A weather target angle measurement method and system based on multi-strategy optimization

Abstract

Embodiments of the present disclosure provide a weather target angle measurement method and system based on multi-strategy optimization, applied to the technical field of weather target detection. The method comprises: acquiring airborne weather radar echo data; performing target detection on the airborne weather radar echo data, performing de-masking processing on the detection result, and taking the successfully de-masked target as a candidate target; using a preset multi-level clutter area judgment strategy to traverse the candidate target for elimination screening, and retaining valid targets; and outputting angle information of the valid targets. In this way, the false alarm of target detection can be reduced, and the detection performance of the airborne weather radar can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of meteorological target detection technology, and in particular to a meteorological target angle measurement method and system based on a multi-strategy optimization. Background Technology

[0002] With the aviation industry's ever-increasing demands for flight safety and efficiency, airborne weather radars need to achieve precise detection and identification of targets such as storms and turbulence. Traditional angle measurement methods, under complex weather conditions, are easily affected by factors such as beam broadening and target non-uniformity, leading to decreased angle measurement accuracy. This makes it difficult to meet the requirement of accurately distinguishing the boundaries and internal structures of hazardous weather areas, directly restricting the effectiveness of route optimization and turbulence avoidance. Therefore, there is an urgent need to develop a high-precision angle measurement method to improve the reliability of meteorological target parameter extraction and provide more accurate data support for flight decision-making.

[0003] Accurate estimation of meteorological target angles is crucial for subsequent meteorological target identification and inversion. In conventional processing methods, after a target is successfully detected in the time domain, multiple detection results are often weighted and averaged to obtain the target angle. However, during airborne radar look-down, strong ground clutter signals are present in the target echo. Although clutter is suppressed through techniques such as strong clutter cancellation, some clutter and interference that are not completely eliminated can still be detected as false alarms, or be mixed with the target and counted as background noise, reducing the target's signal-to-noise ratio. This results in large fluctuations in the target echo signal-to-noise ratio in the detection results. Using a weighted average method will lead to a decrease in the overall angle estimation accuracy. Summary of the Invention

[0004] This disclosure provides a meteorological target angle measurement method and system based on multi-strategy optimization, which solves the technical problem that traditional angle measurement methods have poor target angle measurement accuracy. This is because when airborne meteorological radar detects meteorological targets, the wide radar beam and strong ground clutter reduce the signal-to-noise ratio of the target echo signal, which has an adverse effect on the target angle measurement.

[0005] According to a first aspect of this disclosure, a method for measuring the angle of a meteorological target based on multi-strategy optimization is provided. The method includes:

[0006] Acquire airborne weather radar echo data;

[0007] Target detection is performed on the airborne weather radar echo data, and the detection results are deblurred. Targets that are successfully deblurred are taken as candidate targets.

[0008] The candidate targets are traversed and filtered using a preset multi-level clutter region judgment strategy, retaining the valid targets.

[0009] Output the angle information of the effective target.

[0010] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes:

[0011] After acquiring the airborne weather radar echo data, the airborne weather radar echo data is subjected to a fast Fourier transform along the azimuth direction to obtain spectral data, and then filtered using a space-time adaptive processing algorithm.

[0012] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes:

[0013] After performing a fast Fourier transform on the airborne weather radar echo data along the azimuth direction to obtain the spectrum data, the corresponding position ranges of the main lobe clutter region and the side lobe clutter region before suppression are calculated based on the airborne state parameters and radar parameters.

[0014] Remove the corresponding position data of the main lobe clutter region and the side lobe clutter region from the spectrum data, and accumulate the amplitude of the updated spectrum data along the azimuth direction to calculate the noise level.

[0015] As described above and in any possible implementation, a further implementation is provided, wherein the target detection of the airborne weather radar echo data and the deblurring of the detection result include:

[0016] Based on the noise level, a signal detection threshold is determined, and a constant false alarm rate (CFAR) detection mechanism is used to filter out meteorological target signals with amplitudes higher than the threshold, thereby eliminating random noise interference.

[0017] The meteorological target signal is deblurred, and the information corresponding to the successfully deblurred target is saved.

[0018] As described above and in any possible implementation, a further implementation is provided, wherein the method of using a preset multi-level clutter region judgment strategy to traverse the candidate targets for elimination and screening, retaining valid targets, includes:

[0019] If all the candidate targets fall within the main lobe clutter region, the target with the highest signal-to-noise ratio is selected as the effective target; if some of the candidate targets fall within the main lobe clutter region, the targets within the main lobe clutter region are removed, and an updated first target set is obtained, and it is determined whether the first target set falls entirely within the side lobe clutter region.

[0020] If the entire first target set falls within the sidelobe clutter region, the target with the highest signal-to-noise ratio is selected as the effective target; if a portion of the first target set falls within the sidelobe clutter region, the targets within the sidelobe clutter region are removed, and the updated second target set is obtained as the effective target.

[0021] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the output of the angle information of the effective target includes:

[0022] If the effective target is a single target, then output its corresponding angular error information;

[0023] If there are multiple valid targets, the weighted average of their angular error information is output; wherein, the valid targets are those that have been filtered and eliminated by a multi-level clutter region judgment strategy.

[0024] According to a second aspect of this disclosure, a high-precision angle measurement system for meteorological targets based on multi-strategy optimization is provided. The system includes:

[0025] The acquisition module is used to acquire echo data from airborne weather radar;

[0026] The processing module is used to perform target detection on the airborne weather radar echo data, perform deblurring on the detection results, and take the successfully deblurred targets as candidate targets.

[0027] The filtering module is used to traverse the candidate targets using a preset multi-level clutter region judgment strategy to eliminate and filter them, and retain the valid targets.

[0028] The generation module is used to output the angle information of the effective target.

[0029] According to a third aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0030] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the methods according to the first and / or second aspects of this disclosure.

[0031] This disclosure first performs Fast Fourier Transform and Space-Time Adaptive Processing (SLTAP) filtering on airborne weather radar echo data to initially suppress strong clutter interference. Then, it accurately calculates the range of the main lobe clutter region and side lobe clutter region before suppression and statistically analyzes the noise level by combining airborne state parameters and radar parameters, providing a reliable priori basis for target detection. Subsequently, it uses a constant false alarm rate (CFAR) detection mechanism to screen meteorological target signals and complete de-ambiguation processing to obtain candidate targets. Then, it adopts a multi-level judgment strategy of first targeting the main lobe clutter region and then the side lobe clutter region to selectively eliminate false alarm targets within the clutter region, avoiding the influence of clutter and interference on the angle measurement results. Finally, it outputs angle information reasonably based on the number of valid targets. This not only solves the technical problems of low angle measurement accuracy and high false alarm rate caused by strong ground clutter and beam broadening in traditional angle measurement methods, but also ensures the ease of implementation and engineering adaptability of the solution through a process-oriented and modular design. It significantly improves the accuracy and reliability of meteorological target angle measurement in complex environments, and can provide accurate data support for the refined detection of targets such as storms and turbulence by airborne weather radar, thereby ensuring the flight safety of aircraft and the effectiveness of route optimization planning.

[0032] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0033] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0034] Figure 1 A flowchart of a meteorological target angle measurement method based on multi-strategy optimization according to an embodiment of the present disclosure is shown;

[0035] Figure 2 A block diagram of a meteorological target angle measurement system based on multi-strategy optimization according to an embodiment of the present disclosure is shown;

[0036] Figure 3 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0038] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0039] This disclosure first performs Fast Fourier Transform and Space-Time Adaptive Processing (SLTAP) filtering on airborne weather radar echo data to initially suppress strong clutter interference. Then, it accurately calculates the range of the main lobe clutter region and side lobe clutter region before suppression and statistically analyzes the noise level by combining airborne state parameters and radar parameters, providing a reliable priori basis for target detection. Subsequently, it uses a constant false alarm rate (CFAR) detection mechanism to screen meteorological target signals and complete de-ambiguation processing to obtain candidate targets. Then, it adopts a multi-level judgment strategy of first targeting the main lobe clutter region and then the side lobe clutter region to selectively eliminate false alarm targets within the clutter region, avoiding the influence of clutter and interference on the angle measurement results. Finally, it outputs angle information reasonably based on the number of valid targets. This not only solves the technical problems of low angle measurement accuracy and high false alarm rate caused by strong ground clutter and beam broadening in traditional angle measurement methods, but also ensures the ease of implementation and engineering adaptability of the solution through a process-oriented and modular design. It significantly improves the accuracy and reliability of meteorological target angle measurement in complex environments, and can provide accurate data support for the refined detection of targets such as storms and turbulence by airborne weather radar, thereby ensuring the flight safety of aircraft and the effectiveness of route optimization planning.

[0040] Figure 1 A flowchart of a meteorological target angle measurement method 100 based on multi-strategy optimization according to an embodiment of the present disclosure is shown. Figure 1 As shown, method 100 includes:

[0041] S101, acquire airborne weather radar echo data.

[0042] In some embodiments, after acquiring airborne weather radar echo data, the airborne weather radar echo data is subjected to fast Fourier transform along the azimuth direction to obtain spectral data, and then filtered using a space-time adaptive processing algorithm.

[0043] Specifically, the raw echo data within the detection range is collected by the receiving system of an airborne weather radar. This data includes echo signals from meteorological targets (such as storms and turbulence), clutter signals, and various electromagnetic interference signals. After acquiring the raw echo data, it undergoes two preprocessing steps: First, a Fast Fourier Transform (FFT) is performed on the raw echo data along the azimuth direction, converting the time-domain echo data into spectral data, thus realizing the conversion of the signal from the time domain to the frequency domain, facilitating accurate differentiation of the frequency characteristics of target signals, clutter signals, and interference signals. Second, the Space-Time Adaptive Processing (STAP) algorithm is used to filter the converted spectral data. This algorithm can utilize the joint information of the spatial and temporal domains to specifically suppress strong ground clutter and some electromagnetic interference, significantly reducing the masking effect of clutter on target signals and improving the signal-to-noise ratio of the target echo signal.

[0044] In some embodiments, after performing a fast Fourier transform on the airborne weather radar echo data along the azimuth direction to obtain the spectrum data, the corresponding position ranges of the main lobe clutter region and the side lobe clutter region before suppression are calculated based on the airborne state parameters and radar parameters.

[0045] Remove the corresponding location data of the main lobe clutter region and side lobe clutter region from the spectrum data, and then accumulate the amplitude of the updated spectrum data along the azimuth direction to calculate the noise level.

[0046] Specifically, based on aircraft motion parameters (including but not limited to aircraft speed, flight altitude, and attitude angle) and radar parameters (including but not limited to radar beam pointing angle), a preset algorithm is used to accurately calculate and store the boundary data of the position range corresponding to the main lobe clutter region and side lobe clutter region before suppression. Then, based on the stored boundary data of the main lobe clutter region and side lobe clutter region, all data corresponding to these two clutter regions are precisely removed from the spectrum data obtained through Fast Fourier Transform, resulting in updated spectrum data after clutter interference removal. Finally, the updated spectrum data is amplitude-accumulated along the azimuth direction. By statistically analyzing the distribution characteristics of the accumulated signal amplitude, the background noise level under the current detection environment is determined. This noise level will serve as the basis for setting the signal detection threshold in subsequent target detection processes, ensuring the accuracy of target detection.

[0047] S102, target detection is performed on the airborne weather radar echo data, the detection results are deblurred, and the successfully deblurred targets are taken as candidate targets.

[0048] In some embodiments, a signal detection threshold is determined based on the noise level, and a constant false alarm rate (CFAR) detection mechanism is used to screen out meteorological target signals with amplitudes higher than the threshold and eliminate random noise interference.

[0049] The meteorological target signal is deblurred, and the information corresponding to the successfully deblurred target is saved.

[0050] Specifically, based on the statistical background noise level, a signal detection threshold is determined through a preset algorithm. This threshold must balance the detection rate of the target signal with its anti-interference capability, ensuring that effective meteorological target signals are captured while random noise is filtered out. Subsequently, a constant false alarm rate (CFAR) detection mechanism is used to traverse the spectrum data after STAP filtering, selecting signals with amplitudes higher than the detection threshold as candidate meteorological target signals and directly eliminating random noise interference with amplitudes lower than the threshold, thus ensuring the reliability of the initial screening results.

[0051] In some embodiments, because airborne weather radar often uses low-repetition-weighted waveform detection, target echo signals may exhibit range or Doppler folding, leading to ambiguity in the detected target information and affecting the accuracy of angle measurements. Therefore, after target detection, the selected meteorological target signals need to be de-ambigued. By analyzing features such as signal phase and frequency, the true range, velocity, and other information of the target can be restored. For targets that successfully undergo de-ambiguation, key parameters such as range gate, Doppler gate, power, signal-to-noise ratio, azimuth error, and elevation error are recorded in detail to form a candidate target dataset, which is then stored. This dataset will serve as the core processing object for subsequent clutter removal and screening. Signals that fail to undergo de-ambiguation are determined to be invalid interference and discarded directly to avoid negatively impacting subsequent angle measurement results.

[0052] S103: The preset multi-level clutter region judgment strategy is used to traverse the candidate targets for elimination and screening, and retain the valid targets.

[0053] In some embodiments, if all candidate targets fall within the main lobe clutter region, the target with the highest signal-to-noise ratio is selected as the effective target; if some candidate targets fall within the main lobe clutter region, the targets within the main lobe clutter region are removed, and an updated first target set is obtained, and it is determined whether the first target set falls entirely within the side lobe clutter region.

[0054] If the entire first target set falls within the sidelobe clutter region, the target with the highest signal-to-noise ratio is selected as the effective target; if a portion of the first target set falls within the sidelobe clutter region, the targets within the sidelobe clutter region are removed, and the updated second target set is obtained as the effective target.

[0055] Specifically, the initial screening is performed in the main lobe clutter region. All successfully deblurred targets in the candidate target dataset are traversed, and the position information of each target is compared with the boundary of the main lobe clutter region to determine whether the target falls within the main lobe clutter region before suppression. If all candidate targets fall within the main lobe clutter region, it indicates that the effective target signal and the main lobe clutter have a very high overlap under the current detection environment. In this case, the target with the highest signal-to-noise ratio in the dataset is selected as the effective target, and its corresponding angle-related information will be used for subsequent output. If some candidate targets fall within the main lobe clutter region, these targets in the clutter region are completely removed from the candidate target dataset to obtain the updated first target set, which is then used for side lobe clutter region judgment. If none of the candidate targets fall within the main lobe clutter region, the candidate target dataset is directly used as the first target set (Data_Ublur1) and used for side lobe clutter region judgment.

[0056] Sidelobe clutter region judgment and secondary screening: The targets in the first target set are traversed and judged to check whether each target falls within the sidelobe clutter region. If all targets in the first target set fall within the sidelobe clutter region, the target with the highest signal-to-noise ratio in the set is selected as the effective target, ensuring that the target with the best signal quality is retained first in the clutter environment; if some targets in the first target set fall within the sidelobe clutter region, the targets in this part of the clutter region are removed, and an updated second target set is obtained. This set is the final effective target set after two layers of clutter region screening; if none of the targets in the first target set fall within the sidelobe clutter region, the first target set is directly used as the final effective target set, ensuring that all real targets that are not affected by clutter interference are retained.

[0057] S104 outputs the angle information of the valid target.

[0058] In some embodiments, if the effective target is a single target, its corresponding angular error information is output;

[0059] If there are multiple valid targets, the weighted average of their angular error information is output; where the valid targets are those that have been filtered and eliminated by a multi-level clutter region judgment strategy.

[0060] Specifically, after multi-level judgment and screening, high-reliability targets are retained. If there is only one valid target, the angular error information (azimuth error and elevation error) corresponding to that target is directly extracted and used as the final meteorological target angle information output. If there are multiple valid targets, the weighting weight is determined based on the key parameters such as signal-to-noise ratio and power recorded for each target. The angular error information (azimuth error and elevation error) of all valid targets is calculated by weighted averaging, and the calculation result is used as the final meteorological target angle information output.

[0061] The following is a detailed description of a meteorological target angle measurement method 100 based on multi-strategy optimization provided by this disclosure, with reference to a specific embodiment:

[0062] The flight speed was set at 450 km / h, the altitude at 12 km, and the attitude angle stabilized at 0°. The airborne weather radar used a low repetition rate detection waveform, transmitting a 5-fold waveform per wave position, with a radar beam pointing angle of 30°. The target was a storm weather target at a distance of 105 km. Raw echo data (Data1) was collected through the radar receiving system. This data included storm target echo signals, strong ground clutter signals, and a small amount of electromagnetic interference signals. The data sampling rate was 10 MHz, with 200 range gates and 64 Doppler gates.

[0063] A Fast Fourier Transform (FFT) is performed along the azimuth direction on the original echo data Data1 to convert the time-domain signal into spectral data Data_PC. The transform has 256 points, yielding spectral data in the frequency range of 0-5MHz, thus achieving frequency domain separation between the target signal and clutter signals. The Space-Time Adaptive Processing (STAP) algorithm is then used to filter the spectral data Data_PC. The algorithm is of order 8, with a suppression gain of 30dB, specifically suppressing strong ground clutter and some broadband interference. The filtered data Data2 is then output, at which point the signal-to-noise ratio of the target signal in the filtered data is improved by approximately 15dB compared to the original data.

[0064] Based on the aircraft's motion parameters (speed 450 km / h, altitude 12 km, attitude angle 0°) and radar parameters (beam pointing angle 30°, wavelength 0.03 m), the boundary data of the main lobe clutter region (Data_MbClutter, range gate range 50-60) and the boundary data of the side lobe clutter region (Data_SdClutter, range gate range 65-75) before suppression were calculated using geometric relationships and the Doppler frequency shift formula, and the boundary data were stored in the buffer. The position data corresponding to range gates 50-60 (main lobe clutter region) and 65-75 (side lobe clutter region) were precisely removed from the spectrum data Data_PC to obtain updated spectrum data after clutter removal. The updated spectrum data was then summed along the azimuth direction, and the background noise was statistically analyzed to obtain a mean of -80 dBm and a variance of 2 dBm. This statistical result served as the basis for setting the subsequent target detection threshold.

[0065] Based on the above statistical noise levels (mean -80dBm, variance 2dBm), the constant false alarm rate (CFAR) algorithm is used to set the signal detection threshold to -74dBm (false alarm rate controlled within 10%). -6To ensure effective target signal capture while filtering out random noise interference, the filtered data Data2 was thoroughly inspected, selecting signals with amplitudes higher than -74dBm as candidate meteorological target signals. Four candidate signals were detected, and random noise interference with amplitudes below the threshold was eliminated, initially ensuring the reliability of the target signals. Because the radar uses low-repetition-rate waveforms, the candidate target signals exhibited range-dimensional folding ambiguity. A phase-coherent deblurring algorithm was used to process the four candidate signals to restore the true target range information. Ultimately, all four candidate signals were successfully deblurred, and the key parameters corresponding to each target were recorded.

[0066] Target 1: Range gate 40, Doppler gate 12, power -65dBm, signal-to-noise ratio 25dB, azimuth error -0.078°, pitch error -0.042°;

[0067] Target 2: Range gate 55, Doppler gate 15, power -68dBm, signal-to-noise ratio 18dB, azimuth error -0.627°, pitch error -0.315°;

[0068] Target 3: Range gate 70, Doppler gate 13, power -66dBm, signal-to-noise ratio 20dB, azimuth error -0.274°, pitch error -0.138°;

[0069] Target 4: Range gate 80, Doppler gate 14, power -63dBm, signal-to-noise ratio 28dB, azimuth error -0.055°, pitch error -0.036°;

[0070] Create and store the candidate target dataset Data_Ublur.

[0071] Main lobe clutter region identification and preliminary screening: The main lobe clutter region boundary Data_MbClutter (range gate 50-60) is called, and the four targets in the candidate target dataset Data_Ublur are traversed: Target 2 has a range gate of 55, falling within the main lobe clutter region; the range gates of the other three targets (1, 3, and 4) are all outside this range, representing a case where some targets fall within the main lobe clutter region. The relevant information for target 2 is removed, resulting in the updated first target set Data_Ublur1 (containing targets 1, 3, and 4).

[0072] Sidelobe clutter region judgment and secondary filtering: The sidelobe clutter region boundary Data_SdClutter (range gate 65-75) is called to judge the three targets in the first target set Data_Ublur1: the range gate of target 3 is 70, which falls within the sidelobe clutter region; the range gates of the other two targets (1 and 4) are not within this range, which belongs to the case where some targets fall within the sidelobe clutter region. The relevant information of target 3 is removed to obtain the updated second target set Data_Ublur2 (containing targets 1 and 4), which is the final effective target set retained after two layers of clutter region filtering.

[0073] The second target set, Data_Ublur2, contains two valid targets (targets 1 and 4), representing a case with multiple targets. A weighted average is assigned based on the signal-to-noise ratio (SNR) of the two targets, calculated as "SNR of a single target / SNR of all valid targets".

[0074] The weight of objective 1 = 25dB / (25dB + 28dB) ≈ 0.471;

[0075] The weight of objective 4 = 28dB / (25dB+28dB) ≈ 0.529;

[0076] The weighted average of the azimuth and elevation errors of the two targets is calculated separately:

[0077] The final azimuth error = 0.471 × (-0.078°) + 0.529 × (-0.055°) ≈ -0.0665°;

[0078] The final pitch angle error = 0.471 × (-0.042°) + 0.529 × (-0.036°) ≈ -0.039°;

[0079] The calculated final azimuth error (-0.0665°) and pitch error (-0.039°) are used as the angle information of the meteorological target and output in digital signal form to the display system and flight decision support system of the airborne meteorological radar. The output delay is less than 10ms, which meets the real-time requirements.

[0080] According to the embodiments of this disclosure, the problems of low angle measurement accuracy and high false alarm rate caused by strong ground clutter and signal folding in the traditional weighted averaging method are effectively solved, which significantly improves the accuracy of meteorological target angle measurement. At the same time, it has the advantages of simple operation, easy engineering implementation and low processing delay. It successfully breaks through the application limitations of accurate angle measurement in strong clutter environment, and provides highly reliable data support for the fine detection of targets such as storms and turbulence by airborne meteorological radar, effectively ensuring the safety of aircraft flight and the effectiveness of route optimization planning.

[0081] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0082] The above is an introduction to the method embodiments. The following describes the present disclosure further through device embodiments.

[0083] Figure 2 A block diagram of a high-precision angle measurement system 200 for meteorological targets based on multi-strategy optimization, according to an embodiment of the present disclosure, is shown. Figure 2 As shown, system 200 includes:

[0084] The acquisition module 201 is used to acquire airborne weather radar echo data.

[0085] The processing module 202 is used to perform target detection on the airborne weather radar echo data, perform deblurring on the detection results, and take the successfully deblurred targets as candidate targets.

[0086] The filtering module 203 is used to traverse candidate targets and filter them using a preset multi-level clutter region judgment strategy, retaining valid targets.

[0087] The generation module 204 is used to output the angle information of the valid target.

[0088] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0089] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0090] Figure 3 A schematic block diagram of an electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0091] Electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes according to a computer program stored in ROM 302 or a computer program loaded into RAM 303 from storage unit 308. RAM 303 can also store various programs and data required for the operation of electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via bus 304. I / O interface 305 is also connected to bus 304.

[0092] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0093] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).

[0094] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0095] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0096] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0097] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0098] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0099] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0100] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0101] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A meteorological target angle measurement method based on multi-strategy optimization, characterized in that, include: The process involves acquiring airborne weather radar echo data. Specifically, after acquiring the airborne weather radar echo data, a fast Fourier transform (FFT) is performed along the azimuth direction to obtain spectral data, which is then filtered using a space-time adaptive processing algorithm. After obtaining the spectral data through the FFT, the corresponding position ranges of the main lobe clutter region and side lobe clutter region before suppression are calculated based on airborne state parameters and radar parameters. The corresponding position data of the main lobe clutter region and side lobe clutter region are removed from the spectral data obtained through the FFT, and the updated spectral data is then summed along the azimuth direction to statistically analyze the noise level. Target detection is performed on the airborne weather radar echo data, and the detection results are deblurred. Targets that are successfully deblurred are taken as candidate targets. Specifically, a signal detection threshold is determined based on the noise level, and a constant false alarm rate (CFAR) detection mechanism is used to traverse and detect the spectrum data filtered by the space-time adaptive processing algorithm to screen out meteorological target signals with amplitudes higher than the threshold and eliminate random noise interference; the meteorological target signals are deblurred, and the information corresponding to the successfully deblurred targets is saved; The candidate targets are traversed and filtered using a preset multi-level clutter region judgment strategy to retain valid targets. Specifically, if all candidate targets fall within the main lobe clutter region, the target with the highest signal-to-noise ratio is selected as the valid target; if some candidate targets fall within the main lobe clutter region, targets within the main lobe clutter region are removed to obtain an updated first target set, and it is determined whether the first target set falls entirely within the sidelobe clutter region; if the first target set falls entirely within the sidelobe clutter region, the target with the highest signal-to-noise ratio is selected as the valid target; if some of the first target set falls within the sidelobe clutter region, targets within the sidelobe clutter region are removed to obtain an updated second target set as the valid target. Output the angle information of the effective target.

2. The method according to claim 1, characterized in that, The angle information of the effective target output includes: If the effective target is a single target, then output its corresponding angular error information; If there are multiple valid targets, the weighted average of their angular error information is output; wherein, the valid targets are those that have been filtered and eliminated by a multi-level clutter region judgment strategy.

3. A high-precision angle measurement system for meteorological targets based on multi-strategy optimization, characterized in that, The system is used to perform the method described in any one of claims 1-2, including: The acquisition module is used to acquire echo data from airborne weather radar; The processing module is used to perform target detection on the airborne weather radar echo data, perform deblurring on the detection results, and take the successfully deblurred targets as candidate targets. The filtering module is used to traverse the candidate targets using a preset multi-level clutter region judgment strategy to eliminate and filter them, and retain the valid targets. The generation module is used to output the angle information of the effective target.

4. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1-2.

5. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method described in any one of claims 1-2.

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