Method and apparatus for monitoring radio frequency microwave signals based on broadband spectrum sensing

Abstract

This invention discloses a method and apparatus for monitoring radio frequency microwave signals based on broadband spectrum sensing, relating to the field of signal processing technology. The method includes: receiving radio frequency microwave signals using a broadband antenna and processing them to obtain a broadband time-domain discrete signal stream; performing full-band coverage processing using channelization technology to obtain an initial broadband spectrum panorama; performing statistical analysis on the panorama to construct a state vector of the current electromagnetic environment; based on the state vector, using a multi-objective particle swarm optimization algorithm to solve multiple optimization objective functions to obtain optimal monitoring parameters; configuring an FPGA using the optimal monitoring parameters for fine monitoring, and optimizing the multi-objective particle swarm optimization algorithm. This invention, by combining broadband spectrum sensing with a state vector-based multi-objective particle swarm optimization algorithm, achieves dynamic configuration of FPGA monitoring parameters, balancing monitoring sensitivity and resource consumption in complex and variable electromagnetic environments, and improving monitoring accuracy and system efficiency.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and in particular to a method and apparatus for monitoring radio frequency microwave signals based on broadband spectrum sensing. Background Technology

[0002] With the rapid development of wireless communication technology, the electromagnetic environment is becoming increasingly complex and changeable. Radio frequency microwave signals exhibit characteristics such as wide frequency band coverage, diverse signal patterns, high density, and rapid dynamic changes. Traditional radio frequency microwave signal monitoring systems typically employ fixed monitoring modes, i.e., setting fixed filter bandwidth, resolution bandwidth, and detection thresholds for signal scanning and acquisition.

[0003] However, this fixed-parameter monitoring method has significant limitations. On the one hand, to ensure the capture of weak signals, the system often needs to maintain a high sensitivity state for a long time, which usually means using a large number of FFT points and a high processing gain, resulting in huge computational resource consumption and power consumption. On the other hand, in complex environments with dense signals, a fixed detection threshold can easily lead to an excessively high false alarm rate or missed detections, and it cannot adaptively adjust according to environmental noise and signal distribution. In addition, as the core processing unit of the monitoring system, the Field-Programmable Gate Array (FPGA) has limited internal logic resources, and unreasonable parameter configuration may lead to resource overflow or low utilization.

[0004] Therefore, how to design an intelligent monitoring method that can adaptively adjust monitoring parameters according to the real-time electromagnetic environment and achieve a balance between monitoring sensitivity, resource consumption and real-time performance has become an urgent technical problem to be solved. Summary of the Invention

[0005] This invention provides a method and apparatus for monitoring radio frequency microwave signals based on broadband spectrum sensing, which solves the problems of fixed monitoring parameters, inability to adapt to complex electromagnetic environment changes, and unreasonable resource utilization in the prior art.

[0006] In a first aspect, embodiments of the present invention provide a method for monitoring radio frequency microwave signals based on broadband spectrum sensing, the method comprising:

[0007] A broadband antenna is used to receive radio frequency microwave signals. After low-noise amplification, filtering and down-conversion of the radio frequency microwave signals, they are digitized by an analog-to-digital converter to obtain a broadband time-domain discrete signal stream.

[0008] A channelization technique based on a multiphase filter structure is used to perform full-band coverage processing on the broadband time-domain discrete signal stream to obtain an initial broadband spectrum panorama.

[0009] Statistical analysis is performed on the initial broadband spectrum panorama to extract background noise levels, signal activity, and signal distribution characteristics, and to construct the state vector of the current electromagnetic environment.

[0010] Based on the state vector, a multi-objective particle swarm optimization algorithm is used to solve multiple optimization objective functions to obtain the optimal monitoring parameters for the FPGA;

[0011] The signal processing logic inside the FPGA is configured with optimal monitoring parameters to perform fine monitoring, and the multi-target particle swarm algorithm is optimized based on the actual monitoring performance.

[0012] The technical solution provided in this application has at least the following beneficial effects:

[0013] By constructing an electromagnetic environment state vector and guiding a multi-objective particle swarm optimization algorithm, the monitoring strategy can be automatically adjusted according to environmental characteristics (such as signal sparsity, noise level, and signal type), realizing the transformation from "fixed monitoring" to "intelligent sensing" and improving the level of monitoring intelligence. While ensuring monitoring sensitivity, the resource consumption and power consumption of the FPGA are effectively reduced by optimizing the number of FFT points and parallel channels, thereby improving hardware utilization. By adopting channelization technology based on multiphase filtering and adaptive detection, signals can be accurately captured in complex and congested electromagnetic environments, reducing false alarm rate and missed detection rate, and improving accuracy and anti-interference capability. The algorithm parameters are optimized by using feedback from actual monitoring performance, enabling the monitoring system to continuously adapt to uncontrollable factors such as hardware aging and sudden environmental changes, and maintain long-term stable high-performance operation.

[0014] In one optional implementation, a broadband antenna is used to receive radio frequency (RF) microwave signals. After low-noise amplification, filtering, and down-conversion of the RF microwave signals, an analog-to-digital converter is used for digitization to obtain a broadband time-domain discrete signal stream, including:

[0015] A broadband antenna is used to receive radio frequency microwave signals covering a frequency range.

[0016] A limiter is used to limit the radio frequency microwave signal to obtain a limited radio frequency microwave signal.

[0017] A multi-stage LNA amplifier is used to amplify the clipped RF microwave signal with low noise, resulting in an RF microwave signal with improved signal-to-noise ratio.

[0018] A tunable preselection filter is used to filter the radio frequency microwave signal after the signal-to-noise ratio has been improved, and the filtered radio frequency microwave signal is obtained.

[0019] Based on the center frequency set for the current monitoring task, the filtered radio frequency microwave signal is down-converted using a local oscillator and a mixer to obtain an analog intermediate frequency signal.

[0020] An analog-to-digital converter is used to digitize the analog intermediate frequency signal to obtain a digital intermediate frequency signal;

[0021] Inside the FPGA, a digitally controlled oscillator and a digital mixer are used to downconvert the digital intermediate frequency signal to the baseband to obtain the first digital baseband signal.

[0022] The first digital baseband signal is quadrature demodulated to generate two digital signals, one in phase and one quadrature, and outputs a broadband time-domain discrete signal stream in complex form.

[0023] In one optional implementation, a channelization technique based on a polyphase filter structure is employed to perform full-band coverage processing on the broadband time-domain discrete signal stream to obtain an initial broadband spectral panorama, including:

[0024] The broadband time-domain discrete signal stream is converted from serial to parallel using a channelization technique based on a polyphase filter structure. D The first low-speed data stream, of which, D To determine the extraction multiplier;

[0025] Parallel D The first low-speed data stream is input one by one to the preset prototype low-pass filter to obtain... D In a multiphase sub-filter, multiphase branch filtering is performed to obtain... D Data stream after first filtering;

[0026] Regarding the D After the first filtering, the data stream undergoes a Discrete Fourier Transform to obtain parallel... D Each of the first sub-channel signals corresponds to a specific frequency band in the original broadband spectrum of the radio frequency microwave signal;

[0027] The power spectral density of each first sub-channel signal is calculated as the corresponding first spectral amplitude data. Combined with the first spectral amplitude data of all first sub-channel signals, an initial broadband spectrum panorama is obtained. The frequency resolution of the initial broadband spectrum panorama is determined by the channel bandwidth of the radio frequency microwave signal.

[0028] In one alternative implementation, statistical analysis is performed on the initial broadband spectrum panorama to extract background noise levels, signal activity, and signal distribution characteristics, constructing a state vector of the current electromagnetic environment, including:

[0029] Traverse all frequency points in the initial broadband spectrum panorama, use the noise floor estimation algorithm to identify and remove peak points belonging to the first sub-channel signal in all frequency points, and select the clean frequency points in the corresponding spectrum that are not occupied by the signal as the noise sample set;

[0030] The arithmetic mean of the amplitude values ​​within the noise sample set is calculated to obtain the background noise level of the current frequency band, and the standard deviation of the noise sample set is calculated as the fluctuation characteristic of the background noise.

[0031] Based on the calculated background noise level and fluctuation characteristics, an adaptive signal detection threshold is set.

[0032] Traverse all frequency points in the initial broadband spectrum panorama, mark frequency points whose amplitude exceeds the signal detection threshold as active frequency points, and count the proportion of the number of active frequency points to the total number of frequency points in the panorama to obtain the signal activity level. Based on the magnitude of the signal activity level, define the current electromagnetic environment as a sparse environment, a moderately crowded environment, or a dense environment.

[0033] Connectivity analysis is performed on the initial broadband spectrum panorama to cluster adjacent active frequency points into continuous signal blocks. The bandwidth distribution of all signal blocks is statistically analyzed, and the average and maximum signal bandwidth of the bandwidth distribution are calculated.

[0034] Calculate the probability distribution entropy of the spectral amplitude values ​​of the initial broadband spectral panorama, and based on the temporal envelope undulation of the signal block, preliminarily distinguish between stationary signals and pulse signals, and generate signal type characteristic parameters;

[0035] By combining background noise level, fluctuation characteristics, signal activity, average signal bandwidth, probability distribution entropy, and signal type characteristic parameters into a multi-dimensional vector, the state vector of the current electromagnetic environment is obtained.

[0036] In one alternative implementation, based on the state vector, a multi-objective particle swarm optimization algorithm is used to solve multiple optimization objective functions to obtain the optimal monitoring parameters for the FPGA, including:

[0037] The monitoring parameters of the FPGA are encoded into position vectors of a multi-target particle swarm algorithm, and multiple optimization objective functions are set. The monitoring parameters include the number of FFT points during fine analysis, the threshold coefficient for constant false alarm rate detection, the number of channels actually activated and finely processed, and the intermediate frequency amplification gain.

[0038] Based on the state vector, the parameter search range for initialization of the multi-objective particle swarm algorithm, the dynamic fitness weighting of multiple optimization objective functions, the search space boundary constraints, and the position update parameters are defined.

[0039] The chaotic sequence is generated using the Logistic mapping, and then mapped to the solution space of the particles to obtain the initial particle swarm.

[0040] Load the candidate monitoring parameters corresponding to each initial particle in the initial particle swarm, and use the fitness function to calculate the corresponding fitness value.

[0041] By introducing a convergence factor and the Levy aircraft mechanism, the initial particle swarm is updated to obtain an updated particle swarm.

[0042] Load the candidate monitoring parameters for each updated particle in the updated particle swarm, and use the fitness function to calculate the corresponding fitness value.

[0043] The iteration continues until the number of iterations reaches the iteration threshold, at which point the iteration update of the particle swarm stops, and the particle with the best fitness value in the last updated particle swarm is taken as the global best particle.

[0044] The position vector of the globally optimal particle is decoded to obtain the optimal monitoring parameters of the FPGA. The optimal monitoring parameters include the optimal number of FFT points for fine analysis, the optimal threshold coefficient for constant false alarm rate detection, the optimal number of channels that are actually turned on and finely processed, and the optimal intermediate frequency amplification gain.

[0045] In one optional implementation, the multiple optimization objective function includes a sensitivity objective function and a resource consumption objective function;

[0046] The formula for the multiple optimization objective function is:

[0047]

[0048] In the formula, For particles X The fitness values ​​of the corresponding alternative monitoring parameters; For particles X The sensitivity of the corresponding alternative monitoring parameters; For particles X Resource consumption of the corresponding alternative monitoring parameters; X For particle variables, i.e., the position vectors of particles in the multi-objective particle swarm algorithm; For the state vector The dynamic fitness weighting of the control.

[0049] In one alternative implementation, the signal processing logic within the FPGA is configured with optimal monitoring parameters for fine-grained monitoring, and the multi-target particle swarm optimization algorithm is optimized based on actual monitoring performance, including:

[0050] By configuring the signal processing logic inside the FPGA using the optimal monitoring parameters, a reconstructed FPGA can be obtained.

[0051] A reconstructed FPGA is used to perform fine monitoring of broadband time-domain discrete signal streams, obtain accurate monitoring results, and collect actual monitoring performance data during the fine monitoring process.

[0052] The algorithm parameters of the multi-target particle swarm optimization algorithm were optimized based on actual monitoring performance.

[0053] In one alternative implementation, the signal processing logic inside the FPGA is configured using optimal monitoring parameters to obtain a reconstructed FPGA, including:

[0054] Write the optimal intermediate frequency amplification gain from the optimal monitoring parameters into the SPI control interface of the programmable gain amplifier connected inside the FPGA;

[0055] Based on the optimal number of channels that are actually activated and finely processed in the optimal monitoring parameters, adjust the number of sub-channels that are effectively output by the polyphase filter module inside the FPGA.

[0056] The optimal number of FFT points for fine analysis and the optimal threshold coefficient for constant false alarm rate detection in the optimal monitoring parameters are loaded into the spectrum processing engine inside the FPGA.

[0057] Based on the optimal number of FFT points during the detailed analysis, the point parameters of the IP core of the FFT module of the spectrum processing engine are reconfigured, the read and write address logic of the buffer is adjusted accordingly, and the optimal threshold coefficient of constant false alarm rate detection is used as the multiplication coefficient and input into the threshold calculator of the constant false alarm rate detection module of the spectrum processing engine.

[0058] The reconstructed FPGA is obtained through the above steps.

[0059] In one alternative implementation, a reconfigured FPGA is used to perform fine monitoring of a broadband time-domain discrete signal stream, obtaining accurate monitoring results and acquiring the actual monitoring performance during the fine monitoring process, including:

[0060] A broadband time-domain discrete signal stream is acquired. Based on the optimal intermediate frequency amplification gain in the optimal monitoring parameters, the programmable gain amplifier inside the FPGA is used to digitally adjust the gain of the broadband time-domain discrete signal stream to obtain the gain-adjusted signal.

[0061] The gain-adjusted signal is converted from serial to parallel using a channelization technique based on a polyphase filter structure. D The second lowest speed data stream, of which, D To determine the extraction multiplier;

[0062] Parallel D The second low-speed data stream is input to a preset prototype low-pass filter to obtain... D In a multiphase sub-filter, multiphase branch filtering is performed to obtain... D The data stream after the second filter in the path;

[0063] Regarding the D The data stream after the second filter is subjected to a discrete Fourier transform to obtain parallel... DEach of the second sub-channel signals corresponds to a specific frequency band in the original broadband spectrum of the radio frequency microwave signal;

[0064] Based on the optimal number of channels actually activated and finely processed from the optimal monitoring parameters, from... D In the second sub-channel signal of the path, the channel whose signal amplitude exceeds the signal detection threshold is selected as the channel of interest;

[0065] Based on the optimal number of FFT points in the fine analysis of the optimal monitoring parameters, the spectrum processing engine inside the FPGA is used to perform windowing and fast Fourier transform on the second filtered data stream of all channels of interest to obtain high-frequency resolution frequency domain data.

[0066] The frequency domain data is integrated with the second spectral amplitude data of other unselected second sub-channel signals to obtain an updated broadband spectrum panorama. The frequency resolution of the channel of interest in the updated broadband spectrum panorama is determined by the optimal number of FFT points.

[0067] Based on the optimal threshold coefficient in the optimal monitoring parameters, the threshold calculator of the constant false alarm rate detection module of the spectrum processing engine inside the FPGA is used to recalculate the signal detection threshold and obtain the updated signal detection threshold.

[0068] Traverse all frequency points in the updated broadband spectrum panorama, mark frequency points whose amplitude exceeds the updated signal detection threshold as key frequency points, and integrate the frequency and amplitude information of all key frequency points to obtain accurate monitoring results;

[0069] The actual monitoring performance during the fine monitoring process is collected, including the measured false alarm rate, the measured signal-to-noise ratio improvement, the measured resource utilization rate, and the background noise tracking value.

[0070] Secondly, embodiments of the present invention provide a radio frequency microwave signal monitoring device based on broadband spectrum sensing, used to implement a radio frequency microwave signal monitoring method, the device comprising:

[0071] The broadband spectrum sensing unit is used to receive radio frequency microwave signals using a broadband antenna, perform low-noise amplification, filtering and down-conversion processing on the radio frequency microwave signals, and then digitize them using an analog-to-digital converter to obtain a broadband time-domain discrete signal stream.

[0072] The digital channelization unit is used to perform full-band coverage processing on the broadband time-domain discrete signal stream using channelization technology based on a polyphase filter structure to obtain an initial broadband spectrum panorama.

[0073] The environmental state perception unit is used to perform statistical analysis on the initial broadband spectrum panorama, extract background noise level, signal activity and signal distribution characteristics, and construct the state vector of the current electromagnetic environment;

[0074] The multi-objective optimization unit is used to solve multiple optimization objective functions based on the state vector using the multi-objective particle swarm algorithm to obtain the optimal monitoring parameters of the FPGA.

[0075] The fine monitoring unit is used to configure the signal processing logic inside the FPGA with optimal monitoring parameters to perform fine monitoring and optimize the multi-target particle swarm algorithm based on actual monitoring performance.

[0076] A third aspect of this invention provides an electronic device, which includes:

[0077] At least one processor; and a memory communicatively connected to the at least one processor; wherein,

[0078] The memory stores instructions that can be executed by at least one processor, such that the at least one processor can perform the method proposed in the first aspect of the present invention.

[0079] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in the first aspect of the present invention. Attached Figure Description

[0080] Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of the present invention;

[0081] Figure 2 This is a flowchart illustrating the steps of a radio frequency microwave signal monitoring method based on broadband spectrum sensing provided in an embodiment of the present invention.

[0082] Figure 3 This is a functional unit diagram of a radio frequency microwave signal monitoring device based on broadband spectrum sensing provided in an embodiment of the present invention. Detailed Implementation

[0083] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0084] The present invention will be further described below with reference to the accompanying drawings.

[0085] Reference Figure 1 , Figure 1 This is a schematic diagram of the electronic device structure of the hardware operating environment involved in the embodiments of the present invention.

[0086] like Figure 1 As shown, the electronic device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0087] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0088] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and an electronic program for a radio frequency microwave signal monitoring device based on broadband spectrum sensing.

[0089] exist Figure 1 In the illustrated electronic device, the network interface 1004 is mainly used for monitoring radio frequency microwave signals with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the electronic device of the present invention can be set in the electronic device. The electronic device calls the electronic program of the radio frequency microwave signal monitoring device based on broadband spectrum sensing stored in the memory 1005 through the processor 1001, and executes the radio frequency microwave signal monitoring method based on broadband spectrum sensing provided in the embodiment of the present invention.

[0090] Reference Figure 2The present invention provides a method for monitoring radio frequency microwave signals based on broadband spectrum sensing, the method comprising:

[0091] S201: A broadband antenna is used to receive radio frequency microwave signals. After low-noise amplification, filtering and down-conversion of the radio frequency microwave signals, they are digitized by an analog-to-digital converter to obtain a broadband time-domain discrete signal stream.

[0092] S202: Using channelization technology based on a multiphase filter structure, the broadband time-domain discrete signal stream is processed to cover the entire frequency band, thereby obtaining an initial broadband spectrum panorama.

[0093] S203: Perform statistical analysis on the initial broadband spectrum panorama, extract background noise level, signal activity and signal distribution characteristics, and construct the state vector of the current electromagnetic environment;

[0094] S204: Based on the state vector, a multi-objective particle swarm optimization algorithm is used to solve multiple optimization objective functions to obtain the optimal monitoring parameters of the FPGA;

[0095] S205: Utilizes the optimal monitoring parameters to configure the signal processing logic inside the FPGA for fine monitoring, and optimizes the multi-target particle swarm algorithm based on actual monitoring performance.

[0096] The technical solution provided in this application has at least the following beneficial effects:

[0097] By constructing an electromagnetic environment state vector and guiding a multi-objective particle swarm optimization algorithm, the monitoring strategy can be automatically adjusted according to environmental characteristics (such as signal sparsity, noise level, and signal type), realizing the transformation from "fixed monitoring" to "intelligent sensing" and improving the level of monitoring intelligence. While ensuring monitoring sensitivity, the resource consumption and power consumption of the FPGA are effectively reduced and the hardware utilization is improved by optimizing the number of Fast Fourier Transform (FFT) points and parallel channels. By adopting channelization technology based on multiphase filtering and adaptive detection, signals can be accurately captured in complex and congested electromagnetic environments, reducing false alarm rate and missed detection rate, and improving accuracy and anti-interference capability. The algorithm parameters are optimized by using actual monitoring performance feedback, enabling the monitoring system to continuously adapt to uncontrollable factors such as hardware aging and sudden environmental changes, and maintain long-term stable high-performance operation.

[0098] In one optional implementation, a broadband antenna is used to receive radio frequency (RF) microwave signals. After low-noise amplification, filtering, and down-conversion of the RF microwave signals, an analog-to-digital converter is used for digitization to obtain a broadband time-domain discrete signal stream, including:

[0099] S2011: Uses a broadband antenna to receive radio frequency microwave signals with a frequency range coverage;

[0100] S2012: A limiter is used to limit the radio frequency microwave signal to obtain a limited radio frequency microwave signal;

[0101] S2013: A multi-stage low-noise amplifier (LNA) is used to amplify the clipped RF microwave signal with low noise, resulting in an improved RF microwave signal with a higher signal-to-noise ratio.

[0102] S2014: A tunable preselection filter is used to filter the radio frequency microwave signal after the signal-to-noise ratio has been improved, and the filtered radio frequency microwave signal is obtained.

[0103] S2015: Based on the center frequency set for the current monitoring task, the filtered radio frequency microwave signal is down-converted using a local oscillator and mixer to obtain an analog intermediate frequency signal;

[0104] S2016: The analog intermediate frequency signal is digitized using an analog-to-digital converter to obtain a digital intermediate frequency signal;

[0105] S2017: Inside the FPGA, a digitally controlled oscillator and a digital mixer are used to downconvert the digital intermediate frequency signal to the baseband to obtain the first digital baseband signal;

[0106] S2018: Performs quadrature demodulation on the first digital baseband signal to generate two digital signals, one in phase and one quadrature, and outputs a broadband time-domain discrete signal stream in complex form.

[0107] In one optional implementation, a channelization technique based on a polyphase filter structure is employed to perform full-band coverage processing on the broadband time-domain discrete signal stream to obtain an initial broadband spectral panorama, including:

[0108] S2021: Using channelization technology based on a polyphase filter structure, the broadband time-domain discrete signal stream is converted from serial to parallel to obtain... D The first low-speed data stream, of which, D To determine the extraction multiplier;

[0109] S2022: Parallel D The first low-speed data stream is input one by one to the preset prototype low-pass filter to obtain... D In a multiphase sub-filter, multiphase branch filtering is performed to obtain... D Data stream after first filtering;

[0110] S2023: Regarding the aforementioned D After the first filtering, the data stream undergoes a Discrete Fourier Transform to obtain parallel... DEach of the first sub-channel signals corresponds to a specific frequency band in the original broadband spectrum of the radio frequency microwave signal;

[0111] S2024: Calculate the power spectral density of each first sub-channel signal as the corresponding first spectral amplitude data, and combine the first spectral amplitude data of all first sub-channel signals to obtain an initial broadband spectrum panorama. The frequency resolution of the initial broadband spectrum panorama is determined by the channel bandwidth of the radio frequency microwave signal.

[0112] In one alternative implementation, statistical analysis is performed on the initial broadband spectrum panorama to extract background noise levels, signal activity, and signal distribution characteristics, constructing a state vector of the current electromagnetic environment, including:

[0113] S2031: Traverse all frequency points in the initial broadband spectrum panorama, use the noise floor estimation algorithm to identify and remove peak points belonging to the first sub-channel signal in all frequency points, and select the clean frequency points in the corresponding spectrum that are not occupied by the signal as the noise sample set.

[0114] S2032: Calculate the arithmetic mean of the amplitude values ​​within the noise sample set to obtain the background noise level of the current frequency band. The standard deviation of the noise sample set is calculated as the fluctuation characteristic of the background noise. ;

[0115] S2033: Background noise level calculated based on the background noise level and fluctuation characteristics Set an adaptive signal detection threshold using the following formula:

[0116]

[0117] In the formula, This is the signal detection threshold; This represents the background noise level. It exhibits wave-like characteristics; It is a threshold multiplier;

[0118] S2034: Traverse all frequency points in the initial broadband spectrum panorama, and identify those whose amplitude exceeds the stated signal detection threshold. The frequency points are marked as active frequency points, and the proportion of active frequency points to the total number of frequency points in the panoramic image is counted to obtain the signal activity level. And based on signal activity The size of the electromagnetic environment determines whether the current electromagnetic environment is sparse, moderately crowded, or dense.

[0119] S2035: Perform connected component analysis on the initial broadband spectrum panorama, cluster adjacent active frequency points into continuous signal blocks, statistically analyze the bandwidth distribution of all signal blocks, and calculate the average signal bandwidth of the bandwidth distribution. and maximum signal bandwidth ;

[0120] S2036: Calculate the probability distribution entropy of the spectral amplitude values ​​of the initial broadband spectral panorama. Based on the temporal envelope variability of the signal block, a preliminary distinction is made between stationary signals and pulse signals, and signal type characteristic parameters are generated. ;

[0121] S2037: Adjust background noise level Fluctuation characteristics Signal activity Average signal bandwidth Probability distribution entropy and signal type characteristic parameters Combining these elements into a multidimensional vector yields the current electromagnetic environment's state vector, as shown in the formula:

[0122]

[0123] In the formula, It is a state vector; This represents the background noise level. It exhibits wave-like characteristics; Signal activity; The probability distribution entropy is a characteristic of signal distribution. The average signal bandwidth is a characteristic of signal distribution. These are the signal type characteristic parameters in the signal distribution characteristics.

[0124] In one alternative implementation, based on the state vector, a multi-objective particle swarm optimization algorithm is used to solve multiple optimization objective functions to obtain the optimal monitoring parameters for the FPGA, including:

[0125] S2041: Encode the FPGA's monitoring parameters into position vectors for a multi-objective particle swarm optimization algorithm, and set multiple optimization objective functions. The monitoring parameters include the number of FFT points during fine-grained analysis. Threshold coefficient for constant false alarm rate detection The actual number of channels that are activated and processed. and intermediate frequency amplification gain ;

[0126] S2042: Based on the state vector, the parameter search range for the initialization of the multi-objective particle swarm algorithm, the dynamic fitness weighting of multiple optimization objective functions, the search space boundary constraints, and the position update parameters are set.

[0127] In this embodiment, the parameter search range is set based on the state vector:

[0128] In the algorithm startup phase, the feature information in the state vector is used to generate an initial population through a chaotic sequence, so that the particle distribution is closer to the potential optimal solution region, thereby greatly improving the convergence speed.

[0129] If the state vector Signal type characteristic parameters The environment is indicated as "pulse signal":

[0130] The algorithm determines that the current task requires extremely high temporal resolution to capture transient signals. Therefore, the algorithm will assign parameters representing "temporal resolution" (such as...) to the parameters... The optimization search interval is shrunken and mapped to a high-value region, for example... ∈[2048,8192];

[0131] like The environment indicates a "stable signal":

[0132] The algorithm determines that the current task is more concerned with frequency resolution; therefore, it adjusts the optimization search range of the aforementioned parameter variables to a lower numerical range, for example... ∈[512,1024], to save computational resources.

[0133] For other non-critical parameters, the default global search range can be maintained;

[0134] like High frequency (band congestion), increase the number of parallel channels during initialization. The proportion of larger particles in the population;

[0135] Based on the state vector, a dynamic fitness weighting method is defined for multiple optimization objective functions:

[0136] When calculating the merits of each particle (parameter combination), the state vector determines which is more important in the current environment: "sensitivity" or "resource consumption," and adjusts the weights of the fitness function in real time.

[0137] If the state vector of High sensitivity (in harsh environments), the algorithm automatically increases the sensitivity of the target. The weight of the function reduces resource consumption. The algorithm prioritizes "sensitivity" in low signal-to-noise ratio environments, even at the cost of some processing speed; conversely, it prioritizes "resource consumption" in high signal-to-noise ratio environments.

[0138] like Extremely high (extremely complex environment, dense signal), the algorithm in computation At that time, dynamic introduction of... The penalty items, or increase The weight of fitness calculation; the guiding algorithm selects parameter combinations with stricter thresholds and stronger anti-interference capabilities in complex environments to avoid false alarm explosions;

[0139] Based on the state vector, set boundary constraints for the search space;

[0140] The state vector defines the feasible region of parameters under the current physical conditions, preventing the algorithm from searching for illegal solutions that lead to system failures (such as overflow or saturation).

[0141] Real-time monitoring of algorithms ,like If the threshold is exceeded (indicating an extremely unstable environment), the algorithm will forcibly shrink the decision variables. The search lower bound is set; particles are prohibited from flying into the "extremely low threshold" region. Because in an unstable environment, an extremely low threshold can lead to an uncontrollable false alarm rate, the state vector acts as a "safety valve" here;

[0142] like High resource usage is displayed, algorithm tightening. and The upper limit is set to prevent the selected parameter combination from exceeding the FPGA's logic resource limit or causing the analog-to-digital converter to saturate.

[0143] Based on the state vector, set the position update parameters;

[0144] For the Levy long-jump mechanism introduced in the algorithm, the state vector is used to control when to jump and how far to jump, so as to realize on-demand exploration;

[0145] The algorithm monitors the optimal fitness improvement curve of the population. If it detects that the population has stagnated (premature convergence) and the state vector shows that the current environment has "high entropy" characteristics, then it proceeds according to... The value is dynamically calculated for the step size factor of Levy flight; the more complex the environment ( The higher the step size of Levy flight, the larger it is set, forcing particles to make large, long-distance jumps, quickly escaping the current local optimum trap, and searching for other regions in the parameter space;

[0146] S2043: Use the Logistic mapping to generate a chaotic sequence, and map the chaotic sequence to the solution space of the particles to obtain the initial particle swarm;

[0147] The formula is:

[0148]

[0149] In the formula, For the first n+ 1. n One chaotic variable; The stability coefficient is typically 4. This sequence is ergodic and random, ensuring that the initial particle swarm is uniformly distributed in the solution space, avoiding getting trapped in local optima, which is superior to traditional random initialization. n For chaotic variable indicators;

[0150]

[0151] In the formula, For the initial particle swarm, the first i An initial particle; For the first i One chaotic variable; These are the upper and lower bounds of the search space; For particle indication;

[0152]

[0153] In the formula, For the first i The initial velocity of the initial particle; A random number in the range (-1, 1);

[0154] S2044: Load the candidate monitoring parameters corresponding to each initial particle in the initial particle swarm, and use the fitness function to calculate the corresponding fitness value.

[0155] S2045: Introducing a convergence factor and the Levy aircraft mechanism to update the positions of the initial particle swarm, resulting in an updated particle swarm.

[0156] In the early stages of iteration t <0.7 ,in, t This represents the current iteration number. The number of iterations is the threshold number of iterations.

[0157] The formula is:

[0158]

[0159] In the formula, Number of iterations t+ 1 of i The rate of update of each particle; Number of iterations t The i The update rate of each particle, which is the initial rate during the first iteration; Number of iterations t The convergence factor improves the inertia weight; Number of iterations t The globally optimal particle; Number of iterations t The i A new particle is generated, which is the initial particle in the first iteration; Number of iterations t The i The historical optimal position of each particle; t This represents the current iteration number; The cooperation coefficient; A random number between (0, 1); i For particle indication;

[0160]

[0161] In the formula, These represent the maximum and minimum values ​​of the inertia weight; This is the threshold for the number of iterations; , To adjust the parameters; It is a hyperbolic tangent function; this design results in a larger weight in the early stage, which is beneficial for global search; and a smaller weight with a gradual change in the later stage, which is beneficial for fine-grained local mining.

[0162]

[0163] In the formula, Number of iterations t+ 1 of i An updated particle;

[0164] After 10 consecutive iterations, the globally optimal particle of the population is... If there is almost no change, immediately perform a Levy flight perturbation on the current position of all particles; if not, skip this step and continue with the traditional iteration.

[0165] The formula is:

[0166]

[0167] In the formula, Number of iterations t+ 1. The execution of the Levy flight disturbance i An updated particle; For flight factors; for Levy The random numbers are distributed as follows: b for Levy Step length, and b ∈[1,2], the more complex the environment ( (higher) b The larger; The element-wise dot product symbol;

[0168] In the later stages of the iteration t Based on the adaptive Cauchy mutation mechanism, mutation is performed using the following formula:

[0169]

[0170] In the formula, For the first t+ The first iteration of Cauchy mutation i An updated particle; It is a standard Cauchy distributed random variable; For the first t The convergence factor of the next iteration;

[0171] S2046: Load the candidate monitoring parameters for each updated particle in the updated particle swarm, and use the fitness function to calculate the corresponding fitness value.

[0172] S2047: Stop iterating and updating the particle swarm until the number of iterations reaches the iteration threshold, and take the particle with the best fitness value in the last updated particle swarm as the global best particle.

[0173] S2048: Decode the position vector of the globally optimal particle to obtain the optimal monitoring parameters of the FPGA. The optimal monitoring parameters include the optimal number of FFT points during fine analysis. Optimal threshold coefficient for constant false alarm rate detection The optimal number of channels actually activated and finely processed and optimal intermediate frequency amplification gain .

[0174] In one optional implementation, the multiple optimization objective function includes a sensitivity objective function and a resource consumption objective function;

[0175] The formula for the multiple optimization objective function is:

[0176]

[0177] In the formula, For particles X The fitness values ​​of the corresponding alternative monitoring parameters; For particles X The sensitivity of the corresponding alternative monitoring parameters; For particles X Resource consumption of the corresponding alternative monitoring parameters; X For particle variables, i.e., the position vectors of particles in the multi-objective particle swarm algorithm; For the state vector The dynamic fitness weighting of the control;

[0178] The formula for the sensitivity target function is:

[0179]

[0180] In the formula, The gain normalization value is processed for FFT; This represents the maximum number of FFT points during detailed analysis. This is the normalized value of the link gain; This represents the maximum gain of the intermediate frequency amplification. To detect threshold loss; This represents the maximum threshold coefficient for constant false alarm rate detection; These are internal weighting coefficients;

[0181] The formula for the objective function of resource consumption is:

[0182]

[0183] In the formula, To calculate resource normalization values ​​for FFT; This is a reference value for the maximum FFT computational cost; This represents the normalized value for parallel channel resources. This represents the maximum number of channels that are actually activated and subjected to fine-tuning. This is the normalized value for gain control resources; This represents the resource weighting coefficient.

[0184] In one alternative implementation, the signal processing logic within the FPGA is configured with optimal monitoring parameters for fine-grained monitoring, and the multi-target particle swarm optimization algorithm is optimized based on actual monitoring performance, including:

[0185] S2051: The signal processing logic inside the FPGA is configured using the optimal monitoring parameters to obtain the reconstructed FPGA;

[0186] S2052: Employs a reconstructed FPGA to perform fine monitoring of broadband time-domain discrete signal streams, obtain accurate monitoring results, and collect actual monitoring performance data during the fine monitoring process;

[0187] S2053: Optimize the algorithm parameters of the multi-target particle swarm algorithm based on actual monitoring performance.

[0188] In one alternative implementation, the signal processing logic inside the FPGA is configured using optimal monitoring parameters to obtain a reconstructed FPGA, including:

[0189] S20511: Optimal intermediate frequency amplification gain from the optimal monitoring parameters. Write to the SPI control interface of the programmable gain amplifier connected inside the FPGA;

[0190] It is worth noting that adjusting the gain factor of the analog front-end or digital domain ensures that the amplitude of the broadband time-domain discrete signal stream acquired by the analog-to-digital converter falls within the optimal quantization range (usually -1dB to -3dB of the full scale of the analog-to-digital converter), which prevents saturation clipping and ensures that small signals have sufficient quantization signal-to-noise ratio.

[0191] S20512: The optimal number of channels that are actually activated and finely processed based on the optimal monitoring parameters. Adjust the number of sub-channels of the effective output of the polyphase filter module inside the FPGA;

[0192] It is worth noting that by configuring the parameters of the channelized intellectual property (IP) core via the AXI-Lite bus, specific parallel processing channels can be enabled or disabled. If the power consumption is high, more FFT engines and detection logic will be activated in parallel; if the power consumption is low, some channels will be suspended to reduce power consumption.

[0193] S20513: The optimal FFT points from the fine analysis of the optimal monitoring parameters. The optimal threshold coefficient for constant false alarm rate detection A spectrum processing engine loaded into the FPGA;

[0194] S20514: According to Reconfigure the point count parameters of the IP core of the FFT module in the spectrum processing engine, adjust the read and write address logic of the buffer accordingly, and... As multiplication coefficients, they are input into the threshold calculator of the constant false alarm rate (CFAR) detection module of the spectrum processing engine.

[0195] FPGA according to the new frequency resolution (by The system performs spectral transformation on the signal and calculates the background noise in real time based on the new threshold coefficient, outputting the monitoring results (including signal amplitude, frequency, and time of arrival).

[0196] The reconstructed FPGA is obtained through the above steps.

[0197] In one alternative implementation, a reconfigured FPGA is used to perform fine monitoring of a broadband time-domain discrete signal stream, obtaining accurate monitoring results and acquiring the actual monitoring performance during the fine monitoring process, including:

[0198] S205211: Obtain the broadband time-domain discrete signal stream, based on the optimal intermediate frequency amplification gain in the optimal monitoring parameters. The programmable gain amplifier inside the FPGA is used to digitally adjust the gain of the broadband time-domain discrete signal stream to obtain the gain-adjusted signal.

[0199] S205212: Using channelization technology based on a polyphase filter structure, the gain-adjusted signal is converted from serial to parallel to obtain... D The second lowest speed data stream, of which, D To determine the extraction multiplier;

[0200] S205213: Parallel D The second low-speed data stream is input to a preset prototype low-pass filter to obtain... D In a multiphase sub-filter, multiphase branch filtering is performed to obtain... D The data stream after the second filter in the path;

[0201] S205214: Regarding the aforementioned D The data stream after the second filter is subjected to a discrete Fourier transform to obtain parallel... D Each of the second sub-channel signals corresponds to a specific frequency band in the original broadband spectrum of the radio frequency microwave signal;

[0202] S205215: The optimal number of channels that are actually activated and finely processed based on the optimal monitoring parameters. ,from D In the second sub-channel signal of the path, the channel whose signal amplitude exceeds the signal detection threshold is selected as the channel of interest;

[0203] S205216: Optimal FFT points based on the detailed analysis of the optimal monitoring parameters. Using the spectrum processing engine inside the FPGA, windowing and fast Fourier transform are performed on the second filtered data stream of all channels of interest to obtain high-frequency resolution frequency domain data.

[0204] S205217: Integrate the frequency domain data with the second spectral amplitude data of other unselected second sub-channel signals to obtain an updated broadband spectrum panorama. The frequency resolution of the channels of interest in the updated broadband spectrum panorama is determined by the optimal number of FFT points. Decide;

[0205] S205218: Based on the optimal threshold coefficient in the optimal monitoring parameters The constant false alarm rate (CFAR) detection module's threshold calculator within the FPGA's internal spectrum processing engine is used to recalculate the signal detection threshold, resulting in an updated signal detection threshold. The formula is as follows:

[0206]

[0207] In the formula, For updating the signal detection threshold;

[0208] S205219: Traverse all frequency points in the updated broadband spectrum panorama, mark frequency points whose amplitude exceeds the updated signal detection threshold as key frequency points, and integrate the frequency and amplitude information of all key frequency points to obtain accurate monitoring results;

[0209] S2052110: Collect actual monitoring performance during the fine-grained monitoring process, wherein the actual monitoring performance includes the measured false alarm rate. Actual signal-to-noise ratio improvement Actual resource utilization rate and noise floor tracking value ;

[0210] It is worth noting that the measured false alarm rate : Count the number of times the threshold is exceeded when there is no signal within a unit of time;

[0211] Actual signal-to-noise ratio improvement Compare the signal-to-noise ratio before and after processing;

[0212] Actual resource utilization rate Read the temperature sensor and logic utilization register inside the FPGA;

[0213] Noise floor tracking value : Statistically measure the actual detected noise floor level at the current moment.

[0214] In one optional implementation, the algorithm parameters of the multi-objective particle swarm optimization algorithm are optimized based on actual monitoring performance, including:

[0215] S20531: Utilizing the current state vector ambient noise level Improve the measured signal-to-noise ratio in actual monitoring performance. Normalization is performed to obtain the normalized measured signal-to-noise ratio improvement. ;

[0216] S20532: According to Optimal sensitivity of the optimal monitoring parameters corresponding to actual monitoring performance The sensitivity error is calculated using the following formula:

[0217]

[0218] In the formula, This is for sensitivity error; The optimal monitoring parameters are those corresponding to the globally optimal particle.

[0219] S20533: Based on the measured resource utilization rate in actual monitoring performance. Optimal resource consumption for optimal monitoring parameters corresponding to actual monitoring performance The formula for calculating resource consumption error is:

[0220]

[0221] In the formula, This is due to resource consumption error;

[0222] S20534: Optimal threshold coefficient based on constant false alarm rate detection and the current state vector ambient noise level Calculate the theoretical false alarm rate prediction value ;

[0223] S20535: According to And the measured false alarm rate in actual monitoring performance The false alarm rate deviation is calculated using the following formula:

[0224]

[0225] In the formula, This is due to the false alarm rate deviation.

[0226] S20536: Combine the errors from the above three dimensions into a multi-dimensional error vector to characterize the distortion of the current environmental model. The formula is as follows:

[0227]

[0228] In the formula, It is a multidimensional error vector;

[0229] S20537: If the multidimensional error vector contains >Preset threshold, and > This indicates that the interference density in the current environment far exceeds the model's expectations, leading to an out-of-control false alarm rate. Therefore, increasing the internal weighting coefficient in the sensitivity objective function is necessary. Alternatively, the dynamic fitness weighting of the multi-optimization objective function can be directly reduced. This will guide the algorithm to prioritize the selection of threshold coefficients in the next iteration. Larger (i.e., more stringent, more interference-resistant but slightly less sensitive) parameter combinations;

[0230] S20538: If Larger and < This indicates that the theoretically high gain parameter failed to deliver the expected signal-to-noise ratio improvement in actual hardware (possibly due to analog front-end saturation or nonlinear distortion). Therefore, the decision variables should be modified. The upper limit of the search, For example, setting the new upper limit as ×0.8 forces the algorithm to avoid trying excessively high gain settings in subsequent searches, thus preventing it from entering the nonlinear saturation region of the hardware.

[0231] S20539: If Its continued existence and stability indicate a reference value for the maximum FFT computational cost. If there are deviations from the actual FPGA architecture, then utilize... and optimal monitoring parameters The maximum FFT computation reference value in the resource consumption objective function is then calculated and updated using the following formula:

[0232]

[0233] In the formula, This is an updated reference value for the maximum FFT computation cost; the revised formula can more accurately predict the resource consumption of future parameters and improve the accuracy of optimization.

[0234] S205310: If A large error vector magnitude indicates a severe model mismatch and a possible sudden change in the environment. Therefore, in the position update formula of the multi-objective particle algorithm, the step size factor of the Levy flight mechanism is increased, as shown in the formula:

[0235]

[0236] In the formula, Let be the magnitude of the error vector.

[0237] This invention also provides a radio frequency microwave signal monitoring device 300 based on broadband spectrum sensing, referring to... Figure 3 The device may include the following units:

[0238] The broadband spectrum sensing unit 301 is used to receive radio frequency microwave signals using a broadband antenna, perform low-noise amplification, filtering and down-conversion processing on the radio frequency microwave signals, and then digitize them using an analog-to-digital converter to obtain a broadband time-domain discrete signal stream.

[0239] The digital channelization unit 302 is used to perform full-band coverage processing on the broadband time-domain discrete signal stream using channelization technology based on a polyphase filter structure to obtain an initial broadband spectrum panorama.

[0240] The environmental state perception unit 303 is used to perform statistical analysis on the initial broadband spectrum panorama, extract background noise level, signal activity and signal distribution characteristics, and construct the state vector of the current electromagnetic environment.

[0241] The multi-objective optimization unit 304 is used to solve multiple optimization objective functions based on the state vector using a multi-objective particle swarm optimization algorithm to obtain the optimal monitoring parameters of the FPGA.

[0242] The fine monitoring unit 305 is used to configure the signal processing logic inside the FPGA with the optimal monitoring parameters to perform fine monitoring and optimize the multi-target particle swarm algorithm based on the actual monitoring performance.

[0243] Based on the same inventive concept, another embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus.

[0244] Memory, used to store computer programs;

[0245] When the processor executes the program stored in the memory, it implements the radio frequency microwave signal monitoring method based on broadband spectrum sensing of the present invention.

[0246] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus. The communication interface is used for communication between the aforementioned terminal and other devices. The memory can include Random Access Memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory can also be at least one storage device located remotely from the aforementioned processor.

[0247] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0248] Furthermore, to achieve the above objectives, embodiments of the present invention also propose a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the radio frequency microwave signal monitoring method based on broadband spectrum sensing of the present invention.

[0249] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable hardware devices (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0250] The embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (apparatus), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0251] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0252] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0253] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. "And / or" indicates that either one or both can be chosen. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes the element.

[0254] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for monitoring radio frequency microwave signals based on broadband spectrum sensing, characterized in that, The method includes: A broadband antenna is used to receive radio frequency microwave signals. After low-noise amplification, filtering and down-conversion of the radio frequency microwave signals, they are digitized by an analog-to-digital converter to obtain a broadband time-domain discrete signal stream. A channelization technique based on a multiphase filter structure is used to perform full-band coverage processing on the broadband time-domain discrete signal stream to obtain an initial broadband spectrum panorama. Statistical analysis is performed on the initial broadband spectrum panorama to extract background noise levels, signal activity, and signal distribution characteristics, constructing a state vector of the current electromagnetic environment, including: Traverse all frequency points in the initial broadband spectrum panorama, use the noise floor estimation algorithm to identify and remove peak points belonging to the first sub-channel signal in all frequency points, and select the clean frequency points in the corresponding spectrum that are not occupied by the signal as the noise sample set; The arithmetic mean of the amplitude values ​​within the noise sample set is calculated to obtain the background noise level of the current frequency band, and the standard deviation of the noise sample set is calculated as the fluctuation characteristic of the background noise. Based on the calculated background noise level and fluctuation characteristics, an adaptive signal detection threshold is set. Traverse all frequency points in the initial broadband spectrum panorama, mark frequency points whose amplitude exceeds the signal detection threshold as active frequency points, and count the proportion of the number of active frequency points to the total number of frequency points in the panorama to obtain the signal activity level. Based on the magnitude of the signal activity level, define the current electromagnetic environment as a sparse environment, a moderately crowded environment, or a dense environment. Connectivity analysis is performed on the initial broadband spectrum panorama to cluster adjacent active frequency points into continuous signal blocks. The bandwidth distribution of all signal blocks is statistically analyzed, and the average and maximum signal bandwidth of the bandwidth distribution are calculated. Calculate the probability distribution entropy of the spectral amplitude values ​​of the initial broadband panoramic image, and based on the temporal envelope undulation of the signal block, preliminarily distinguish between stationary signals and pulse signals, and generate signal type characteristic parameters; By combining background noise level, fluctuation characteristics, signal activity, average signal bandwidth, probability distribution entropy, and signal type characteristic parameters into a multi-dimensional vector, the state vector of the current electromagnetic environment is obtained. Based on the state vector, a multi-objective particle swarm optimization algorithm is used to solve multiple optimization objective functions to obtain the optimal monitoring parameters for the FPGA, including: The monitoring parameters of the FPGA are encoded into position vectors of a multi-objective particle swarm algorithm, and multiple optimization objective functions are set. The monitoring parameters include the number of FFT points during fine analysis, the threshold coefficient for constant false alarm rate detection, the number of channels actually activated and finely processed, and the intermediate frequency amplification gain. The multiple optimization objective functions include a sensitivity objective function and a resource consumption objective function; The formula for the multiple optimization objective function is: In the formula, For particles X The fitness values ​​of the corresponding alternative monitoring parameters; For particles X The sensitivity of the corresponding alternative monitoring parameters; For particles X Resource consumption of the corresponding alternative monitoring parameters; X For particle variables, i.e., the position vectors of particles in the multi-objective particle swarm algorithm; For the state vector The dynamic fitness weighting of the control; Based on the state vector, the parameter search range for initialization of the multi-objective particle swarm algorithm, the dynamic fitness weighting of multiple optimization objective functions, the search space boundary constraints, and the position update parameters are defined. The chaotic sequence is generated using the Logistic mapping, and then mapped to the solution space of the particles to obtain the initial particle swarm. Load the candidate monitoring parameters corresponding to each initial particle in the initial particle swarm, and use the fitness function to calculate the corresponding fitness value. By introducing a convergence factor and the Levy aircraft mechanism, the initial particle swarm is updated to obtain an updated particle swarm. Load the candidate monitoring parameters for each updated particle in the updated particle swarm, and use the fitness function to calculate the corresponding fitness value. The iteration continues until the number of iterations reaches the iteration threshold, at which point the iteration update of the particle swarm stops, and the particle with the best fitness value in the last updated particle swarm is taken as the global best particle. The position vector of the globally optimal particle is decoded to obtain the optimal monitoring parameters of the FPGA. The optimal monitoring parameters include the optimal number of FFT points for fine analysis, the optimal threshold coefficient for constant false alarm rate detection, the optimal number of channels that are actually activated and finely processed, and the optimal intermediate frequency amplification gain. The signal processing logic inside the FPGA is configured with optimal monitoring parameters for fine-grained monitoring. The multi-target particle swarm optimization algorithm is then optimized based on actual monitoring performance, including: By configuring the signal processing logic inside the FPGA using optimal monitoring parameters, a reconstructed FPGA is obtained, including: Write the optimal intermediate frequency amplification gain from the optimal monitoring parameters into the SPI control interface of the programmable gain amplifier connected inside the FPGA; Based on the optimal number of channels that are actually activated and finely processed in the optimal monitoring parameters, adjust the number of sub-channels that are effectively output by the polyphase filter module inside the FPGA. The optimal number of FFT points for fine analysis and the optimal threshold coefficient for constant false alarm rate detection in the optimal monitoring parameters are loaded into the spectrum processing engine inside the FPGA. Based on the optimal number of FFT points during the detailed analysis, the point parameters of the IP core of the FFT module of the spectrum processing engine are reconfigured, the read and write address logic of the buffer is adjusted accordingly, and the optimal threshold coefficient of constant false alarm rate detection is used as the multiplication coefficient and input into the threshold calculator of the constant false alarm rate detection module of the spectrum processing engine. The reconstructed FPGA is obtained through the configuration steps of the signal processing logic inside the FPGA; A reconstructed FPGA is used to perform fine monitoring of broadband time-domain discrete signal streams, obtaining accurate monitoring results and acquiring actual monitoring performance data during the fine monitoring process, including: A broadband time-domain discrete signal stream is acquired. Based on the optimal intermediate frequency amplification gain in the optimal monitoring parameters, the programmable gain amplifier inside the FPGA is used to digitally adjust the gain of the broadband time-domain discrete signal stream to obtain the gain-adjusted signal. The gain-adjusted signal is converted from serial to parallel using a channelization technique based on a polyphase filter structure. D The second lowest speed data stream, of which, D To determine the extraction multiplier; Parallel D The second low-speed data stream is input to a preset prototype low-pass filter to obtain... D In a multiphase sub-filter, multiphase branch filtering is performed to obtain... D The data stream after the second filter in the path; Regarding the D The data stream after the second filter is subjected to a discrete Fourier transform to obtain parallel... D Each of the second sub-channel signals corresponds to a specific frequency band in the original broadband spectrum of the radio frequency microwave signal; Based on the optimal number of channels actually activated and finely processed from the optimal monitoring parameters, from... D In the second sub-channel signal of the path, the channel whose signal amplitude exceeds the signal detection threshold is selected as the channel of interest; Based on the optimal number of FFT points in the fine analysis of the optimal monitoring parameters, the spectrum processing engine inside the FPGA is used to perform windowing and fast Fourier transform on the second filtered data stream of all channels of interest to obtain high-frequency resolution frequency domain data. The frequency domain data is integrated with the second spectral amplitude data of other unselected second sub-channel signals to obtain an updated broadband spectrum panorama. The frequency resolution of the channel of interest in the updated broadband spectrum panorama is determined by the optimal number of FFT points. Based on the optimal threshold coefficient in the optimal monitoring parameters, the threshold calculator of the constant false alarm rate detection module of the spectrum processing engine inside the FPGA is used to recalculate the signal detection threshold and obtain the updated signal detection threshold. Traverse all frequency points in the updated broadband spectrum panorama, mark frequency points whose amplitude exceeds the updated signal detection threshold as key frequency points, and integrate the frequency and amplitude information of all key frequency points to obtain accurate monitoring results; The actual monitoring performance during the fine monitoring process is collected, including the measured false alarm rate, the measured signal-to-noise ratio improvement, the measured resource utilization rate, and the noise floor tracking value. The algorithm parameters of the multi-target particle swarm optimization algorithm were optimized based on actual monitoring performance.

2. The radio frequency microwave signal monitoring method based on broadband spectrum sensing according to claim 1, characterized in that, A broadband antenna is used to receive radio frequency (RF) microwave signals. After low-noise amplification, filtering, and down-conversion of the RF microwave signals, they are digitized using an analog-to-digital converter to obtain a broadband discrete-time signal stream, including: A broadband antenna is used to receive radio frequency microwave signals covering a frequency range. A limiter is used to limit the radio frequency microwave signal to obtain a limited radio frequency microwave signal. A multi-stage LNA amplifier is used to amplify the clipped RF microwave signal with low noise, resulting in an RF microwave signal with improved signal-to-noise ratio. A tunable preselection filter is used to filter the radio frequency microwave signal after the signal-to-noise ratio has been improved, and the filtered radio frequency microwave signal is obtained. Based on the center frequency set for the current monitoring task, the filtered radio frequency microwave signal is down-converted using a local oscillator and a mixer to obtain an analog intermediate frequency signal. An analog-to-digital converter is used to digitize the analog intermediate frequency signal to obtain a digital intermediate frequency signal; Inside the FPGA, a numerically controlled oscillator and a digital mixer are used to downconvert the digital intermediate frequency signal to the baseband to obtain the first digital baseband signal. The first digital baseband signal is quadrature demodulated to generate two digital signals, one in phase and one quadrature, and outputs a broadband time-domain discrete signal stream in complex form.

3. The radio frequency microwave signal monitoring method based on broadband spectrum sensing according to claim 2, characterized in that, A channelization technique based on a polyphase filter structure is employed to perform full-band coverage processing on the broadband time-domain discrete signal stream, obtaining an initial broadband spectral panorama, including: The broadband time-domain discrete signal stream is converted from serial to parallel using a channelization technique based on a polyphase filter structure. D The first low-speed data stream, of which, D To determine the extraction multiplier; Parallel D The first low-speed data stream is input one by one to the preset prototype low-pass filter to obtain... D In a multiphase sub-filter, multiphase branch filtering is performed to obtain... D Data stream after first filtering; Regarding the D After the first filtering, the data stream undergoes a Discrete Fourier Transform to obtain parallel... D Each of the first sub-channel signals corresponds to a specific frequency band in the original broadband spectrum of the radio frequency microwave signal; The power spectral density of each first sub-channel signal is calculated as the corresponding first spectral amplitude data. Combined with the first spectral amplitude data of all first sub-channel signals, an initial broadband spectrum panorama is obtained. The frequency resolution of the initial broadband spectrum panorama is determined by the channel bandwidth of the radio frequency microwave signal.

4. A radio frequency microwave signal monitoring device based on broadband spectrum sensing, used to implement the radio frequency microwave signal monitoring method as described in any one of claims 1-3, characterized in that, The device includes: The broadband spectrum sensing unit is used to receive radio frequency microwave signals with a broadband antenna, perform low-noise amplification, filtering and down-conversion processing on the radio frequency microwave signals, and then digitize them with an analog-to-digital converter to obtain a broadband time-domain discrete signal stream. The digital channelization unit is used to perform full-band coverage processing on the broadband time-domain discrete signal stream using channelization technology based on a polyphase filter structure to obtain an initial broadband spectrum panorama. The environmental state perception unit is used to perform statistical analysis on the initial broadband spectrum panorama, extract background noise level, signal activity and signal distribution characteristics, and construct the state vector of the current electromagnetic environment. The multi-objective optimization unit is used to solve multiple optimization objective functions based on the state vector using the multi-objective particle swarm algorithm to obtain the optimal monitoring parameters of the FPGA. The fine monitoring unit is used to configure the signal processing logic inside the FPGA with optimal monitoring parameters to perform fine monitoring and optimize the multi-target particle swarm algorithm based on actual monitoring performance.

Images