A method, apparatus, and equipment for enhancing the brightness temperature resolution of a microwave radiometer

Abstract

This invention discloses a method for enhancing the brightness temperature resolution of a microwave radiometer, along with related devices, equipment, and storage media. It relates to the field of spaceborne microwave radiometer technology and aims to solve the problems of insufficient brightness temperature resolution, sidelobe-induced reconstruction distortion, and the difficulty in balancing resolution and stability in spaceborne real-aperture microwave radiometers. The method includes: acquiring relevant data; constructing a convex optimization model with sidelobe suppression constraints and solving for initial weights; iterating with the initial weights, adaptively updating until a termination condition is met, and outputting the optimal weights; using the optimal weights to reconstruct the original brightness temperature data, outputting a result with enhanced resolution and no obvious oscillation artifacts. This invention requires no hardware modification, balances sidelobe suppression and resolution through algorithmic optimization, takes into account both accuracy and stability, adapts to the real-time requirements of spaceborne systems, provides support for meteorological and oceanographic monitoring, and discloses corresponding devices, equipment, and storage media.

Description

Technical Field

[0001] This application relates to the field of microwave radiometer resolution enhancement technology, and in particular to a method, apparatus, and equipment for enhancing the brightness temperature resolution of a microwave radiometer. Background Technology

[0002] Spaceborne microwave radiometers are important remote sensing instruments, offering all-weather, all-day observation advantages and widely used in meteorological monitoring, marine and terrestrial environmental exploration, and other fields. However, due to space constraints on the spaceborne platform, it is difficult to increase the antenna aperture, resulting in extremely low resolution of brightness temperature data in the low-frequency channel, which cannot meet the high-resolution observation requirements of near-shore and terrestrial scenarios. In engineering practice, increasing the antenna aperture to improve resolution is costly and risky, making it difficult to scale up. Therefore, enhancing the resolution of brightness temperature data through post-processing algorithms based on existing hardware has become a core solution that balances economy, feasibility, and application scalability.

[0003] Existing methods for enhancing brightness temperature data resolution primarily rely on alternating iterative approximation gradient methods. These methods construct specific mathematical models and iteratively optimize the original data using quadratic problem solving and TV norm denoising. However, these methods have significant drawbacks: slow iterative convergence, low processing efficiency, poor adaptability to complex underlying surfaces such as nearshore mixed land and sea areas, limited resolution enhancement effects, and susceptibility to introducing false information, failing to meet the requirements for refined processing of low-frequency channels.

[0004] In summary, there is an urgent need to develop a method, device, or equipment for enhancing the brightness temperature resolution of microwave radiometers to overcome the aforementioned shortcomings of existing technologies and meet the requirements for refined observation. Summary of the Invention

[0005] The purpose of this invention is to provide a method, apparatus, and device for enhancing the brightness temperature resolution of a microwave radiometer, which solves the defects of existing methods such as slow iterative convergence, poor adaptability, and easy introduction of false information. It achieves efficient and accurate enhancement of the brightness temperature resolution of a spaceborne real-aperture microwave radiometer, meets the needs of refined observation in near-shore and land scenarios, and provides technical support for the engineering application of spaceborne microwave remote sensing data.

[0006] To achieve the above-mentioned objectives, this application adopts the following technical solution to form a closed-loop brightness temperature resolution enhancement technology: a method for enhancing the brightness temperature resolution of a microwave radiometer, the method comprising the following steps: Step 1: Obtain the preset brightness temperature observation data, antenna pattern and antenna surface projection matrix. Based on the brightness temperature observation data, antenna pattern and antenna surface projection matrix, construct a convex optimization model with sidelobe suppression constraints. By solving the convex optimization model, obtain the initial combination weight coefficients of the synthetic beam. Step 2: Using the initial combined weight coefficients as the initial values ​​for iteration, the synthesized beam is iteratively optimized. In each iteration, the combined weights are adaptively updated based on the energy response of the side lobes outside the main lobe of the synthesized beam, until the resolution error between the reconstructed synthesized beam and the target beam meets the preset accuracy condition, and the final combined weight coefficients are obtained. Step 3: Use the final combined weighting coefficients to perform weighted reconstruction processing on the original brightness temperature observation data, and output the brightness temperature resolution enhancement result.

[0007] Furthermore, the antenna surface projection matrix is ​​normalized, and the normalized antenna surface projection matrix corresponds one-to-one with the antenna pattern parameters and the mapping relationship from the antenna to the ground surface pixels. Moreover, the accuracy of the mapping relationship matches the projection resolution accuracy of the antenna surface projection matrix.

[0008] Furthermore, in the iteration process of step 2, a maximum number of iterations is preset, and the iteration termination condition is: the preset maximum number of iterations is reached, or the resolution error between the reconstructed synthetic beam and the target beam meets a preset accuracy condition. The iteration terminates when either of the two conditions is met.

[0009] Furthermore, in step 1, the sidelobe suppression constraint introduced in the process of solving the synthesized beam weight is specifically that the sidelobe level of the synthesized beam is lower than a preset sidelobe threshold. The convex optimization model is solved using a convex optimization algorithm. The initial combined weight coefficients are calculated from the noise covariance, initial regularization parameters, target beam matrix, and antenna pattern matrix offset. The antenna pattern matrix offset is the deviation matrix between the antenna pattern matrix in actual operation and the nominal antenna pattern matrix in ideal design.

[0010] Furthermore, the adaptive update of the combined weights in step 2 specifically includes the following sub-steps: Sub-step 2.1: Define the main lobe region and side lobe region of the synthesized beam, extract the side lobe energy response value of the side lobe region, and calculate the total side lobe energy based on the side lobe energy response value; Sub-step 2.2: Normalize the sidelobe energy response value, construct an optimization model with the goal of minimizing sidelobe energy, and combine the dynamic adjustment value of the regularization parameter, the antenna pattern matrix and the normalized sidelobe energy response value to solve for the contribution weight corresponding to the sidelobe energy. Sub-step 2.3: The dynamic adjustment value of the regularization parameter is obtained by adaptively updating the initial regularization parameter, the preset growth step size and the current iteration number, and the dynamic adjustment value of the regularization parameter does not exceed the preset upper limit of the regularization parameter.

[0011] Furthermore, in step 2, the combined weights are updated using a preset fusion ratio. Specifically, based on the contribution weights corresponding to the main lobe region energy, the total energy of the side lobes, and the contribution weights of the side lobes, the combined weights are fused according to the preset fusion ratio to complete the update of the combined weights. The contribution weight corresponding to the energy in the main lobe region is calculated by the dynamic adjustment value of the regularization parameter, the antenna pattern matrix, and the synthetic beam parameters obtained from the previous iteration.

[0012] Furthermore, in step 2, after each iteration of updating the combined weights, the combined weights are weighted and fused with the antenna pattern matrix, and then the fusion result is normalized to obtain a new synthetic beam. For the newly obtained synthetic beam, its sidelobe energy change is monitored synchronously, and its resolution error with the preset target beam is calculated. The resolution error is defined as the relative difference between the resolution of the current synthetic beam and the resolution of the target beam. The preset error threshold is 0.1-0.5, which is consistent with the resolution dimension. The resolution error being less than the preset error threshold and the sidelobe energy being within a preset reasonable range are used together as the basis for determining the preset accuracy condition in the iteration termination condition.

[0013] In addition, to achieve the above-mentioned objectives, this application also provides a microwave radiometer brightness temperature resolution enhancement device, which corresponds one-to-one with the above-mentioned methods. It can realize the engineering implementation of the above-mentioned methods, has a simple structure, strong portability, and is suitable for the space and performance constraints of spaceborne platforms.

[0014] Furthermore, the device includes: an initial weight calculation module, used to acquire preset brightness temperature observation data, antenna pattern and antenna surface projection matrix, and based on the brightness temperature observation data, antenna pattern and antenna surface projection matrix, construct a convex optimization model with sidelobe suppression constraints, and obtain the initial combination weight coefficients of the synthetic beam by solving the convex optimization model; The iterative optimization module is used to iteratively optimize the synthetic beam using the initial combined weight coefficients as the initial values ​​for iteration. In each iteration, the combined weights are adaptively updated based on the energy response of the side lobes outside the main lobe of the synthetic beam until the resolution error between the reconstructed synthetic beam and the target beam meets the preset accuracy condition, and the final combined weight coefficients are obtained. The result output module is used to perform weighted reconstruction processing on the original brightness temperature observation data using the final combined weighting coefficients, and output the brightness temperature resolution enhancement result.

[0015] In addition, to achieve the above-mentioned objectives, this application also provides a microwave radiometer brightness temperature resolution enhancement device and a computer-readable storage medium, providing hardware and software support for the stable and efficient execution of the above method, and facilitating the large-scale application and promotion of the method.

[0016] This invention proposes a method, apparatus, device, and computer-readable storage medium for enhancing the brightness temperature resolution of a microwave radiometer. By constructing a convex optimization model with sidelobe suppression constraints to solve for the initial combined weight coefficients, iterative optimization is performed using these initial values, and the combined weights are adaptively updated. Finally, the original brightness temperature observation data is reconstructed using the final combined weights. This solves the technical problems of insufficient brightness temperature resolution and low reconstruction accuracy caused by sidelobe interference in spaceborne real-aperture microwave radiometers, achieving the following beneficial effects:

[0017] This invention constructs a convex optimization model with sidelobe suppression constraints based on brightness temperature observation data, antenna radiation patterns, and antenna surface projection matrices. This ensures the rationality of the initial combined weights and reduces sidelobe interference from the initial stage. Iterative optimization is performed based on the initial weights, adaptively updating the weights in conjunction with the sidelobe energy response until the accuracy requirements are met. This dynamically corrects observation biases, ensuring a high degree of matching between the synthesized beam and the target beam. Compared to traditional methods, this approach improves resolution enhancement while avoiding reconstruction distortion, balancing reconstruction accuracy and algorithm stability.

[0018] This invention introduces sidelobe suppression constraints and limits the sidelobe level threshold in the convex optimization model, achieving effective suppression of sidelobe interference from the initial solution stage. During the iterative solution process, sidelobe energy information is extracted in real time, and the sidelobe suppression weight is dynamically adjusted in combination with regularization parameters to continuously suppress sidelobe components, further reducing data pollution caused by sidelobes. This makes the brightness temperature enhancement results closer to the real surface brightness temperature, significantly improving data reliability and providing high-quality data support for subsequent remote sensing applications.

[0019] This invention employs a normalized antenna surface projection matrix, which better aligns with equipment parameters and observation characteristics. The iterative process incorporates dual termination conditions, balancing computational efficiency and enhancement effects. This solution requires no hardware modifications; resolution is improved solely through algorithm optimization, significantly reducing application costs. Simultaneously, the initial combination weights are calculated from key parameters, ensuring scientific design. During iteration, regularization parameters are dynamically and linearly adjusted without exceeding an upper limit, and weight updates fuse main and sidelobe energy contributions according to a preset ratio. The algorithm logic is clear, balancing enhancement effects and computational complexity, adapting to the real-time processing requirements of spaceborne equipment, and demonstrating outstanding practicality, scalability, and controllability.

[0020] This invention not only proposes a brightness temperature resolution enhancement method, but also provides a corresponding device, equipment, and computer-readable storage medium. The initial weight calculation module, iterative optimization module, and result output module in the device correspond one-to-one with the method steps, enabling the method's engineering implementation. The design of the equipment and storage medium ensures that the method can operate stably in actual spaceborne equipment, facilitating its widespread application and further enhancing the practical value of brightness temperature resolution enhancement technology, thus driving technological upgrades in the field of spaceborne microwave radiometer applications. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the process for enhancing the data resolution of the microwave radiometer according to the present invention; Figure 2 This is a comparison diagram of the enhanced results of the method of the present invention and the existing technology; Figure 3 This is a comparison diagram of the cross-sectional changes between the method of this invention and the prior art method; Figure 4 A schematic diagram of the device provided in the embodiment of the present invention; Figure 5 A schematic diagram of the device provided for an embodiment of the present invention. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0023] In related technologies, spaceborne real-aperture microwave radiometers, when performing tasks such as meteorological observation and ocean monitoring, generally suffer from low spatial resolution of brightness temperature observation data due to limitations in the physical aperture size of the antenna. Existing technologies typically employ beamforming or image reconstruction algorithms to improve resolution, but these face two major technical bottlenecks in practical applications: first, during resolution enhancement, sidelobe effects easily lead to oscillation artifacts and data distortion in the reconstructed image; second, existing algorithms struggle to achieve a dynamic balance between the magnitude of resolution improvement and the stability of the reconstruction results, often resulting in either insufficient enhancement or incomplete sidelobe suppression. This severely restricts the acquisition of high-precision, high-reliability brightness temperature data by spaceborne microwave radiometers, hindering their further application in high-end remote sensing fields.

[0024] First Embodiment Reference Figure 1 The first embodiment of this invention provides a method for enhancing the brightness temperature resolution of a microwave radiometer, applied to a spaceborne real-aperture microwave radiometer system, aiming to solve the technical pain points of insufficient resolution and sidelobe interference in the prior art. The method specifically includes the following steps:

[0025] Step S1: Initial weight calculation and model construction Pre-defined brightness temperature observation data, antenna pattern, and antenna surface projection matrix are acquired. Based on this data, a convex optimization model with sidelobe suppression constraints is constructed. The core constraint of this model is that the sidelobe level of the synthesized beam is lower than a preset threshold. The model is solved using a convex optimization algorithm to calculate the initial combination weight coefficients of the synthesized beam. In the specific calculation, the antenna surface projection matrix needs to be normalized first, and its resolution accuracy must be strictly matched with the accuracy of the mapping relationship between the antenna and the surface pixels to ensure the reliability of the basis for subsequent weight calculations. The initial combination weight coefficients are calculated comprehensively from key parameters such as noise covariance, initial regularization parameters, target beam matrix, and antenna pattern matrix offset. A sidelobe suppression mechanism is introduced from the algorithm source to ensure the scientific and reasonable nature of the initial weights.

[0026] Step S2: Adaptive Iterative Weight Optimization The initial combined weight coefficients obtained in step S1 are used as the initial values ​​for iteration, and the optimization iteration loop is entered. During each iteration, the combined weights are adaptively updated based on the energy response characteristics of the side lobes outside the main lobe of the synthesized beam. The iteration process continues until a preset iteration termination condition is met, ultimately outputting the optimal combined weight coefficients. The iteration termination condition is set to either one of two conditions: first, reaching a preset maximum number of iterations; second, the resolution error between the reconstructed synthesized beam and the target beam meets a preset accuracy requirement (i.e., the error is less than 0.1-0.5).

[0027] The adaptive weight update mechanism is as follows: First, the main lobe and side lobe regions of the synthesized beam are defined, the side lobe energy response values ​​are extracted, and the total energy is calculated. After normalizing the side lobe energy, the contribution weight of the side lobe energy is solved by combining dynamically adjusted regularization parameters and the antenna pattern matrix. The regularization parameters are dynamically adjusted (non-linearly increasing) with the number of iterations to balance the enhancement strength in the early stage and the stability in the later stage, avoiding oscillation accumulation and noise amplification. Finally, the energy weight of the main lobe region and the side lobe suppression weight are combined according to a preset fusion ratio to complete a single weight update. After each update, the synthesized beam and its resolution error must be recalculated to determine whether convergence has occurred.

[0028] Step S3: Brightness Temperature Data Reconstruction and Output Using the optimal combined weight coefficients obtained in step S2, the original brightness temperature observation data is subjected to weighted linear reconstruction processing, and the final output brightness temperature data results with significantly enhanced resolution and no obvious oscillation artifacts are produced.

[0029] The beneficial effects of this embodiment are as follows: without modifying the hardware, it only requires algorithm optimization to suppress sidelobe interference at the source and dynamically correct weight deviation, effectively solving the technical problems of insufficient brightness temperature resolution and low reconstruction accuracy caused by sidelobe interference in spaceborne real aperture microwave radiometers; compared with the existing technology, it can significantly improve the spatial resolution of brightness temperature images, effectively suppress sidelobe oscillation and reconstruction distortion, and balance the resolution enhancement effect, reconstruction accuracy and algorithm stability, adapting to the real-time processing needs of spaceborne equipment, and providing high-quality brightness temperature data support for applications such as meteorological observation and marine monitoring.

[0030] Second embodiment: Microwave radiometer brightness temperature resolution enhancement device Reference Figure 4 The second embodiment of this application provides a microwave radiometer brightness temperature resolution enhancement device, which corresponds one-to-one with the method of the first embodiment, and is used to realize the engineering implementation of the method, facilitating its integration and application in spaceborne equipment. The device includes the following functional modules:

[0031] The initial weight calculation module is used to acquire preset brightness temperature observation data, antenna pattern and antenna surface projection matrix. Based on the brightness temperature observation data, antenna pattern and antenna surface projection matrix, a convex optimization model with sidelobe suppression constraints is constructed. By solving the convex optimization model, the initial combination weight coefficients of the synthetic beam are obtained. The iterative optimization module is used to iteratively optimize the synthetic beam using the initial combined weight coefficients as the initial values ​​for iteration. In each iteration, the combined weights are adaptively updated based on the energy response of the side lobes outside the main lobe of the synthetic beam until the resolution error between the reconstructed synthetic beam and the target beam meets the preset accuracy condition, and the final combined weight coefficients are obtained. The result output module is used to perform weighted reconstruction processing on the original brightness temperature observation data using the final combined weighting coefficients, and output the brightness temperature resolution enhancement result.

[0032] The modules of the device described in this embodiment have clear logic and well-defined functions, and correspond completely to the method steps. It can accurately replicate the entire technical process of the first embodiment, and has a compact structure that is easy to implement and integrate in engineering.

[0033] Third embodiment: Microwave radiometer brightness temperature resolution enhancement device Reference Figure 5 The third embodiment of this application provides a microwave radiometer brightness temperature resolution enhancement device for carrying out the above-described method, ensuring its stable operation in actual spaceborne equipment. The device includes a processor and a memory connected via a bus.

[0034] Memory: Used to store computer program instructions for implementing the brightness temperature resolution enhancement method described above, as well as brightness temperature observation data, antenna parameters, iteration parameters, and other related data.

[0035] Processor: Calls program instructions stored in memory to execute all the steps of the method described in the first embodiment.

[0036] Preferred Solution: The processor utilizes a high-performance processor adapted to the extreme environment of spaceborne systems, possessing powerful real-time computing capabilities. It can quickly complete convex optimization solutions and iterative weight updates, meeting the processing time requirements of spaceborne equipment. Testing has shown that this device requires no modification to the existing spaceborne radiometer hardware architecture; only the integration of corresponding program instructions is needed for functional upgrades, significantly reducing application costs and facilitating large-scale deployment.

[0037] Fourth embodiment: Computer-readable storage medium The fourth embodiment of this application provides a computer-readable storage medium for storing computer program instructions that implement the above-described method. When the program instructions on the storage medium are executed by a processor, they fully implement the microwave radiometer brightness temperature resolution enhancement method described in the first embodiment.

[0038] Preferred solution: The storage medium uses a non-volatile memory chip adapted for spaceborne equipment, possessing characteristics of resistance to space radiation, high temperature resistance, and stable read / write operation. This medium can be integrated into the device of the third embodiment or used as an independent module adapted to different models of spaceborne microwave radiometers, exhibiting strong versatility and portability, effectively expanding the application scope of the technical solution.

[0039] Example verification In this embodiment, observational data from the scanning microwave radiometer (SMR) of Haiyang-2B satellite are used for verification. Low-frequency C-band (6.925 GHz) data is selected as an example, with a spatial resolution of 90 km × 150 km. The details are as follows:

[0040] First, preparatory work is carried out before iteration, including calculating the surface projection matrix, initializing the number of iterations, and solving for the initial weights of the synthesized beam. The specific steps are as follows: 1. Calculate the surface projection matrix A(x,y) of the spaceborne microwave radiometer: This projection matrix is ​​used to characterize the mapping relationship between the antenna pattern and the surface sampling grid. Its calculation result directly determines the number of overlapping fields of view and their corresponding weights involved in the joint enhancement process in the subsequent resolution enhancement process.

[0041] 2. Initialize the number of iterations: Initialize the number of iterations k by setting the initial value of k to 0.

[0042] 3. Solving for the initial weights of the synthesized beam: Solve for the weights of the synthesized beam. The obtained weights are used as the initial weight coefficients, denoted as q(0). The calculation formula is as follows: , The initial weighting coefficient is related to the overlapping beams associated with it within a certain range. In this embodiment, the associated beam range is set to 11 dB. The parameters are defined as follows: ), NEDT represents the noise equivalent temperature difference. , As the initial regularization parameter, in this embodiment Take 0.5, , , For the target antenna matrix, , Position offset Antenna pattern matrix, subscript , Corresponding to the offset.

[0043] After initialization is complete, the iterative solution process begins, which includes the following steps; First step, update the iteration count: k = k + 1; The second step is to calculate the synthesized beam in the k-th iteration: ; The third step is to divide the main lobe and side lobe regions of the synthesized beam, and extract the side lobe portion of the synthesized beam, defined as follows: Step 4: Calculate the energy of the sidelobe region: , The fifth step is to normalize the sidelobe energy, and the normalization result is denoted as: Step 6: For the sidelobe region, calculate the weighting coefficients of the parts that contribute to the sidelobe energy: in, , , Among them, the regularization parameter It is dynamically updated during the iteration process to enhance the stability of the algorithm in the later stages.

[0044] In this embodiment, the growth step size Setting it to 0.05 sets the upper limit for the regularization parameter. It is 0.8.

[0045] Step 7: Calculate the weights of the main lobe region: in, Here, the main lobe region weight is mainly used to ensure that the updated synthetic beam still maintains its ability to approximate the target pattern.

[0046] Step 8: Combine the main lobe and side lobe regions to update the weights, obtaining a new comprehensive weight coefficient. The update method is expressed as follows: Step 9: Resolve the synthesized beam based on the updated weighting coefficients: Step 10: Perform overall normalization on the resulting synthesized beam. Step 11: Check if the iteration termination condition is met: In this embodiment, the termination condition is determined by monitoring the energy of the sidelobe region. When the normalized sidelobe energy is lower than a preset threshold, it is considered that the sidelobe has been effectively suppressed, and the iteration stops; otherwise, the next iteration continues. The corresponding criterion can be expressed as: This embodiment selects .

[0047] The initial regularization parameter determines the regularization strength at the beginning of the iteration: the smaller the parameter value, the more the algorithm emphasizes the pattern approximation ability in the first few iterations, resulting in more aggressive resolution enhancement; the larger the parameter value, the more the algorithm prioritizes stability and noise suppression in the first few iterations. Considering that the main goal of the first few iterations in this invention is to more aggressively weaken sidelobes, while maintaining a small regularization strength in later iterations could easily lead to problems such as oscillation accumulation, excessive weights, and noise amplification, this invention adopts a method of gradually increasing the regularization parameter with the number of iterations, thereby achieving a better balance between resolution enhancement capability and reconstruction stability.

[0048] like Figure 2 As shown, Figure 2 (a) is a simulated high-brightness temperature gradient region. Figure 2 (b) is an image observed by the scanning microwave radiometer of Haiyang-2B satellite. Figure 2 (b) It can be seen that the original observation results are significantly blurred at the location of the brightness temperature abrupt change. The brightness temperature image enhanced by the BG method is as follows: Figure 2 As shown in (c), the gradient at the location of the brightness-temperature abrupt change is significantly tighter than that of the original observed image, indicating that the method has a certain resolution enhancement capability; however, a relatively obvious oscillation phenomenon occurs on both sides of the brightness-temperature gradient. In contrast, the method proposed in this invention... Figure 2 (b) After enhancement, while maintaining a gradient tightening effect similar to the BG method, the oscillations on both sides of the brightness temperature gradient are significantly suppressed, indicating that the present invention has a better balance between resolution enhancement and sidelobe suppression.

[0049] like Figure 3 As shown, this is a curve depicting the brightness temperature change trend at the midline position. Figure 3 As can be seen, compared with the original observed brightness temperature curve, both the method proposed in this invention and the BG method can recover the original brightness temperature gradient trend well. However, both enhancement methods introduce a certain degree of fluctuation on both sides of the brightness temperature gradient. Among them, this invention, while maintaining a gradient recovery capability comparable to the BG method, significantly reduces the amplitude of fluctuations on both sides, especially in the secondary fluctuation region where the suppression effect is more obvious. This further demonstrates that this invention can effectively reduce the oscillation artifacts introduced during the enhancement process while achieving brightness temperature image resolution enhancement.

[0050] Therefore, compared with the existing BG method, the resolution enhancement method based on microwave radiometer brightness temperature data proposed in this invention can not only improve the spatial resolution of brightness temperature images, but also effectively suppress sidelobe oscillations generated during the enhancement process, resulting in better stability and superior reconstruction effect.

[0051] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for enhancing the brightness temperature resolution of a microwave radiometer, characterized in that, Applied to a spaceborne real-aperture microwave radiometer, the method includes the following steps: Step 1: Obtain the preset brightness temperature observation data, antenna pattern and antenna surface projection matrix. Based on the brightness temperature observation data, antenna pattern and antenna surface projection matrix, construct a convex optimization model with sidelobe suppression constraints. By solving the convex optimization model, obtain the initial combination weight coefficients of the synthetic beam. Step 2: Using the initial combined weight coefficients as the initial values ​​for iteration, the synthesized beam is iteratively optimized. In each iteration, the combined weights are adaptively updated based on the energy response of the side lobes outside the main lobe of the synthesized beam, until the resolution error between the reconstructed synthesized beam and the target beam meets the preset accuracy condition, and the final combined weight coefficients are obtained. Step 3: Use the final combined weighting coefficients to perform weighted reconstruction processing on the original brightness temperature observation data, and output the brightness temperature resolution enhancement result.

2. The method for enhancing the brightness temperature resolution of a microwave radiometer according to claim 1, characterized in that, The antenna surface projection matrix is ​​normalized. The normalized antenna surface projection matrix corresponds one-to-one with the antenna pattern parameters and the mapping relationship from the antenna to the ground surface pixels. The accuracy of the mapping relationship matches the projection resolution accuracy of the antenna surface projection matrix.

3. The method for enhancing the brightness temperature resolution of a microwave radiometer according to claim 1, characterized in that, In the iteration process of step 2, a maximum number of iterations is preset. The iteration termination condition is: the preset maximum number of iterations is reached, or the resolution error between the reconstructed synthetic beam and the target beam meets a preset accuracy condition. The iteration terminates when either of the two conditions is met.

4. The method for enhancing the brightness temperature resolution of a microwave radiometer according to claim 1, characterized in that, In step 1, the sidelobe suppression constraint introduced in the process of solving the composite beam weight is specifically that the sidelobe level of the composite beam is lower than the preset sidelobe threshold. The convex optimization model is solved using a convex optimization algorithm. The initial combined weight coefficients are calculated from the noise covariance, initial regularization parameters, target beam matrix, and antenna pattern matrix offset. The antenna pattern matrix offset is the deviation matrix between the antenna pattern matrix in actual operation and the nominal antenna pattern matrix in ideal design.

5. The method for enhancing the brightness temperature resolution of a microwave radiometer according to claim 1, characterized in that, The adaptive update of the combined weights in step 2 specifically includes the following sub-steps: Sub-step 2.1: Define the main lobe region and side lobe region of the synthesized beam, extract the side lobe energy response value of the side lobe region, and calculate the total side lobe energy based on the side lobe energy response value; Sub-step 2.2: Normalize the sidelobe energy response value, construct an optimization model with the goal of minimizing sidelobe energy, and combine the dynamic adjustment value of the regularization parameter, the antenna pattern matrix and the normalized sidelobe energy response value to solve for the contribution weight corresponding to the sidelobe energy. Sub-step 2.3: The dynamic adjustment value of the regularization parameter is obtained by adaptively updating the initial regularization parameter, the preset growth step size and the current iteration number, and the dynamic adjustment value of the regularization parameter does not exceed the preset upper limit of the regularization parameter.

6. The method for enhancing the brightness temperature resolution of a microwave radiometer according to claim 1, characterized in that, In step 2, the combined weights are updated using a preset fusion ratio. Specifically, the combined weights are updated by fusing the contribution weights corresponding to the main lobe region energy, the total energy of the side lobes, and the contribution weights of the side lobes according to the preset fusion ratio. The contribution weight corresponding to the energy in the main lobe region is calculated by the dynamic adjustment value of the regularization parameter, the antenna pattern matrix, and the synthetic beam parameters obtained from the previous iteration.

7. The method for enhancing the brightness temperature resolution of a microwave radiometer according to claim 1, characterized in that, In step 2, after each iteration of updating the combined weights, the combined weights are weighted and fused with the antenna pattern matrix, and the fusion result is normalized to obtain a new synthetic beam. For the newly obtained synthetic beam, its sidelobe energy change is monitored synchronously, and its resolution error with the preset target beam is calculated. The resolution error is defined as the relative difference between the resolution of the current synthetic beam and the resolution of the target beam. The preset error threshold is 0.1-0.5, which is consistent with the resolution dimension. The resolution error being less than the preset error threshold and the sidelobe energy being within a preset reasonable range are used together as the basis for determining the preset accuracy condition in the iteration termination condition.

8. A microwave radiometer brightness temperature resolution enhancement device, characterized in that, include: The initial weight calculation module is used to acquire preset brightness temperature observation data, antenna pattern and antenna surface projection matrix. Based on the brightness temperature observation data, antenna pattern and antenna surface projection matrix, a convex optimization model with sidelobe suppression constraints is constructed. By solving the convex optimization model, the initial combination weight coefficients of the synthetic beam are obtained. The iterative optimization module is used to iteratively optimize the synthetic beam using the initial combined weight coefficients as the initial values ​​for iteration. In each iteration, the combined weights are adaptively updated based on the energy response of the side lobes outside the main lobe of the synthetic beam until the resolution error between the reconstructed synthetic beam and the target beam meets the preset accuracy condition, and the final combined weight coefficients are obtained. The result output module is used to perform weighted reconstruction processing on the original brightness temperature observation data using the final combined weighting coefficients, and output the brightness temperature resolution enhancement result.

9. A microwave radiometer brightness temperature resolution enhancement device, characterized in that, include: A processor is used to execute computer program instructions; Memory is used to store computer program instructions; When the processor calls the computer program instructions in the memory, it executes the steps of the microwave radiometer brightness temperature resolution enhancement method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by a processor, they implement the steps of the microwave radiometer brightness temperature resolution enhancement method according to any one of claims 1 to 7.

Images