Brightness temperature imaging method and system for air hidden detection

Abstract

The invention relates to a brightness temperature imaging method and system for air hidden detection, and the method comprises the following steps: 1, carrying out the modeling of an air shielding detection model, and obtaining a model that a radiometer is buried in sandy soil and a detection object is above the sandy soil; 2, the model is subdivided into a plurality of small areas or grids; 3, setting different dielectric constants on the measured surfaces of the target object, the sandy soil and the atmosphere, performing ray tracing, and simulating a propagation path of millimeter waves from the sky to the ground; 4, taking the Green's second theorem as a starting point, and utilizing a Kirchhoff residence phase approximation method and a Kirchhoff scalar approximation method to solve the transmission scattering problem of the Gaussian rough surface; 5, inverting the brightness temperature to the emission source point according to the brightness temperature and the transmission coefficient of the ray terminal to obtain the total brightness temperature of each position; generating an unprocessed brightness temperature map in the detected scene through accumulation calculation of multiple reflections and transmissions of rays in each medium; and 6, performing data processing on the brightness temperature map by using Gaussian filtering, and outputting a brightness temperature imaging result.

Description

Technical Field

[0001] The present invention belongs to the field of radiation emission technology, and in particular relates to a brightness temperature imaging method and system for covert air detection. Background Art

[0002] Passive millimeter wave imaging simulation plays a vital role in the field of passive millimeter wave imaging. Through simulation, the radiation characteristics of the target can be effectively studied and understood, its radiation phenomenon can be analyzed, and the laws of radiation can be discovered. Specifically, passive millimeter wave imaging relies on the millimeter wave radiation emitted or reflected by objects in the natural environment for imaging, rather than relying on active emission signals for detection.

[0003] However, existing millimeter wave imaging systems are usually exposed to the surface environment and are easily interfered by ground radiation noise and stray signals. They fail to fully utilize the advantages of the sky as a "cold source" background and fail to effectively use the radiation contrast between the sky and the ground and the target object to enhance the imaging effect. This makes the detection sensitivity, image quality and signal-to-noise ratio of the system in some application scenarios fail to reach the optimal level. In order to overcome this defect, the present invention proposes a brightness temperature imaging method and system for covert air detection. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention proposes a brightness temperature imaging method and system for covert air detection, in which the radiometer is buried underground. This design can effectively shield the interference of ground noise and focus on receiving radiation signals from the sky, thereby significantly improving the signal-to-noise ratio and sensitivity of the system. In addition, the present invention also has good concealment, and is particularly suitable for application scenarios such as security monitoring and military reconnaissance.

[0005] The present invention adopts the following technical scheme:

[0006] A brightness temperature imaging method for covert detection of air, the specific steps are as follows:

[0007] Step 1: Model the air shielding detection model, obtain a model in which the radiometer is located under the sand and the detection object is located above the sand, and construct a three-dimensional virtual scene model of the detected scene.

[0008] Step 2: The three-dimensional virtual scene obtained in step 1 is segmented and divided into multiple small areas or grids to facilitate more detailed radiation calculation and simulation.

[0009] Step 3: Set different dielectric constants for the target, sand, and atmosphere surfaces to perform ray tracing to simulate the propagation path of millimeter waves from the sky to the ground, as well as reflection and refraction phenomena in different media.

[0010] Step 4: Starting from Green's second theorem, Kirchhoff's resident phase approximation and Kirchhoff's scalar approximation are used to solve the transmission scattering problem of Gaussian rough surface.

[0011] Step 5: According to the brightness temperature and transmission coefficient of the ray terminal, the brightness temperature is inverted to the emission source point to obtain the total brightness temperature at each position; through the cumulative calculation of multiple reflections and transmissions of the rays in each medium, the unprocessed brightness temperature map of the detected scene is generated.

[0012] Step 6: Use Gaussian filtering to process the brightness temperature image and generate the final brightness temperature imaging result.

[0013] The present invention also discloses a brightness temperature imaging system for covert air detection, which is used to execute the above method and includes the following modules:

[0014] Modeling module: Modeling of air shielding detection model is carried out to obtain a model in which the radiometer is buried in the sand and the detection object is above the sand;

[0015] Model segmentation module: segment the obtained model into multiple small areas or grids;

[0016] Ray tracing module: Set different dielectric constants for the measured surfaces of the target, sand, and atmosphere, perform ray tracing, and simulate the propagation path of millimeter waves from the sky to the ground;

[0017] Transmission scattering problem solving module: Starting from Green's second theorem, Kirchhoff's resident phase approximation method and Kirchhoff's scalar approximation method are used to solve the transmission scattering problem of Gaussian rough surface;

[0018] Brightness temperature map generation module: According to the brightness temperature and transmission coefficient of the ray terminal, the brightness temperature is inverted to the emission source point to obtain the total brightness temperature at each position; through the cumulative calculation of multiple reflections and transmissions of the ray in each medium, the unprocessed brightness temperature map of the detected scene is generated;

[0019] Gaussian filter module: Use Gaussian filtering to process the brightness temperature image and output the brightness temperature imaging results.

[0020] Beneficial effects of the present invention:

[0021] The radiometer is buried underground to avoid interference from stray signals on the ground, focusing on the radiation signal of the sky background, greatly improving the signal-to-noise ratio of the system. The present invention makes full use of the "cold source" characteristics of the sky background, combined with the radiation contrast between the ground and the target object, to improve the detection sensitivity. It can be applied to border monitoring, security systems in sensitive areas, military reconnaissance and other fields to meet the needs of covert detection in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1Schematic diagram of the radiometer burial scenario model.

[0023] Figure 2 Schematic diagram of rough surface transmission.

[0024] Figure 3 This is a single ray brightness temperature tracing model for a rough surface of a multi-layer medium.

[0025] Figure 4 This is a single ray brightness temperature inversion model for a rough surface of a multi-layer medium.

[0026] Figure 5(a) and Figure 5(b) show the perpendicular polarization brightness temperature image and the parallel polarization brightness temperature image, respectively.

[0027] Figure 6(a) and Figure 6(b) show the measured brightness temperature distribution before and after applying the Gaussian filtering algorithm, respectively.

[0028] Figure 7 The present invention is a block diagram of a brightness temperature imaging system for covert air detection according to a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0029] The preferred embodiments of the present invention are further described below in conjunction with the accompanying drawings.

[0030] This embodiment discloses a brightness temperature imaging method for covert air detection, including the following specific steps:

[0031] Step (1): Use the gmsh software to build an air shielding detection model, and obtain a model in which the radiometer is placed in a transparent box and buried in the sand, with the detection object above the sand. Figure 1 As shown, it is mainly divided into three layers of media. The bottom layer is the air around the radiometer, the second layer is the buried sand, and the third layer is the atmosphere. The rough surfaces here are all considered to be Gaussian rough surfaces.

[0032] Step (2): The three-dimensional virtual scene obtained in step (1) is segmented and divided into a plurality of small areas or grids for more sophisticated radiation calculation and simulation.

[0033] Step (3): Set different dielectric constants for the measured surfaces of the target object, sand, and atmosphere, perform ray tracing, and simulate the propagation path of the millimeter wave from the sky to the ground.

[0034] Step (4): Starting from Green's second theorem, the transmission scattering problem of Gaussian rough surface is studied using Kirchhoff's resident phase approximation and Kirchhoff's scalar approximation. For this model, the brightness temperature of each layer needs to accurately reflect the contribution of the brightness temperature of the rays in each direction of the lower layer to the total brightness temperature. Using the integral method, the brightness temperature of the rays in each direction is multiplied by the corresponding transmission coefficient and accumulated. The transmission coefficient calculation formula is as follows:

[0035]

[0036] Among them, θ k and represents the azimuth and polar angle of the scattering direction. θ1 and is the angle of the incident direction. ppt and σ pqt represents the scattering coefficient in different modes. N and M are discrete step length related parameters of the angle in the integration domain, which determine the integration range. sin(θ) is the volume integral weight in spherical coordinates, cos(θ k ) is the angle factor used for correction.

[0037] According to the dwell phase approximation method, Figure 2 The bidirectional scattering coefficient in medium 2 can be expressed as:

[0038]

[0039] It represents the characteristic impedance ratio of two media, which is used to describe the difference in the propagation of electromagnetic waves in different media; k2 is Figure 2 The wave number in the medium 2 shown affects the propagation characteristics of the wave; is the scattering vector and its components, which describes the directional change of the scattering and can be expressed as:

[0040]

[0041] D pq is the polarization-related coefficient and can be expressed as:

[0042]

[0043] ρ”(0) is the second-order derivative of the surface autocorrelation function at zero point, which describes the spatial correlation of surface roughness. Since the rough surface is regarded as a Gaussian rough surface, it can be expressed as:

[0044]

[0045] In the above formulas:

[0046]

[0047]

[0048] Step (5): According to the brightness temperature and transmission coefficient of the ray terminal, the brightness temperature is inverted to the emission source point to obtain the total brightness temperature at each position. The unprocessed brightness temperature distribution map in the detected scene is generated by cumulative calculation of multiple reflections and transmissions of the ray in each medium. The parallel polarization brightness temperature map simulation map and the vertical polarization brightness temperature map simulation map of the air shielding detection model are shown in Figure 5(a) and Figure 5(b), which verify the effectiveness of the radiometer concealment method.

[0049] Step (6): The brightness temperature image is processed by Gaussian filtering through adaptive weighting, edge protection and multi-scale processing methods to improve the denoising effect and detail retention ability, and the final image is output.

[0050] The Gaussian function has the form:

[0051]

[0052] In image processing, the weighted average formula of Gaussian filtering is:

[0053]

[0054] The measured brightness temperature distribution diagram before and after the Gaussian filtering algorithm are shown in FIG6(a) and FIG6(b). It can be seen that the present invention enhances the contour information of the target object and filters out certain noise.

[0055] like Figure 7 As shown, this embodiment discloses a brightness temperature imaging system for covert air detection, which is used to execute the above method and includes the following modules:

[0056] Modeling module: Modeling of air shielding detection model is carried out to obtain a model in which the radiometer is buried in the sand and the detection object is above the sand;

[0057] Model segmentation module: segment the obtained model into multiple small areas or grids;

[0058] Ray tracing module: Set different dielectric constants for the measured surfaces of the target, sand, and atmosphere, perform ray tracing, and simulate the propagation path of millimeter waves from the sky to the ground;

[0059] Transmission scattering problem solving module: Starting from Green's second theorem, Kirchhoff's resident phase approximation method and Kirchhoff's scalar approximation method are used to solve the transmission scattering problem of Gaussian rough surface;

[0060] Brightness temperature map generation module: According to the brightness temperature and transmission coefficient of the ray terminal, the brightness temperature is inverted to the emission source point to obtain the total brightness temperature at each position; through the cumulative calculation of multiple reflections and transmissions of the ray in each medium, the unprocessed brightness temperature map of the detected scene is generated;

[0061] Gaussian filter module: Use Gaussian filtering to process the brightness temperature image and output the brightness temperature imaging results.

[0062] For other contents of this embodiment, please refer to the above method embodiment.

[0063] The above description is only a detailed description of the preferred embodiments and principles of the present invention. For ordinary technicians in this field, according to the ideas provided by the present invention, there will be changes in the specific implementation methods, and these changes should also be regarded as the protection scope of the present invention.

Claims

1. A brightness temperature imaging method for covert air detection, characterized in that: The specific steps of the method are as follows: Step (1), modeling an air shielding detection model, obtaining a model in which the radiometer is buried in the sand and the detection object is above the sand; Step (2), dividing the model obtained in step (1) into a plurality of small areas or grids; Step (3), setting different dielectric constants for the measured surfaces of the target object, sand, and atmosphere, performing ray tracing, and simulating the propagation path of the millimeter wave from the sky to the ground; Step (4), starting from Green's second theorem, using Kirchhoff's resident phase approximation method and Kirchhoff's scalar approximation method to solve the transmission scattering problem of Gaussian rough surface; Step (5), according to the brightness temperature and transmission coefficient of the ray terminal, the brightness temperature is inverted to the emission source point to obtain the total brightness temperature at each position; by accumulating the multiple reflections and transmissions of the ray in each medium, an unprocessed brightness temperature map of the detected scene is generated; Step (6), use Gaussian filtering to process the brightness temperature image and output the brightness temperature imaging result.

2. A brightness temperature imaging method for covert air detection as claimed in claim 1, characterized in that: Step (4) is as follows: using the integration method, multiply the brightness temperature of the rays in each direction by the corresponding transmission coefficient, and add them up; the transmission coefficient calculation formula is as follows: Among them, θ k and represents the azimuth and polar angle of the scattering direction; θ1 and is the angle of the incident direction; σ ppt and σ pqt represents the scattering coefficient under different modes; N and M are discrete step length related parameters of the angle in the integration domain, which determine the integration range; sin(θ) is the volume integral weight in spherical coordinates, cos(θ k ) Angle factor for correction; According to the resident phase approximation, the scattering coefficient is expressed as: η1 / η2 represents the characteristic impedance ratio of the two media, which is used to describe the difference in the propagation of electromagnetic waves in different media; k2 is the wave number in medium 2, which affects the propagation characteristics of the wave; σ 2 Represents the parameters related to surface roughness, which affects the scattering characteristics of electromagnetic waves on rough surfaces; as well as is the scattering vector and its components, describing the change in scattering direction, expressed as: Among them, θ i and represents the incident angle and the azimuth of the incident wave; θ s and represents the scattering angle and the azimuth of the scattered wave; D pq is the polarization-dependent coefficient, expressed as: Among them, D hh , D vh , D hv and D vv Respectively represent the cases of horizontal-horizontal polarization, vertical-horizontal polarization, horizontal-vertical polarization and vertical-vertical polarization; and The unit vector representing the direction of the electric field of the incident wave and the scattered wave; T ⊥ and T / / The unit vectors representing the directions of the electric fields of the incident and scattered waves; ρ”(0) is the second-order derivative of the surface autocorrelation function at zero point, which describes the spatial correlation of surface roughness. Since the rough surface is regarded as a Gaussian rough surface, it can be expressed as: Where, l represents the coherence length; In the above formula:

3. A brightness temperature imaging method for covert air detection as claimed in claim 2, characterized in that: Step (6) is as follows: The form of the Gaussian function is: In image processing, the weighted average formula of Gaussian filtering is:

4. A brightness temperature imaging system for covert detection of air, used to perform the method according to any one of claims 1 to 3, characterized in that: The system comprises the following modules: Modeling module: Modeling of air shielding detection model is carried out to obtain a model in which the radiometer is buried in the sand and the detection object is above the sand; Model segmentation module: segment the obtained model into multiple small areas or grids; Ray tracing module: Set different dielectric constants for the measured surfaces of the target, sand, and atmosphere, perform ray tracing, and simulate the propagation path of millimeter waves from the sky to the ground; Transmission scattering problem solving module: Starting from Green's second theorem, Kirchhoff's resident phase approximation method and Kirchhoff's scalar approximation method are used to solve the transmission scattering problem of Gaussian rough surface; Brightness temperature map generation module: According to the brightness temperature and transmission coefficient of the ray terminal, the brightness temperature is inverted to the emission source point to obtain the total brightness temperature at each position; through the cumulative calculation of multiple reflections and transmissions of the ray in each medium, the unprocessed brightness temperature map of the detected scene is generated; Gaussian filter module: Use Gaussian filtering to process the brightness temperature image and output the brightness temperature imaging results.

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