing factors, the simulated ghost imaging system parameters are reasonably optimized to obtain the optimal parameters, and then the optimal parameters are applied to the ghost imaging search and rescue system in a foggy environment. By changing the ghost imaging system, a ghost imaging search and rescue system with a larger imaging range in a foggy environment can be built, which has the characteristics of a large depth of field, high sensitivity, and a wide imaging angle. Among them, the large depth of field allows for a large range of search and rescue in the axial range; the high sensitivity allows for clear imaging in extremely weak or dark light environments; the wide imaging angle allows for 360-degree search and rescue work without blind spots.

[0075] The present invention utilizes the above characteristics of ghost imaging technology to significantly increase the search and rescue range of unknown targets in foggy environments, thereby helping rescuers to carry out rescue work efficiently and quickly. Therefore, it can achieve search and rescue in more complex environments and larger areas, which is of great significance for applications in real environments such as rescue and search and rescue in severe weather.

[0076] Please refer to Figure 2 This embodiment also provides a large-scale ghost imaging search and rescue system optimization system in a foggy environment, wherein the system applies the large-scale ghost imaging search and rescue system optimization method in a foggy environment as described above, and the system includes:

[0077] 2f system building blocks for:

[0078] Based on ZEMAX, a light source is selected, and a beam splitter is placed after the light source to form two beams with the same distribution characteristics, thereby obtaining a detection optical path and a reference optical path;

[0079] A lens, an object to be tested and a barrel detector are sequentially arranged on the detection optical path, a layer of fog simulation is arranged on the lens object to be tested, and a surface detector is arranged on the reference optical path;

[0080] Simulation operation module, used for:

[0081] Through ZEMAX simulation, the total light intensity passing through the object to be tested is recorded using a bucket detector, and the spatial distribution of the light intensity at the corresponding position is recorded using a surface detector;

[0082] Calculate the light field distribution of the bucket detector position part and the surface detector position part;

[0083] Influencing factor calculation module, used for:

[0084] The width of the Gaussian point spread function is calculated based on the relationship between the light field and the light intensity, and the depth of field is calculated based on the lens parameters;

[0085] Imaging parameter optimization module for:

[0086] The optimization is performed with minimizing the width of the Gaussian point spread function and maximizing the depth of field as the optimization objectives. After the optimization is completed, the optimal imaging parameters are obtained.

[0087] In order to verify the effectiveness of the present invention, this example uses a transmissive object "E" as a target to be measured, and the specific steps are as follows:

[0088] 1. Choose the right light source

[0089] In ghost imaging, a light source 101 that can generate randomly distributed speckles is selected. Here, the present invention selects a circular light source whose light field distribution follows a first-order Gaussian distribution. A 5-5 beam splitter 102 without polarization characteristics is placed behind the light source to generate two beams with the same distribution characteristics, which are respectively directed to the reference light path and the detection light path. The circular Gaussian light source generates a light field distribution such as Figure 3 shown.

[0090] 2. Use Mie scattering to simulate foggy environment

[0091] A rectangular body, i.e., fog simulant 105, is set behind the light source. The x, y, and z half-widths of the rectangular body are 20 mm, 20 mm, and 5 mm, and volume scattering is set for it. Mie scattering theory can accurately describe the propagation characteristics of light in a foggy environment, including light attenuation, scattering, polarization, etc. Compared with the measured data, the Mie scattering model can obtain results that are very close to the actual situation, so the volume scattering model of the present invention selects Mie scattering. By setting the parameters such as the refractive index, volume, and density of the particles, a foggy environment is simulated, and the effect in the 2f imaging system is as follows. Figure 4 shown.

[0092] 3. Build reference optical path and detection optical path

[0093] Estimate the position range of the target to be measured, and select the appropriate light source size, lens 104 focal length, and aperture size to build the optical path. The approximate position of the detection target in this example is between 480mm and 520mm. The light source size used is 1*1, the lens focal length is 100mm, and the aperture size is 10mm. The detector of the reference optical path selects a barrel detector 107 with a pixel count of 100*100 for data collection. The detector of the detection optical path selects a surface detector 103 with no spatial resolution capability and only the function of recording total light intensity for data collection.

[0094] 4. Setting of the target to be tested

[0095] This example uses a transmissive object "E" and uses the "Setnscposition" function of the ZEMAX macro language to set the position of the transmissive object "E" to 480mm, 490mm, 500mm, 510mm, and 520mm, respectively, to simulate an object to be tested with uncertain position.

[0096] 5. Get detector data

[0097] Use the "NSDD" operand to clear the data on the surface detector and the bucket detector, ensuring that the data collected by the detector each time is independent. Use "NSTR" to perform ray tracing, and use "output" and "print" to read and save the data of the two detectors respectively. Use the loop function to repeat this step 10,000 times.

[0098] 6. Restore the image information of the target to be measured through association operation, that is:

[0099] ;

[0100] The imaging information of the object to be measured can be obtained through the correlation operation of the above formula.

[0101] from Figure 5 It can be seen from the figure that the imaging effect of the conventional lens 2f imaging system is poor in the presence of a heavy fog simulant. In order to verify the advantages of the present invention over the conventional lens 2f imaging system in terms of imaging, Figure 6 As shown, Figure 6 The figure shows the comparison of random target imaging five times between the present invention and the conventional imaging system in a foggy environment. From top to bottom, the imaging comparisons between the conventional imaging system and the imaging system of the present invention at 480mm, 490mm, 500mm, 510mm, and 520mm are shown.

[0102] pass Figure 6 It can be seen that the present invention has great advantages over traditional imaging in terms of imaging clarity and range. The present invention combines Mie scattering with the concept of depth of field in traditional imaging, and simulates the propagation characteristics of light such as attenuation, scattering, and polarization in a foggy environment through Mie scattering, which is more in line with the actual situation. The ghost imaging quality of target objects with unknown positions in a foggy environment is improved by expanding the depth of field, and ghost imaging of a large range in a complex environment is achieved. This technology is feasible from principle to experiment, and relevant verification has been carried out in the laboratory. In summary, it can be seen that the present invention can significantly improve the search and rescue range and imaging quality of unknown targets in a foggy environment, thereby helping rescue personnel to carry out rescue work efficiently and quickly. The realization of search and rescue in more complex environments and larger ranges is of great significance for applications in real environments such as rescue and search and rescue in severe weather.

[0103] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0104] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0105] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for optimizing a large-scale ghost imaging search and rescue system in a foggy environment, characterized in that: The method comprises the following steps: Step 1: Based on ZEMAX, select a light source and place a beam splitter behind the light source to form two light beams with the same distribution characteristics, thereby obtaining a detection light path and a reference light path; A lens, an object to be tested and a barrel detector are sequentially arranged on the detection optical path, a layer of fog simulation is arranged on the lens object to be tested, and a surface detector is arranged on the reference optical path; Step 2: Perform simulation using ZEMAX, use a bucket detector to record the total light intensity passing through the object to be tested, and use a surface detector to record the spatial distribution of the light intensity at the corresponding position; Calculate the light field distribution of the bucket detector position part and the surface detector position part; Step 3, calculating the width of the Gaussian point spread function according to the relationship between the light field and the light intensity, and calculating the depth of field according to the lens parameters; Step 4: Optimize by minimizing the width of the Gaussian point spread function and maximizing the depth of field. After the optimization is completed, the optimal imaging parameters are obtained.

2. The optimization method of a large-scale ghost imaging search and rescue system in a foggy environment according to claim 1, characterized in that: In the step 1, the light source is a circular light source that complies with a first-order Gaussian distribution, the beam splitter is a 5-5 beam splitter without polarization characteristics, and the fog simulation object is constructed based on a Mie scattering model.

3. The optimization method of a large-scale ghost imaging search and rescue system in a foggy environment according to claim 2, characterized in that: In step 2, the process of calculating the light field distribution corresponding to the position of the bucket detector has the following relationship: ； in, Represents the light field distribution of the part where the pseudo-bucket detector is located, They represent the impulse response functions from light source to lens, lens to object, and object to bucket detector respectively. represents the transmission function of the target to be measured, represents the bucket detector plane, Indicates the plane where the light source is located. Represents a light source.

4. The optimization method of a large-scale ghost imaging search and rescue system in a foggy environment according to claim 3 is characterized in that: In the process of calculating the impulse response function from light source to lens and from lens to object, according to the linear optical transfer system , the impulse response function from the light source to the lens and from the lens to the object is expressed as the impulse response function from the light source through the lens to the object. The calculation process of the impulse response function from the light source through the lens to the object has the following relationship: ； in, Represents the impulse response function of the light source passing through the lens to the object, Respectively represent the distance from the light source to the lens, and the distance from the lens to the object, represents the wave number, represents the imaginary unit, represents the focal length of the lens, Indicates the plane where the object is located. Indicates increment.

5. The optimization method of a large-scale ghost imaging search and rescue system in a foggy environment according to claim 4, characterized in that: The calculation process of the increment has the following relationship: 。 6. The optimization method of a large-scale ghost imaging search and rescue system in a foggy environment according to claim 5, characterized in that: The calculation process of the impulse response function from the object to the bucket detector has the following relationship: ； in, Indicates the distance from the object to the bucket detector.

7. The optimization method of a large-scale ghost imaging search and rescue system in a foggy environment according to claim 6, characterized in that: In step 2, the process of calculating the light field distribution of the surface detector position portion corresponds to the following relationship: ； in, Represents the light field distribution of the surface detector position part, represents the impulse response function from the light source to the surface detector, Represents the surface detector plane.

8. The optimization method of a large-scale ghost imaging search and rescue system in a foggy environment according to claim 7, characterized in that: The calculation process of the impulse response function from the light source to the surface detector has the following relationship: ； in, represents the impulse response function from the light source to the surface detector, Indicates the distance from the light source to the surface detector.

9. The optimization method of a large-scale ghost imaging search and rescue system in a foggy environment according to claim 8, characterized in that: In step 3, calculating the width of the Gaussian point spread function according to the relationship between the light field and the light intensity specifically includes the following steps: According to the total light intensity of the target object and the spatial distribution of the light intensity at the surface detector position, the intensity correlation calculation is performed to obtain the image information of the target to be measured. The corresponding process has the following relationship: ； in, Indicates the number of measurements, represents the mean operation, Represents the image information of the target to be measured, Represents the total light intensity of the target object, Represents the spatial distribution of light intensity at the surface detector position; According to the relationship between light field and light intensity, the intensity correlation calculation process is expressed as the second-order correlation function of the light field, and the corresponding process has the following relationship: ； ； in, represents the second-order correlation function of the light field, represent the light intensity of the reference light path and the detection light path respectively, Respectively The complex conjugate of Substitute the impulse response function from the object to the bucket detector and the impulse response function from the light source through the lens to the object into the second-order correlation function of the light field to obtain the Gaussian point spread function width. The corresponding process has the following relationship: ； in, represents the width of the Gaussian point spread function, represents the wavelength, Indicates the size of the light source.

10. A large-scale ghost imaging search and rescue system optimization system in a foggy environment, characterized in that: The system applies the optimization method of a large-scale ghost imaging search and rescue system in a foggy environment as described in any one of claims 1 to 9, and the system comprises: 2f system building blocks for: Based on ZEMAX, a light source is selected, and a beam splitter is placed after the light source to form two beams with the same distribution characteristics, thereby obtaining a detection optical path and a reference optical path; A lens, an object to be tested and a barrel detector are sequentially arranged on the detection optical path, a layer of fog simulation is arranged on the lens object to be tested, and a surface detector is arranged on the reference optical path; Simulation operation module, used for: Through ZEMAX simulation, the total light intensity passing through the object to be tested is recorded using a bucket detector, and the spatial distribution of the light intensity at the corresponding position is recorded using a surface detector; Calculate the light field distribution of the bucket detector position part and the surface detector position part; Influencing factor calculation module, used for: The width of the Gaussian point spread function is calculated based on the relationship between the light field and the light intensity, and the depth of field is calculated based on the lens parameters; Imaging parameter optimization module for: The optimization is performed with minimizing the width of the Gaussian point spread function and maximizing the depth of field as the optimization objectives. After the optimization is completed, the optimal imaging parameters are obtained.

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