Full-band electromagnetic signal intelligent scanning detection system

Abstract

The invention discloses an intelligent scanning detection system for full-band electromagnetic signals. The intelligent scanning detection system comprises a signal acquisition module for acquiring full-band electromagnetic signals by adopting a three-stage cascade radio frequency chain design; the signal integration module optimizes and selects the optimal unit spacing through an improved genetic algorithm, receives the full-band electromagnetic signals based on the integrated millimeter wave antenna array of the optimal unit spacing, and outputs optimized electromagnetic signals; the intelligent scanning module is used for dividing different frequency band signals according to the different frequency band signals in the optimized electromagnetic signals, formulating corresponding scanning strategies and outputting scanning electromagnetic signals; and the signal processing module is used for extracting three-dimensional comprehensive characteristics including time-frequency-space from the scanning electromagnetic signals, determining the three-dimensional comprehensive characteristics with potential relation based on similarity matching, and carrying out enhancement processing through an improved CAPN beam forming algorithm to obtain optimal signal representation, so that intelligent signal scanning and analysis are realized, and the accuracy and the reliability of the system are improved. Valuable signal representation can be rapidly and accurately obtained from a complex electromagnetic environment.

Description

Technical Field

[0001] The present invention relates to the technical field of signal processing, and in particular to a full-band electromagnetic signal intelligent scanning detection system. Background Art

[0002] At present, electromagnetic signals are increasingly used in various fields, from communications, radar, navigation to industrial production, medical equipment, etc. Electromagnetic signals are everywhere. However, as the electromagnetic environment becomes increasingly complex, efficient and accurate scanning and detection of full-band electromagnetic signals faces many challenges. At present, the scanning detection accuracy and efficiency of traditional systems are low. When processing signals, traditional systems adopt a uniform scanning strategy, and the dwell time of key frequency bands and non-key frequency bands is the same, which will lead to low scanning efficiency. Measured data show that a full scan in the 24GHz frequency band takes more than 200ms, which cannot meet the real-time detection needs. When the non-key frequency band adopts mean statistics (such as V2), the time domain details of transient signals (such as pulse width <1μs) are lost, resulting in low-probability threat signals being missed. Moreover, traditional electromagnetic signal scanning detection systems lack intelligence. They usually require a lot of manual parameter settings and data analysis to obtain the final signal representation, and it is difficult to automatically adjust the detection strategy according to changes in the actual electromagnetic environment. Faced with the increasingly complex and changeable electromagnetic environment, this method of manual intervention is inefficient and prone to errors, and cannot give full play to the performance advantages of the system. Therefore, a full-band electromagnetic signal intelligent scanning detection system is proposed. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention proposes the following technical solutions: A full-band electromagnetic signal intelligent scanning detection system, comprising: Signal acquisition module: adopts a three-stage cascaded RF chain design to collect full-band electromagnetic signals; Signal integration module: An improved genetic algorithm is used to optimize and select the optimal unit spacing. The integrated millimeter-wave antenna array based on the optimal unit spacing receives full-band electromagnetic signals and outputs optimized electromagnetic signals. Intelligent scanning module: According to the different frequency band signals in the optimized electromagnetic signal, the signals of different frequency bands are divided and corresponding scanning strategies are formulated to output scanning electromagnetic signals; Signal processing module: extracts three-dimensional comprehensive features including time, frequency and space from the scanning electromagnetic signal, determines the three-dimensional comprehensive features with potential connections based on similarity matching, and performs enhancement processing through the improved CAPON beamforming algorithm to obtain the best signal representation; The improved genetic algorithm optimizes the basic genetic algorithm by introducing the index gain and the sidelobe suppression ratio for comprehensive consideration; The improved CAPON beamforming algorithm introduces a weighting factor to optimize the acquisition of beamforming weight vectors based on the traditional CAPON algorithm.

[0004] The full-band electromagnetic signal includes a low-band electromagnetic signal, a mid-band electromagnetic signal and a high-band electromagnetic signal.

[0005] The low-frequency electromagnetic signal adopts a direct sampling architecture, uses a 16-bit ADC, and has a sampling frequency of f=10GS / s. The intermediate-frequency electromagnetic signal is collected by superheterodyne frequency conversion, and the collected local oscillator step accuracy is less than or equal to 1Hz. The high-frequency electromagnetic signal adopts second harmonic mixing combined with a low-temperature low-noise amplifier, wherein the noise coefficient of the low-temperature low-noise amplifier is less than 3dB.

[0006] The optimal cell spacing acquisition process is as follows: Select the highest frequency in the full-band electromagnetic signal band Calculate the corresponding wavelength to determine the initial value of the unit spacing. Through the basic genetic algorithm, use the initial value of the unit spacing as the individual gene in the genetic algorithm, initialize a population, and obtain the antenna array performance index gain corresponding to each individual in the population. SLS , and the sidelobe suppression ratio Gain with performance indicators Take a comprehensive approach and define a comprehensive performance indicator function ; Based on comprehensive performance index function , calculate the fitness of each individual in the initial population according to the comprehensive performance index function, and select according to the fitness of the individual, and finally obtain the unit spacing value that makes the antenna array performance optimal under the current conditions .

[0007] The acquisition process of the optimized electromagnetic signal is as follows: Based on the optimal cell spacing Design antenna structure and beam scanning basis, integrated millimeter wave antenna array receives electromagnetic signals of different frequency bands from space, converts them into electrical signals and outputs them to obtain optimized electromagnetic signals .

[0008] The acquisition process of the scanning electromagnetic signal is as follows: Based on optimizing electromagnetic signal The set of frequency bands covered, defining the signal density function and importance weight function , based on the signal density function and importance weight function Get comprehensive metrics for each frequency band , based on comprehensive indicators Divide the frequency bands into key frequency bands and non-key frequency bands; Use frequency-by-frequency scanning to obtain key signal characteristics for key frequency bands ; Use frequency hopping fast scanning to obtain basic statistical characteristics for non-key frequency bands ; Based on key signal characteristics And basic statistical characteristics Acquiring scanning electromagnetic signals .

[0009] The three-dimensional comprehensive features include time domain feature signals, frequency domain feature signals and space domain feature signals.

[0010] The process of obtaining the optimal signal representation is as follows: The cosine similarity algorithm is used to obtain the similarity between different 3D comprehensive features, and the similarity threshold is used Perform signal matching to obtain the same signal source; Get weighting factor Optimizing the beamforming weight vector of the traditional CAPON algorithm , through the weight vector The three-dimensional comprehensive features of the same signal source received are weighted to obtain the enhanced optimal signal representation .

[0011] The weighting factor The acquisition process is: The different features in the three-dimensional comprehensive features are linearly combined according to the weights to obtain the preliminary weighting factor. The reward rules are set based on the improvement of the signal-to-noise ratio of the signal and the improvement of the target signal and the interference signal. The reinforcement learning algorithm is used to adjust the mapping relationship between the preliminary weighting factor and each signal feature according to the reward rule to obtain the weighting factor. .

[0012] The present invention has the following beneficial effects: In the present invention, firstly, by adopting a three-stage cascaded radio frequency chain design and combining targeted processing methods of different frequency bands, the present invention can comprehensively collect electromagnetic signals of the entire frequency band, and at the same time, through the signal integration module, an improved genetic algorithm is used to optimize and select the integrated millimeter wave antenna array with the optimal unit spacing to receive the signal. The algorithm comprehensively considers the antenna array performance index gain and sidelobe suppression ratio, and dynamically adjusts the calculation weight. Compared with the traditional design, it can significantly improve the gain of the antenna array, enhance the signal receiving capability, and effectively suppress the sidelobe level and reduce signal interference. Secondly, the improved CAPON beamforming algorithm introduces an adaptive weighting factor, which is dynamically adjusted according to the three-dimensional comprehensive characteristics of the signal and system requirements, further enhancing the target signal and suppressing the interference signal, making the final signal higher in quality and more conducive to subsequent signal recognition, classification, demodulation and other processing; Finally, the intelligent scanning module calculates the comprehensive index according to the signal density function and the importance weight function, intelligently divides the signals in different frequency bands, and formulates corresponding scanning strategies. The key frequency bands use frequency-point precise scanning to obtain detailed feature vectors, and the non-key frequency bands use frequency hopping fast scanning to obtain basic statistical characteristics. This not only improves the scanning efficiency, but also can effectively analyze the characteristics of signals in different frequency bands. The signal processing module can accurately judge the potential connection of signals based on the signal matching mechanism of the time-frequency-space three-dimensional characteristics. The entire system realizes intelligent signal scanning and analysis, and can quickly and accurately obtain valuable signal representations from complex electromagnetic environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a system block diagram of a full-band electromagnetic signal intelligent scanning detection system proposed by the present invention. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] Example: Figure 1 As shown, the present invention proposes a full-band electromagnetic signal intelligent scanning detection system, comprising: Signal acquisition module: adopts a three-stage cascaded RF chain design to collect full-band electromagnetic signals; Full-band electromagnetic signals include low-band electromagnetic signals (DC-6GHz), mid-band electromagnetic signals (6-18GHz) and high-band electromagnetic signals (18-24GHz); The low frequency band (DC-6GHz) adopts a direct sampling architecture, uses a 16-bit ADC, and a sampling frequency of f=10GS / s. The input low frequency signal is assumed to be a continuous time signal. According to the sampling theorem, the low-frequency discrete signal is obtained after ADC sampling , the sampling process satisfies ,in , is the collection cycle; Specifically, the direct sampling architecture avoids the frequency conversion loss and image interference problems caused by the traditional frequency conversion architecture, and can simply and effectively obtain low-frequency signal information, and finally outputs a discrete low-frequency signal after sampling. ; The medium frequency band (6-18GHz) is processed by superheterodyne frequency conversion, and the local oscillator signal , where the local oscillator step accuracy is less than or equal to 1Hz, and the input intermediate frequency signal is , after being mixed by a double-balanced mixer, the intermediate frequency signal is output according to the mixing principle for: ,in is the gain factor of the mixer; Specifically, by precisely controlling the local oscillator frequency , which can realize the accurate selection and processing of intermediate frequency signals of different frequencies, and convert the RF signal into an intermediate frequency signal that is convenient for subsequent processing ; The high frequency band (18-24GHz) uses second harmonic mixing combined with a low-temperature low-noise amplifier. The input high-frequency signal is , the local oscillator signal is , the signal after second harmonic mixing for: in is the gain factor of the second harmonic mixer; Specifically, second harmonic mixing improves the mixing efficiency and reduces the requirements for the local oscillator frequency. The signal is then amplified by a low-temperature low-noise amplifier (noise figure less than 3dB), and the signal-to-noise ratio of the output signal is significantly improved. The output is a high-frequency signal after mixing and amplification. ; The full-band RF subsystem finally outputs a signal set processed in different frequency bands. , these signals will serve as input to the next module.

[0016] Signal integration module: An improved genetic algorithm is used to optimize and select the optimal unit spacing. The integrated millimeter-wave antenna array based on the optimal unit spacing receives full-band electromagnetic signals and outputs optimized electromagnetic signals. The optimal cell spacing acquisition process is: Determine the frequency range of the system operation (DC-24GHz). Within this frequency range, the frequency changes continuously. Select the highest frequency in the full-band electromagnetic signal band. Calculate the corresponding wavelength to determine the preliminary value of the unit spacing. The calculation formula is: ,in, is the signal wavelength, is the maximum scanning angle; Specifically, select the highest frequency in the frequency band Because its corresponding wavelength Shortest, calculated cell spacing is the most stringent value that meets the requirements of the entire frequency band. For example, for the 18-24 GHz frequency band, select Calculate wavelength; The implementation process of the improved genetic algorithm is as follows: Through a basic genetic algorithm, the unit spacing is used as the individual gene in the genetic algorithm, and a containing multiple different The value of the population; By introducing the index gain SLS Comprehensive considerations are used to optimize the basic genetic algorithm to obtain an improved genetic algorithm; Calculate the antenna array performance index gain corresponding to each individual (i.e. each value), expressed as ,in, is the index gain, is the number of antenna units, is the acquisition frequency; Get the sidelobe suppression ratio , calculate the antenna array’s pattern function through Fourier transform , based on the directional pattern function It is calculated by calculating the ratio of the main lobe peak to the side lobe peak, that is, ,in is the angle corresponding to the main lobe peak, is the angle corresponding to the sidelobe peak; At the same time, the sidelobe suppression ratio Gain with performance indicators Take a comprehensive consideration and define a comprehensive performance index function: , is the number of antenna units; in, Performance index gain The weight coefficient of Sidelobe suppression ratio The weight coefficient of Based on comprehensive performance index function For each individual in the initial population, its fitness is calculated according to the comprehensive performance index function, and selection is made according to the individual's fitness. Individuals with high fitness have a greater probability of being selected to enter the next generation, and the population is iteratively updated by selecting crossover and mutation genetic operations, so that the population gradually moves towards the optimal unit spacing. Evolution, and finally obtain the unit distance value that makes the antenna array performance optimal under the current conditions ; Specifically, by improving the genetic algorithm to optimize the unit spacing, we no longer only focus on the gain in the antenna array performance indicators, but introduce the sidelobe suppression ratio and adaptive parameter adjustment. According to the distribution of individuals in the current population and the convergence trend of the objective function, we dynamically adjust the weights of the calculated gain and sidelobe suppression. For example: When it is found that the population is close to convergence in terms of gain index, but the sidelobe suppression ratio index still has a lot of room for improvement, it will automatically increase The weight value of the antenna array is optimized to strengthen the sidelobe suppression ratio, so that the gain of the antenna array is significantly improved. Under the same signal input and environmental conditions, compared with the traditional antenna array, the received signal strength may be increased by 20%-30% or even higher, effectively enhancing the signal reception capability. At the same time, the sidelobe level can be effectively suppressed and reduced by 5-10dB, reducing signal interference and improving signal purity and quality. Based on the optimal cell spacing Design antenna structure and beam scanning basis, the integrated millimeter wave antenna array receives electromagnetic signals of different frequency bands from space, converts them into electrical signals and outputs them to obtain optimized electromagnetic signals, which can be expressed as ; For example, in the 24 GHz frequency band, a specific optimal unit spacing can maximize the antenna's sensing efficiency for signals in this frequency band. At the same time, when scanning electromagnetic signals in the full frequency band, the optimal unit spacing combined with a specific antenna structure design can achieve three-dimensional beam scanning, and the scanning range is azimuth. , pitch , during the scanning process, based on the optimal cell spacing The antenna array can flexibly adjust the beam direction and accurately aim at signal sources in different directions, ensuring efficient signal collection throughout the entire scanning range without signal omissions or reception blind spots.

[0017] Intelligent scanning module: According to the different frequency band signals in the optimized electromagnetic signal, the signals of different frequency bands are divided and corresponding scanning strategies are formulated to output scanning electromagnetic signals; Assume that the optimized electromagnetic signal received by the system The frequency bands covered are ( ), is the index; Define a signal density function , which is used to measure the number or intensity distribution of signals within a unit frequency range and define an importance weight function , which indicates the importance of different frequency bands set according to application requirements, through the signal density function and importance weight function Calculate composite index , for each frequency band Calculate according to The value of is used to divide the frequency band into key frequency band and non-key frequency band; Set a threshold ,when When The frequency band is divided into key frequency bands; when When the frequency band is divided into non-key frequency bands; For the divided key frequency bands, the frequency-by-frequency precise scanning method is adopted, and the dwell time is set to (For example At each frequency point, the collected signal is subjected to detailed feature extraction, and the time-frequency analysis method is used to obtain the characteristics of the signal in the time domain and frequency domain (such as the amplitude change, frequency component, and pulse width of the signal) to form key signal characteristics. ; For non-key frequency bands, use frequency hopping fast scanning mode and set the dwell time to (For example ), quickly obtain the signal profile of these frequency bands, and directly obtain the basic statistical characteristics of the signal at each frequency hopping point (mean signal strength, frequency range, probability of signal occurrence); For example, for some low-frequency non-key frequency bands, a quick scan can be performed to obtain the approximate signal strength and frequency distribution to determine whether there are interference signals or abnormal signals; After frequency band division and scanning strategy processing, the scanning electromagnetic signal is output ; Specifically, scanning electromagnetic signals The signal feature vector obtained by precise scanning of the key frequency band in ,These eigenvectors contain detailed information of the key frequency band signals, which can be used for subsequent processing such as signal identification, classification, and demodulation; Basic statistical characteristics obtained by quick scanning of non-key frequency bands , used to quickly understand the signal status of non-key frequency bands and determine whether there are signal changes or abnormal conditions that require further attention. These scanning electromagnetic signals will serve as input to subsequent signal processing units for more in-depth analysis and processing.

[0018] Signal processing module: extracts three-dimensional comprehensive features including time, frequency and space from the scanning electromagnetic signal, determines the three-dimensional comprehensive features with potential connections based on similarity matching, and performs enhancement processing through the improved CAPON beamforming algorithm to obtain the best signal representation; The process of obtaining three-dimensional comprehensive features is: Time-frequency-space three-dimensional features, including time domain characteristic signals, frequency domain characteristic signals and space domain characteristic signals; Scanning electromagnetic signal Signals in different frequency bands (signal feature vectors of key frequency bands) Basic statistical characteristics of non-key frequency bands ), respectively extract its time domain characteristics (signal amplitude change), assuming that the scanning electromagnetic signal The characteristic vector of the key frequency band in the time domain is , the feature vector of the non-key frequency band is , then the electromagnetic signal is scanned The time domain characteristic signal is ; Extract frequency domain features (such as frequency components), and the feature vector of the key frequency band in the frequency domain is , the feature vector of the non-key frequency band in the frequency domain is , then the electromagnetic signal is scanned The frequency domain characteristic signal is ; Extract the spatial features (the direction of arrival of the signal), and the feature vector of the key frequency band in the spatial domain is , the feature vector of the non-key frequency band in the airspace is , then the electromagnetic signal is scanned The spatial characteristic signal is ; The three-dimensional comprehensive feature is expressed as , calculate the similarity between different three-dimensional comprehensive features for signal matching, and use the cosine similarity algorithm to calculate the similarity. Suppose two three-dimensional comprehensive features The similarity is expressed as:

[0019] Set a similarity threshold , when the similarity between two signals is greater than the similarity threshold When , it is considered that the two signals have a potential connection, that is, they come from the same signal source, and are uniformly expressed as ; The same signal source is enhanced by an improved CAPON beamforming algorithm; the acquisition process of the improved CAPON beamforming algorithm is: The improved CAPON beamforming algorithm introduces a weighting factor based on the traditional CAPON algorithm. To optimize the weight vector of the traditional CAPON algorithm beamforming , assuming the steering vector of the antenna array is , the covariance matrix of the received signal is , the improved weight vector for:

[0020] in It is a weighting factor that is dynamically adjusted based on the spatial characteristics of the signal (such as the DOA of the signal) and system requirements. represents the conjugate transpose. For example, for a focused signal from a specific direction, the corresponding value to enhance the gain of the signal in this direction; Weighting Factor The acquisition process is: Introducing an adaptive learning mechanism to determine the weighting factors ,In the initial operation stage, the weighting factors are preliminarily set based on the ,three-dimensional comprehensive characteristics of the signal based on the preset rules; Specifically, the preset rule is to obtain the linear combination of different features in the three-dimensional comprehensive features according to the weights, and the weighting factor initially set is expressed as ,in, is the time domain weight, is the frequency domain weight, is the airspace weight; Constantly monitor the improvement of the signal-to-noise ratio, the target signal and the interference signal, and set reward rules: Assume that the signal-to-noise ratio of the target signal before processing is , after processing , the power of the interference signal before processing is , after processing ; After beamforming processing, if the signal-to-noise ratio of the target signal exceeds a certain threshold, or the interference signal is successfully separated, a positive reward is given, specifically: like (is the set signal-to-noise ratio enhancement threshold), and ( ) is the interference signal power reduction ratio threshold), a positive reward is given, if the above conditions are not met, a negative reward is given; Using reinforcement learning algorithms, adjust the initial weighting factors based on the rewards The mapping relationship between the signal characteristics is used to obtain the weighting factor ; Specifically, if it is found that for a certain type of time-domain characteristic signal, the signal-to-noise ratio is significantly improved after increasing the weighting factor, the reinforcement learning algorithm will increase the weight of the time-domain feature in the generation of the weighting factor, so that the subsequent processing is more inclined to adjust the weighting factor according to the time-domain feature. Through this adaptive learning mechanism, the weighting factor can be continuously optimized to continuously improve the beamforming effect in different electromagnetic signal scenarios; According to the weight vector The three-dimensional comprehensive characteristics of the same signal source received Perform weighted processing to obtain the best signal representation after enhancement , the formula is: = ,in, Represents the weight vector The conjugate transpose of ; Specifically, the signal after being processed by the above improved CAPON beamforming algorithm is represented as follows: , which is the best signal representation of the improved CAPON beamforming algorithm. The signal representation result can be used for subsequent signal recognition, classification, demodulation and other applications. The signal obtained after time-frequency-space three-dimensional comprehensive feature extraction, similarity matching and enhanced processing by the improved CAPON beamforming algorithm is the result of optimization processing based on the multi-dimensional characteristics of the signal and the complex electromagnetic environment. This result can provide more accurate and clearer signal information and improve the accuracy of signal detection; Example: In signal recognition, based on the best signal representation Clearer features for pattern matching; When classifying signals, use the best signal representation Features are classified by rules or machine learning models; In signal demodulation, high-quality signals are used to represent Accurately restore the original signal.

[0021] In the application, several formulas involved are calculated by removing dimensions and taking their numerical values. The formulas are established by collecting a large amount of data and performing software simulation to obtain a formula for the most recent actual situation. Some coefficients or weights in the formulas are set by technicians in this field according to actual conditions, so they will not be elaborated here.

[0022] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0023] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A full-band electromagnetic signal intelligent scanning detection system, characterized in that: include: Signal acquisition module: adopts a three-stage cascaded RF chain design to collect full-band electromagnetic signals; Signal integration module: An improved genetic algorithm is used to optimize and select the optimal unit spacing. The integrated millimeter-wave antenna array based on the optimal unit spacing receives full-band electromagnetic signals and outputs optimized electromagnetic signals. Intelligent scanning module: According to the different frequency band signals in the optimized electromagnetic signal, the signals of different frequency bands are divided and corresponding scanning strategies are formulated to output scanning electromagnetic signals; Signal processing module: extracts three-dimensional comprehensive features including time, frequency and space from the scanning electromagnetic signal, determines the three-dimensional comprehensive features with potential connections based on similarity matching, and performs enhancement processing through the improved CAPON beamforming algorithm to obtain the best signal representation; The improved genetic algorithm optimizes the basic genetic algorithm by introducing the index gain and the sidelobe suppression ratio for comprehensive consideration; The improved CAPON beamforming algorithm introduces a weighting factor to optimize the acquisition of beamforming weight vectors based on the traditional CAPON algorithm.

2. The full-band electromagnetic signal intelligent scanning detection system according to claim 1 is characterized in that: The full-band electromagnetic signal includes a low-band electromagnetic signal, a mid-band electromagnetic signal and a high-band electromagnetic signal.

3. The full-band electromagnetic signal intelligent scanning detection system according to claim 2 is characterized in that: The low-frequency electromagnetic signal adopts a direct sampling architecture, uses a 16-bit ADC, and has a sampling frequency of f=10GS / s. The intermediate-frequency electromagnetic signal is collected by superheterodyne frequency conversion, and the collected local oscillator step accuracy is less than or equal to 1Hz. The high-frequency electromagnetic signal adopts second harmonic mixing combined with a low-temperature low-noise amplifier, and the noise coefficient of the low-temperature low-noise amplifier is less than 3dB.

4. The full-band electromagnetic signal intelligent scanning detection system according to claim 1 is characterized in that: The optimal cell spacing acquisition process is as follows: Select the highest frequency in the full-band electromagnetic signal band Calculate the corresponding wavelength to determine the initial value of the unit spacing. Use the genetic algorithm to use the initial value of the unit spacing as the individual gene in the genetic algorithm, initialize a population, and obtain the antenna array performance index gain corresponding to each individual in the population. SLS , and the sidelobe suppression ratio Gain with performance indicators Take a comprehensive approach and define a comprehensive performance indicator function ; Based on comprehensive performance index function , calculate the fitness of each individual in the initial population according to the comprehensive performance index function, and select according to the fitness of the individual to obtain the optimal unit spacing value for the best antenna array performance .

5. The full-band electromagnetic signal intelligent scanning detection system according to claim 1 is characterized in that: The acquisition process of the optimized electromagnetic signal is as follows: Based on the optimal cell spacing Design antenna structure and beam scanning basis, integrated millimeter wave antenna array receives electromagnetic signals of different frequency bands from space, converts them into electrical signals and outputs them to obtain optimized electromagnetic signals .

6. The full-band electromagnetic signal intelligent scanning detection system according to claim 1 is characterized in that: The acquisition process of the scanning electromagnetic signal is as follows: Based on optimizing electromagnetic signal The set of frequency bands covered ( ), As the index, define the signal density function and importance weight function ; Based on the signal density function and importance weight function Get comprehensive metrics for each frequency band , based on comprehensive indicators Divide the frequency bands into key frequency bands and non-key frequency bands; Use frequency-by-frequency scanning to obtain key signal characteristics for key frequency bands ; Use frequency hopping fast scanning to obtain basic statistical characteristics for non-key frequency bands ; Based on key signal characteristics And basic statistical characteristics Acquiring scanning electromagnetic signals .

7. The full-band electromagnetic signal intelligent scanning detection system according to claim 1, characterized in that: The three-dimensional comprehensive features include time domain feature signals, frequency domain feature signals and space domain feature signals.

8. The full-band electromagnetic signal intelligent scanning detection system according to claim 1 is characterized in that: The process of obtaining the optimal signal representation is as follows: The cosine similarity algorithm is used to obtain the similarity between different 3D comprehensive features, and the similarity threshold is used Perform signal matching to obtain the same signal source; Get weighting factor Optimizing the beamforming weight vector of the traditional CAPON algorithm , through the weight vector The three-dimensional comprehensive features of the same signal source received are weighted to obtain the enhanced optimal signal representation .

9. The full-band electromagnetic signal intelligent scanning detection system according to claim 8, characterized in that: The weighting factor The acquisition process is: The different features in the three-dimensional comprehensive features are linearly combined according to the weights to obtain the preliminary weighting factor. The reward rules are set based on the improvement of the signal-to-noise ratio of the signal and the improvement of the target signal and the interference signal. The reinforcement learning algorithm is used to adjust the mapping relationship between the preliminary weighting factor and each signal feature according to the reward rule to obtain the weighting factor. .

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