Micro-service intelligent airspace anti-interference method

Abstract

The invention discloses a micro-service intelligent airspace anti-interference method, and relates to the crossing field of array signal processing and artificial intelligence. The method comprises the following steps: firstly, sending historical interference coming direction and demand beam pointing data through a client; then, in the server side, carrying out differential processing on historical interference coming direction data, and obtaining a potential interference coming direction range; secondly, a target function is constructed in combination with required beam pointing transmitted by a client side, and an antenna array element phase is optimized through an improved quasi-annealing algorithm; and finally, returning the optimized antenna array element phase to a client to form an antenna pattern capable of ensuring main lobe pointing and suppressing interference, thereby realizing intelligent airspace anti-interference. According to the method, flexible acquisition of'central calculation and distributed use 'of the antenna array element phase is achieved in a mode of constructing the client and the server. In addition, balance is achieved in precise point prediction and full sidelobe suppression, and a better beam forming effect is achieved in the anti-interference aspect.

Description

Technical Field

[0001] The present invention discloses a microservices-based intelligent airspace anti-jamming method, belonging to the cross field of array signal processing and artificial intelligence. Background Art

[0002] With the continuous maturity of array antenna technology, frequency-using equipment has widely adopted array antennas to achieve high gain and strong anti-jamming capabilities. By modulating different phases for each element of the array antenna, it is possible to achieve "nulling" of the interference direction while ensuring the main lobe pointing, thereby realizing airspace anti-jamming.

[0003] In the patent "A Hybrid Coherent Interference Suppression Beamforming Method under Impulse Noise Background" applied by Sun Yat-sen University with the application number CN202410646145.2, the non-linear compression processing and spatial decomposition coherence technology are combined, and the minimum variance distortionless response criterion is taken as the goal to achieve optimal beamforming. On the basis of realizing stable anti-jamming capabilities, the problem of poor robustness in the beamforming process is improved. However, this method has more implementation steps, and the implementation of each step requires a large amount of professional knowledge and domain background, and a paradigm initial process cannot be formed.

[0004] In the patent "A Digital Beamforming Terminal Device for Satellite Communication" applied by the 54th Research Institute of China Electronics Technology Group Corporation with the publication number CN111030748B, digital beamforming technology is used to zero the antenna gain at the interference direction, thereby realizing airspace anti-jamming. However, this patent does not consider the change of dynamic interference direction, and the delay in obtaining interference direction information or beamforming will cause the null point to be unable to accurately align with the interference direction.

[0005] In the article "Adjacent Target Vital Sign Detection Based on Adaptive Beamforming Technology" published by Xiong Junjun et al. in "Radar Science and Technology", an adaptive beamforming method is proposed to form nulls in the direction of adjacent interference, avoid adjacent interference, and improve the target life detection effect. The application background of this method also does not involve the impact of dynamic interference direction on beamforming.

[0006] In the article "Interference Suppression in the Worst Environment Based on Frequency Control Array Radar" published by Tao Xinke et al. in "Applied Science and Technology", a worst performance optimization algorithm is proposed to weaken the sidelobe energy while forming a point beam to achieve interference suppression, and it is applicable to uniform linear arrays and circular frequency control arrays, with strong engineering application capabilities. However, this method aims to overall suppress the sidelobe gain, uses less interference direction information, and has insufficient precise anti-jamming capabilities.

[0007] In addition, current anti-jamming methods are mainly embedded in the system by tightly coupled methods, with poor ability reusability and inconvenient modification. Summary of the Invention

[0008] The present invention provides a microservices-based intelligent airspace anti-jamming method, aiming to flexibly respond to dynamic interference and changes by constructing microservices. By differentiating the collected historical interference direction data, the interference direction range is obtained, and the objective function is constructed in combination with the main lobe pointing. Through an improved simulated annealing algorithm, the antenna beam gain in the interference direction area is reduced, thereby achieving the purpose of airspace anti-jamming.

[0009] A microservices-based intelligent airspace anti-jamming method includes the following steps: Step 1: Send the historical interference direction data and the required beam pointing parameters of the array antenna to the server through the client; Step 2: In the server, perform differential processing on the historical interference direction data, obtain the potential interference direction range, construct the objective function in combination with the required beam pointing passed in by the client, optimize the phase of the array antenna elements through an improved simulated annealing algorithm, and send the optimized phase of the array antenna elements back to the client; Step 3: The client adjusts the array antenna to form an antenna pattern using the optimized phase of the array antenna elements.

[0010] Furthermore, the specific method of Step 1 is as follows: Step 1-1: Import the requests toolkit; Step 1-2: Interference direction data collection: Using the array antenna as the origin, continuously collect the interference direction observation data at T moments Save it as a list, where ; Step 1-3: Construct the anti-jamming requirement dictionary anti_jamming_dict, including the historical interference direction data history_data, the required beam pointing theta_target, the element spacing d, and the number of elements N; Step 1-4: Set the url address of the microservice, and send the anti-jamming requirement dictionary anti_jamming_dict to the microservice through the function in the requests package requests.post(url, json=anti_jamming_dict).

[0011] Furthermore, the specific method of Step 2 is as follows: Step 2-1: Import the Flask, request, and jsonify functions from the flask toolkit; Step 2-2: Define the function compute_anti_jamming_parameters; Step 2-3: Generate a Flask framework application, app = Flask(__name__), and bind the function compute_anti_jamming_parameters to the routing address, @app.route(' / compute_anti_jamming_parameter', methods=['POST']); Step 2-4: Start the microservice; Step 2-5: Obtain the anti-jamming requirement dictionary anti_jamming_dict transmitted by the client through request.get_json(); Step 2-6: Perform differencing processing on the historical interference incoming data history_data in the anti-jamming requirement dictionary anti_jamming_dict to obtain the difference value , at this time ; Step 2-7: Obtain the maximum difference value in the difference values ; Step 2-8: Obtain the current perceived interference incoming direction , and obtain the interference incoming direction range as ; Using as the resolution, discretely sample the interference incoming direction range to obtain the interference incoming direction list : ; Where , if is a positive integer, then ; Otherwise ; represents rounding down; Step 2-9: Initialize the candidate array antenna element phase vector, which is composed of the phases of N array antenna elements, and the values of the phases of the N array antenna elements are all random numbers within the range of ; Step 2-10: Construct the anti-jamming objective function object_function; the inputs are the element spacing d, the number of elements N, the required beam pointing , the interference incoming direction list , the array antenna element phase vector in the current iteration process, and the maximum difference value ; The output is the objective function value : ; Wherein, is the array antenna element phase vector in the current iteration process The phases of N array antenna elements therein; set the fitness value to be updated and the optimal antenna element phase vector , The initial value of is to initialize the candidate array antenna element phase vector, The initial value of is the output objective function value obtained by substituting the initialized candidate array antenna element phase vector into the anti-interference objective function object_function; Step 2-11: Set the temperature value and the final temperature value ; > ; Step 2-12: For the array antenna element phase vector in the current iteration process , add uniformly distributed random numbers in the range of to each array antenna element phase respectively, and update the numerical value of the array antenna element phase vector ; Step 2-13: Calculate the objective function value corresponding to the superimposed array antenna element phase vector value ; Step 2-14: If the new objective function value is greater than the fitness value to be updated or , where is a random number between [0,1], then assign the new objective function value to the fitness value to be updated , and assign the numerical value of the array antenna element phase vector to the optimal antenna element phase vector ; otherwise, keep the fitness value to be updated and the optimal antenna element phase vector unchanged; Step 2-15: Repeat Step 2-12 to Step 2-14 for a total of L times; Step 2-16: Update the temperature value , where is the cooling rate; Step 2-17: If the temperature value is greater than or equal to the final temperature value , then return to Step 2-12; if the temperature value is less than the final temperature value , then end the optimization and record the final optimal antenna element phase vector ; Step 2-18: Use the jsonify function to process the final optimal antenna element phase vector and use it as the return value of the function compute_anti_jamming_parameters.

[0012] Further, the specific method of step 3 is as follows: requests.post(url, json=anti_jamming_dict) will return response. After processing it through.json(), the final optimal antenna element phase vector can be obtained on the client side and set the phase parameters of each antenna element to form an antenna pattern.

[0013] Due to the above technical solutions, the method of the present invention has the following advantages compared with the background technology: 1. The present invention obtains the interference incoming direction area through differential processing. Considering that the beamforming of the array antenna and the positioning of the interference source will cause time delay, it is easy to cause the attenuation area not to be aligned with the interference incoming direction, resulting in a decrease in the anti-interference effect. And directly suppressing the side lobes outside the main lobe requires a relatively high difficulty for the beamforming algorithm, and the suppression is not concentrated. By estimating the interference incoming direction area, the probability of the interference incoming direction in the gain suppression area is increased, achieving a balance between accurate point prediction and full side lobe suppression.

[0014] 2. The present invention adopts an improved simulated annealing optimization method. By improving the annealing rule, the temperature does not continuously decrease, but satisfies the sine function rule and has a temperature increase phenomenon during the decreasing process, thereby enhancing the probability of the simulated annealing algorithm jumping out of the local optimal value, improving the ability to obtain the global optimal value, and making the beamforming effect in anti-interference more excellent.

[0015] 3. The present invention provides intelligent airspace anti-interference capabilities through a microservices framework. By hosting the core anti-interference method using a microservices framework and modularizing and independently processing the capabilities, devices with requirements in the network can all access through the server side, thereby obtaining the optimized antenna element phase and obtaining intelligent airspace anti-interference capabilities. Description of the Drawings

[0016] Figure 1 It is a method flow chart of a microservices-based intelligent airspace anti-interference method in an embodiment of the present invention.

[0017] Figure 2 It is a schematic diagram of interference incoming direction observation data in an embodiment of the present invention.

[0018] Figure 3 It is a differential schematic diagram of interference incoming direction observation data in an embodiment of the present invention.

[0019] Figure 4This is the variation of the optimal value with temperature in the embodiments of the present invention.

[0020] Figure 5 This is a schematic diagram of the comparison of antenna radiation patterns in the embodiments of the present invention. Detailed implementation manners

[0021] The present invention will be further described below in conjunction with the accompanying drawings and specific implementation manners.

[0022] An intelligent airspace anti-jamming method based on microservices, the overall process of which is as Figure 1 shown. Specifically, it includes the following steps: Step 1: Send historical interference direction data and parameters such as the required beam pointing to the server through the client; Among them, the client refers to the phased array antenna, and the server refers to the computing and processing center. The client puts forward the required parameters for optimizing the phased array antenna. After the server completes the optimization of the antenna beam, it returns the phases of each antenna to the client, achieving the flexible acquisition of "centralized calculation and distributed use" of the phases of the antenna array elements; Step 1-1: Import the requests toolkit; Step 1-2: Interference direction data collection; taking the array antenna as the origin, continuously collect the interference direction observation data at T moments and save it as a list, where , T is taken as 100, as Figure 2 shown.

[0023] Step 1-3: Construct an anti-jamming requirement dictionary anti_jamming_dict, including historical interference direction data history_data, required beam pointing theta_target, element spacing d, and number of elements N.

[0024] anti_jamming_dict = {'history_data': [-49.05401933 - 53.00770256 -50.09575992 - 54.90413872 - 58.69484854 - 60.69905881 - 56.35090798 - 56.18562937 - 55.13574698 -53.80290047 - 57.51515798 - 53.27817762 - 57.3564365 - 55.64489685 - 51.3912332 - 51.75682654 - 54.7452387 - 58.7875423 - 63.50236881 - 63.83478632 - 68.46184085 - 67.49060189 - 67.37568536 - 68.48579776 - 70.4616519 - 69.38602106 - 72.14030558 - 69.03715136 - 67.64552818 - 67.99037603 - 63.21392391 - 59.16183118 - 54.25211866 - 50.25031746 - 46.84548763 - 48.68349035 - 46.91765144 - 51.47297335 - 52.09212519 - 47.8605501 - 50.80578655 - 46.0973724 - 48.86001672 - 50.30728429 - 47.82087116 - 47.29617171 - 42.43492919 - 45.59434953 - 45.07799935 - 40.83129651 - 43.5535794 - 45.7176746 - 44.31950188 - 39.45971212 - 34.52068327 - 30.76475617 - 26.90702218 - 29.77809234 - 25.50468986 - 24.15166433 - 28.84765781 - 28.3012196 - 33.14057907 - 28.62813308 - 30.91025464 - 29.97427368 - 30.75257854 - 32.61891927 - 27.63111511 - 23.29666842 - 28.26180665 - 28.38007384 - 25.73529672 - 27.07890262 - 29.72888276 - 27.66953931 - 26.59776839 - 30.82254258 - 27.9638306 - 25.94469437 - 22.35258757 - 23.92826514 - 28.45733605 - 26.22554607 - 25.16568161 - 25.0898955 - 21.31000726 - 17.16158893 - 21.95933163 - 23.38276178 - 24.86843994 - 23.72825611 - 19.44301989 - 21.94404244 - 23.22363348 - 26.96105628 - 29.56431569 - 26.10967205 - 23.82280526 - 27.81776342 ], 'theta_target': 30, 'd': 0.5, 'N': 32,} Step 1 - 4: Set the URL address of the microservice to "http: / / 127.0.0.1:5000 / compute_anti_jamming_parameters", and send the anti - jamming requirement dictionary anti_jamming_dict to the microservice through the function in the requests package requests.post(url, json = anti_jamming_dict).

[0025] Step 2: On the server side, perform differential processing on historical interference data to obtain the potential interference direction range. Combine the beam pointing passed in by the client to construct an objective function, optimize the phase of the antenna array elements through an improved simulated annealing algorithm, and send the optimized phase of the antenna array elements back to the client; Step 2 - 1: Import the Flask, request, and jsonify functions from the flask toolkit; Step 2 - 2: Define the function compute_anti_jamming_parameters, Step 2-3: Generate a Flask framework application, app = Flask(__name__), and bind the function compute_anti_jamming_parameters to the routing address, @app.route(' / compute_anti_jamming_parameters', methods=['POST']); Step 2-4: Start the microservice; if __name__ == '__main__': app.run(host='0.0.0.0', port=5000, debug=True) Step 2-5: Obtain the anti-jamming requirement dictionary anti_jamming_dict transmitted by the client through request.get_json(); Step 2-6: Perform differential processing on the historical interference incoming data history_data in the anti-jamming requirement dictionary anti_jamming_dict to obtain the difference value At this time For example Figure 3 as shown; Step 2-7: Obtain the maximum difference value in the difference values = 4.97°; Step 2-8: Obtain the current perceived interference incoming direction to obtain an interference incoming direction range of . Taking as the resolution and taking 1°, discretely sample the interference incoming direction range, and the interference incoming direction list can be expressed as ; where if is a positive integer, then ; otherwise ; represents rounding down; Step 2-9: Construct the anti-jamming objective function object_function. The inputs are the preset pointing direction , the interference incoming direction list , the antenna array element phase vector and the maximum difference value . The antenna array element phase vector is composed of the phases of each antenna array element, where , N is the number of array elements, taking 32. The output value is ; where d is the antenna element spacing, taking 0.5 m; Step 2-10: Initialize the candidate antenna element phase vector , which is composed of the phases of each antenna element, and the value range is . Substitute the preset pointing direction = 30°, the interference direction list = [-70°, …, -60°], the antenna element phase vector and the maximum difference value into the anti-interference objective function to obtain the fitness value . In addition, assign the fitness value to the optimal fitness value , and assign the candidate antenna element phase vector to the optimal antenna element phase vector ; Step 2-11: Set the temperature value = 1000 and the final temperature value = 1; Step 2-12: Add a uniformly distributed random number in the range of to the phase of each antenna element in the array antenna element phase vector in the current iteration process, and update the value of the array antenna element phase vector ; Among them, if it is the first iteration process, then is the initialized candidate antenna element phase vector ; Step 2-13: Calculate the objective function value of the superposed array antenna element phase vector value and update ; Step 2-14: If the new objective function value is greater than the optimal fitness value or , where is a random number between [0, 1], then assign the new objective function value to the optimal fitness value , and assign the value of the candidate antenna element phase vector to the optimal antenna element phase vector ; otherwise, keep the fitness value to be updated and the optimal antenna element phase vector unchanged; Step 2-15: Repeat Step 2-12 to Step 2-14 for a total of L times, taking 1000 times; Step 2-16: Update the temperature value , where is the cooling rate, taking 0.9; Step 2-17: If the temperature value is greater than or equal to the final temperature value , then return to Step 2-8; if the temperature value is less than the final temperature value , then end the optimization, record the final optimal antenna element phase vector , as Figure 4 shown; Step 2-18: After processing the final optimal antenna element phase vector using the jsonify function, use it as the return of the function compute_anti_jamming_parameters.

[0026] Step 3: Use the optimized antenna element phases to form an antenna pattern.

[0027] From requests.post(url, json=anti_jamming_dict) will return response, after processing through.json(), the optimal antenna element phase vector can be obtained on the client side , set the phase parameters of each antenna element to form an antenna pattern, as Figure 5 shown.

[0028] Compare this method with the traditional simulated annealing method, the variation of the optimal value with temperature is as Figure 4 shown. It can be seen that when the temperature reaches the termination, the optimal value of the antenna pattern formed by the improved method is higher. The antenna pattern formed by the optimal antenna element phase vector is as Figure 5 shown. It is obvious that the antenna pointing gain formed by this method is higher, and the width of the depression formed in the interference direction is wider than that formed by the traditional simulated annealing method.

[0029] In summary, it can be seen that this patented technology can intelligently implement anti-jamming beamforming.

[0030] Those skilled in the art will realize that the described embodiments are for helping readers understand the principles of the present invention, and it should be understood that the protection scope of the present invention is not limited to the described embodiments. For those skilled in the art, various changes and modifications can be made to the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. An intelligent airspace anti-interference method based on microservices, characterized in that It includes the following steps: Step 1: Send the historical interference direction data of the array antenna and the demand beam pointing parameters to the server through the client; Step 2: In the server, perform differential processing on the historical interference direction data to obtain the potential interference direction range, construct an objective function by combining the demand beam pointing passed in by the client, optimize the phases of the array antenna elements through an improved simulated annealing algorithm, and return the optimized phases of the array antenna elements to the client; Step 3: The client adjusts the array antenna to form an antenna pattern using the optimized phases of the array antenna elements.

2. The intelligent airspace anti-interference method with microservices according to claim 1, characterized in that The specific method of Step 1 is as follows: Step 1-1: Import the requests toolkit; Step 1-2: Interference direction data collection: Using the array antenna as the origin, continuously collect the interference direction observation data at T moments and save them as a list, where ; ; Step 1-3: Construct an anti-jamming requirement dictionary anti_jamming_dict, including historical interference direction data history_data, demand beam pointing theta_target, element spacing d, and the number of elements N; Step 1-4: Set the url address of the microservice, and send the anti-jamming requirement dictionary anti_jamming_dict to the microservice through the function in the requests package requests.post(url, json = anti_jamming_dict).

3. The microservice-based intelligent airspace anti-jamming method according to claim 2, characterized in that, The specific method of Step 2 is as follows: Step 2-1: Import the Flask, request, and jsonify functions from the flask toolkit; Step 2-2: Define the function compute_anti_jamming_parameters; Step 2-3: Generate a flask framework application, app = Flask(__name__), and bind the function compute_anti_jamming_parameters to the routing address, @app.route(' / compute_anti_jamming_parameter', methods=['POST']); Step 2-4: Start the microservice; Step 2-5: Obtain the anti-jamming requirement dictionary anti_jamming_dict transmitted by the client through request.get_json(); Step 2-6: Perform differential processing on the historical interference incoming data history_data in the anti-jamming requirement dictionary anti_jamming_dict to obtain the difference value , at this time ; Step 2-7: Obtain the maximum difference value among the difference values ; Step 2-8: Obtain the current perceived interference direction , and obtain that the interference direction range is ; Using as the resolution, discretely sample the interference direction range to obtain an interference direction list : ； Among them If is a positive integer, then ; Otherwise ; represents rounding down; Step 2-9: Initialize the phase vector of the candidate array antenna elements. The phase vector of the candidate array antenna elements consists of the phases of N array antenna elements, and the values of the phases of the N array antenna elements are all random numbers within ; Step 2-10: Construct the anti-interference objective function object_function; the inputs are the element spacing d, the number of array elements N, the required beam direction , the list of interference arrival directions , the phase vector of the array antenna elements in the current iteration process and the maximum difference value ; the output is the objective function value : ； Among them, is the phase vector of the array antenna elements in the current iteration process and the N phase values of the array antenna elements; set the fitness value to be updated and the optimal antenna element phase vector , the initial value of is to initialize the candidate array antenna element phase vector, the initial value of is the output objective function value obtained by substituting the initialized candidate array antenna element phase vector into the anti-interference objective function object_function; Step 2-11: Set the temperature value and the final temperature value ; > ; Step 2-12: For the phase vectors of the array antenna elements in the current iteration process , add uniformly distributed random numbers within the range of to the phases of each array antenna element respectively, and update the numerical values of the phase vectors of the array antenna elements ; ​ Step 2-13: Calculate the phase vector of the array antenna elements after superposition The corresponding objective function value ; Step 2-14: If the new objective function value is greater than the fitness value to be updated or , where is a random number between [0, 1], then assign the new objective function value to the fitness value to be updated , and assign the value of the array antenna element phase vector to the optimal antenna element phase vector ; otherwise, keep the fitness value to be updated and the optimal antenna element phase vector unchanged; Step 2-15: Repeat Step 2-12 to Step 2-14 for a total of L times; Step 2-16: Update the temperature value , where is the cooling rate; Step 2-17: If the temperature value is greater than or equal to the final temperature value , then return to Step 2-12; if the temperature value is less than the final temperature value , then end the optimization and record the final optimal antenna element phase vector ; Step 2-18: After processing the final optimal antenna element phase vector with the jsonify function, use it as the return of the function compute_anti_jamming_parameters.

4. A microservice-based intelligent airspace anti-jamming method according to claim 3, characterized in that, The specific method of step 3 is as follows: requests.post(url, json=anti_jamming_dict) will return a response, which, after being processed by.json(), will obtain the final optimal antenna element phase vector at the client side. , set the phase parameters of each antenna element to form an antenna pattern.

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