Segmented frequency conversion planning method for radio frequency signal

Abstract

The invention discloses a segmented frequency conversion planning method for a radio frequency signal, which belongs to the field of radio frequency signal processing, and comprises the following steps: performing down-conversion on a radio frequency time domain signal, extracting joint distribution of the radio frequency time domain signal, performing spectral analysis, and determining a frequency coverage range of the radio frequency signal; establishing a target optimization function in the sub-band division process of the radio frequency signal; randomly dividing N sub-frequency bands of the radio frequency signal based on the frequency coverage range of the radio frequency signal, optimizing the N sub-frequency bands by using crossover and variation of a genetic algorithm, and outputting an optimal continuous sub-frequency band; and calculating a frequency conversion control signal of the sub-frequency band in the optimal continuous sub-frequency band based on the output optimal continuous sub-frequency band. According to the scheme, segmented frequency conversion planning of a complex radio frequency signal can be realized so as to adapt to subsequent complex frequency spectrum requirements. The signal processing capability of the whole system is improved, the spurious interference is reduced, and the system adapts to complex and changeable radio frequency application scenes.

Description

Technical Field

[0001] The present invention relates to the field of radio frequency signal processing, and in particular to a segmented frequency conversion planning method for radio frequency signals. Background Art

[0002] In many fields such as modern communications and radar detection, the frequency conversion of RF signals plays a vital role. Traditional frequency conversion methods often use a single frequency conversion mode, such as directly using a fixed local oscillator frequency to mix with the input RF signal. This method has problems such as reduced spectrum purity, serious spurious interference, and inability to flexibly adapt to different frequency bands for efficient processing when facing complex spectrum requirements and high dynamic range signal processing. With the development of technology, the requirements for RF signal frequency conversion accuracy, efficiency, and adaptability are increasing, and an innovative frequency conversion planning method is urgently needed to meet diverse needs. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for segmented frequency conversion planning of radio frequency signals, which utilizes target optimization and deep genetic algorithm to implement segmented frequency conversion planning of radio frequency signals and improve the performance of radio frequency signal frequency conversion processing.

[0004] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: A method for planning segmented frequency conversion of a radio frequency signal is provided, comprising: Step S1: down-converting the RF time domain signal, extracting the joint distribution of the RF time domain signal, and performing spectrum analysis to determine the frequency coverage of the RF signal; Step S2: establishing a target optimization function in the process of dividing the frequency sub-bands of the radio frequency signal; Step S3: Randomly divide the radio frequency signal into N sub-bands, and use the crossover and mutation of genetic algorithms to N Optimize the sub-bands and output the optimal continuous sub-bands; Step S4: based on the output optimal continuous sub-frequency band, calculating the frequency conversion control signal of the sub-frequency band in the optimal continuous sub-frequency band.

[0005] Further, step S1 includes: Step S11: RF time domain signal Perform down-conversion and extract RF time domain signal based on Fourier transform The joint distribution of ; ; in, f is the signal frequency, t is the time domain of the signal, is the Fourier transform, is the carrier frequency, j is an imaginary unit, is Fourier convolution, is the transfer function of the reconfigurable filter bank, e is a natural constant; Step S12: Based on joint distribution For RF time domain signals Perform spectrum analysis to determine the frequency coverage of RF signals .

[0006] Furthermore, the objective optimization function Specifically: ; in, n is the number of the sub-band, For the division N A set of sub-bands, is the bandwidth set of all sub-bands, For the n The rated bandwidth of the sub-bands is For the n The transmit power of each sub-band, For the n The starting frequency of each sub-band, is the frequency band power weight coefficient, is the frequency band continuity weight coefficient, K is the rectangular coefficient of the filter, is the intermediate frequency of the subsequent signal processing chain, For the n The preset local oscillator frequency for each sub-band. is the IF bandwidth, is the phase noise power spectral density of the local oscillator source, is the noise power spectral density of the sub-band, is the signal-to-noise ratio threshold, is the minimum frequency interval ratio, For the n The stop frequency of each sub-band, N is the number of sub-bands, For the N The stop frequency of each sub-band, As constraints, d For the integral operation, Represents the minimum value function.

[0007] Further, step S3 includes: Step S31: Based on the frequency coverage of the radio frequency signal , and randomly divide the RF signal into N A continuous sub-band; ; in, For the N The frequency range of the sub-bands, For the N The starting frequency of each sub-band, For the N The stop frequency of the sub-band, and ; After sub-band division, the transfer function of the filter bank can be reconstructed for; ; in, Q is the dynamic adjustment coefficient, For the n The carrier frequency of each sub-band; Step S32: divide the currently divided N Continuous sub-bands Enter the target optimization function Calculate the current fitness value , is the scaling factor, e is a natural constant; Step S33: Use the ideal bandwidth of each sub-band currently divided as a genetic gene to construct the chromosome code of the genetic algorithm , , For the N Genetic gene; Step S34: Based on chromosome encoding , calculate the bandwidth difference between any two genetic genes, and select the genetic gene combination with the smallest bandwidth difference ; ; in, Chromosome encoding R The two genes with the smallest bandwidth difference, They are the numbers of the two genetic genes with the smallest difference; Step S35: Based on genetic combination Encoded in chromosomes The coding position within the gene The coding positions between them are exchanged to form a set of chromosome coding after genetic gene crossover transformation. , and encode the chromosome The corresponding divided NSub-band input target optimization function Calculate the fitness value ; Step S36: Compare fitness values With fitness value The size between; like , then execute steps S37-S310; like , then execute step S311; Step S37: Encode the chromosome The smallest genetic gene As the basis for mutation, according to the set mutation rate Mutate and obtain the mutated genetic gene The remaining genes undergo mutations. , get the mutated chromosome code , Encoding for chromosomes The genetic genes in k Encoding for chromosomes The number of the genetic gene in Step S38: Encode the chromosome The corresponding divided N Sub-band input target optimization function Calculate the fitness value ; Step S39: Return to step S37 and encode the chromosome The smallest genetic gene As the basis for mutation, and execute steps S37-S38 to calculate the fitness value ; Step S310: until M After the genetic mutation, M Fitness value , and filter out M Fitness value The minimum fitness value in ; like , then the output fitness minimum value The corresponding chromosome code , the chromosome encoding The corresponding divided N sub-bands as the planned optimal continuous sub-bands; like , then the chromosome encoding The corresponding divided N sub-bands as the planned optimal continuous sub-bands; Step S311: Screening out chromosome codes The bandwidth combination with the second smallest difference in medium bandwidth ,and , Encoding for chromosomes The two genes with the second smallest difference in bandwidth, Encoding for chromosomes The numbers of the two genes with the second smallest bandwidth difference are obtained; and step S35 is executed to form the chromosome code after the second genetic gene crossover transformation. , calculate the fitness value ; Step S312: Compare fitness values With fitness value The size between; like , then use chromosome encoding Execute steps S37-S310; like , then return to step S311, in the chromosome encoding Continue to perform crossover transformation of genetic genes until the optimal continuous sub-band is output; Step S313: If the chromosome code After all the genetic genes in the crossover transformation are completed, if the optimal continuous sub-band cannot be output, then return to step S31 and randomly divide again N continuous sub-frequency bands, and execute steps S31-S312.

[0008] Furthermore, the frequency conversion control signal ; ; in, is the gain coefficient, is the optimal continuous sub-band n The bandwidth of the sub-band, is the optimal continuous sub-band n The frequency of the sub-band, e is a natural constant.

[0009] The beneficial effects of the present invention are as follows: this scheme can realize the segmented frequency conversion planning of complex RF signals to adapt to subsequent complex spectrum requirements. The frequency segmentation of RF signals is optimized by constructing a target optimization function, and the optimal frequency band power and frequency band continuity are followed during the frequency segmentation process, while meeting the signal noise requirements. At the same time, the divided frequency bands are optimized based on genetic algorithms, which increases the accuracy of the segmented frequency conversion planning of RF signals and makes the frequency conversion planning more efficient, achieving precise frequency conversion for RF signals in different frequency bands, improving the signal processing capability of the overall system, reducing stray interference, and adapting to complex and changeable RF application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 The present invention is a flowchart of a method for planning a segmented frequency conversion of a radio frequency signal. DETAILED DESCRIPTION

[0011] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0012] like Figure 1 As shown, a segmented frequency conversion planning method for a radio frequency signal includes: Step S1: down-convert the RF time domain signal, extract the joint distribution of the RF time domain signal, and perform spectrum analysis to determine the frequency coverage of the RF signal. Step S1 specifically includes: Step S11: RF time domain signal Perform down-conversion and extract RF time domain signal based on Fourier transform The joint distribution of ; ; in, f is the signal frequency, t is the time domain of the signal, is the Fourier transform, is the carrier frequency, j is an imaginary unit, is Fourier convolution, is the transfer function of the reconfigurable filter bank, e is a natural constant; Step S12: Based on joint distribution For RF time domain signals Perform spectrum analysis to determine the frequency coverage of RF signals In this embodiment, by extracting the RF time domain signal The frequency domain diagram can provide clear frequency characteristics of the RF signal.

[0013] Step S2: Establishing the target optimization function in the process of sub-band division of RF signals ; ; in, n is the number of the sub-band, For the division N A set of sub-bands, is the bandwidth set of all sub-bands, For the n The rated bandwidth of the sub-bands is For the n The transmit power of each sub-band, For the n The starting frequency of each sub-band, is the frequency band power weight coefficient, is the frequency band continuity weight coefficient. Under normal conditions, the weight coefficient is , , K is the rectangular coefficient of the filter, is the intermediate frequency of the subsequent signal processing chain, For the n The preset local oscillator frequency for each sub-band. is the IF bandwidth, is the phase noise power spectral density of the local oscillator source, is the noise power spectral density of the sub-band, is the signal-to-noise ratio threshold, is the minimum frequency interval ratio, For the n The stop frequency of each sub-band, N is the number of sub-bands, For the N The stop frequency of each sub-band, As constraints, d For the integral operation, Represents the minimum value function.

[0014] Step S3: Randomly divide the radio frequency signal into N sub-bands, and use the crossover and mutation of genetic algorithms to N The sub-bands are optimized and the optimal continuous sub-bands are output. Step S3 specifically includes: Step S31: Based on the frequency coverage of the radio frequency signal , and randomly divide the RF signal into N A continuous sub-band; ; in, For the N The frequency range of the sub-bands, For the N The starting frequency of each sub-band, For the N The stop frequency of the sub-band, and ; After sub-band division, the transfer function of the filter bank can be reconstructed for; ; in, Q is the dynamic adjustment coefficient, For the n The reconfigurable filter bank can dynamically change the filtering characteristics to adapt to different signals or environmental changes, and can meet the needs of variable frequency signals by adjusting parameters or structures while maintaining stability.

[0015] Step S32: divide the currently divided N Continuous sub-bands Enter the target optimization function Calculate the current fitness value , is the scaling factor, e is a natural constant; Step S33: Use the ideal bandwidth of each sub-band currently divided as a genetic gene to construct the chromosome code of the genetic algorithm , , For the N Genetic gene; Step S34: Based on chromosome encoding , calculate the bandwidth difference between any two genetic genes, and select the genetic gene combination with the smallest bandwidth difference ; ; in, Chromosome encoding R The two genes with the smallest bandwidth difference, They are the numbers of the two genetic genes with the smallest difference; Step S35: Based on genetic combination Encoded in chromosomes The coding position within the gene The coding positions between them are exchanged to form a set of chromosome coding after genetic gene crossover transformation. , and encode the chromosome The corresponding divided N Sub-band input target optimization function Calculate the fitness value ; Step S36: Compare fitness values With fitness value The size between; like , then execute steps S37-S310; like , then execute step S311; The genetic algorithm logic used in this embodiment first performs crossover changes of genetic genes to perform large-scale chromosome coding changes, and then performs genetic mutation to achieve small-scale chromosome coding changes, ensuring that the optimal continuous sub-bands can be quickly output.

[0016] Step S37: Encode the chromosome The smallest genetic gene As the basis for mutation, according to the set mutation rate To mutate, generally take the mutation rate is 0.1, and the mutated genetic gene is obtained In order to ensure that the overall frequency range of the RF signal remains unchanged, the mutation value needs to be equally divided into other genetic genes to achieve the effect of overall genetic gene mutation. The remaining genetic genes are mutated. , get the mutated chromosome code , Encoding for chromosomes The genetic genes in k Encoding for chromosomes The number of the genetic gene in Step S38: Encode the chromosome The corresponding divided N Sub-band input target optimization function Calculate the fitness value ; Step S39: Return to step S37 and encode the chromosome The smallest genetic gene As the basis for mutation, and execute steps S37-S38 to calculate the fitness value ; Step S310: until M After the genetic mutation, M Fitness value , and filter outM Fitness value The minimum fitness value in ; like , then the output fitness minimum value The corresponding chromosome code , the chromosome encoding The corresponding divided N sub-bands as the planned optimal continuous sub-bands; like , then the chromosome encoding The corresponding divided N sub-bands as the planned optimal continuous sub-bands; Step S311: Screening out chromosome codes The bandwidth combination with the second smallest difference in medium bandwidth ,and , Encoding for chromosomes The two genes with the second smallest difference in bandwidth, Encoding for chromosomes The numbers of the two genes with the second smallest bandwidth difference are obtained; and step S35 is executed to form the chromosome code after the second genetic gene crossover transformation. , calculate the fitness value ; Step S312: Compare fitness values With fitness value The size between; like , then use chromosome encoding Execute steps S37-S310; like , then return to step S311, in the chromosome encoding Continue to perform crossover transformation of genetic genes until the optimal continuous sub-band is output; Step S313: If the chromosome code After all the genetic genes in the crossover transformation are completed, if the optimal continuous sub-band cannot be output, then return to step S31 and randomly divide again N continuous sub-frequency bands, and execute steps S31-S312.

[0017] Step S4: Based on the output optimal continuous sub-band, calculate the frequency conversion control signal of the sub-band in the optimal continuous sub-band ; ; in, is the gain coefficient, is the optimal continuous sub-band n The bandwidth of the sub-band, is the optimal continuous sub-band n The frequency of the sub-band, e is a natural constant.

[0018] The present invention can realize the segmented frequency conversion planning of complex RF signals to adapt to subsequent complex spectrum requirements. The frequency segmentation of RF signals is optimized by constructing a target optimization function. The optimal frequency band power and frequency band continuity are followed during the frequency segmentation process, while meeting the signal noise requirements. At the same time, the divided frequency bands are optimized based on genetic algorithms, which increases the accuracy of the segmented frequency conversion planning of RF signals and makes the frequency conversion planning more efficient. Accurate frequency conversion is achieved for RF signals in different frequency bands, improving the signal processing capability of the overall system, reducing stray interference, and adapting to complex and changeable RF application scenarios.

Claims

1. A method for planning segmented frequency conversion of a radio frequency signal, characterized in that: include: Step S1: down-converting the RF time domain signal, extracting the joint distribution of the RF time domain signal, and performing spectrum analysis to determine the frequency coverage of the RF signal; Step S2: establishing a target optimization function in the process of dividing the frequency sub-bands of the radio frequency signal; Step S3: Randomly divide the radio frequency signal into N sub-bands, and use the crossover and mutation of genetic algorithms to N Optimize the sub-bands and output the optimal continuous sub-bands; Step S4: based on the output optimal continuous sub-frequency band, calculating the frequency conversion control signal of the sub-frequency band in the optimal continuous sub-frequency band.

2. The method for planning segmented frequency conversion of radio frequency signals according to claim 1, characterized in that: The step S1 comprises: Step S11: RF time domain signal Perform down-conversion and extract RF time domain signal based on Fourier transform The joint distribution of ; ； in, f is the signal frequency, t is the time domain of the signal, is the Fourier transform, is the carrier frequency, j is an imaginary unit, is Fourier convolution, is the transfer function of the reconfigurable filter bank, e is a natural constant; Step S12: Based on joint distribution For RF time domain signals Perform spectrum analysis to determine the frequency coverage of RF signals .

3. The method for planning segmented frequency conversion of radio frequency signals according to claim 2, characterized in that: The objective optimization function Specifically: ； in, n is the number of the sub-band, For the division N A set of sub-bands, is the bandwidth set of all sub-bands, For the n The rated bandwidth of the sub-bands is For the n The transmit power of each sub-band, For the n The starting frequency of each sub-band, is the frequency band power weight coefficient, is the frequency band continuity weight coefficient, K is the rectangular coefficient of the filter, is the intermediate frequency of the subsequent signal processing chain, For the n The preset local oscillator frequency for each sub-band. is the IF bandwidth, is the phase noise power spectral density of the local oscillator source, is the noise power spectral density of the sub-band, is the signal-to-noise ratio threshold, is the minimum frequency interval ratio, For the n The stop frequency of each sub-band, N is the number of sub-bands, For the N The stop frequency of each sub-band, As constraints, d For the integral operation, Represents the minimum value function.

4. The method for planning segmented frequency conversion of radio frequency signals according to claim 3, characterized in that: The step S3 comprises: Step S31: Based on the frequency coverage of the radio frequency signal , and randomly divide the RF signal into N A continuous sub-band; ； in, For the N The frequency range of the sub-bands, For the N The starting frequency of each sub-band, For the N The stop frequency of the sub-band, and ; After sub-band division, the transfer function of the filter bank can be reconstructed for; ； in, Q is the dynamic adjustment coefficient, For the n The carrier frequency of each sub-band; Step S32: divide the currently divided N Continuous sub-bands Enter the target optimization function Calculate the current fitness value , is the scaling factor, e is a natural constant; Step S33: Use the ideal bandwidth of each sub-band currently divided as a genetic gene to construct the chromosome code of the genetic algorithm , , For the N Genetic gene; Step S34: Based on chromosome encoding , calculate the bandwidth difference between any two genetic genes, and select the genetic gene combination with the smallest bandwidth difference ; ； in, Chromosome encoding R The two genes with the smallest bandwidth difference, They are the numbers of the two genetic genes with the smallest difference; Step S35: Based on genetic combination Encoded in chromosomes The coding position within the gene The coding positions between them are exchanged to form a set of chromosome coding after genetic gene crossover transformation. , and encode the chromosome The corresponding divided N Sub-band input target optimization function , calculate the fitness value ; Step S36: Compare fitness values With fitness value The size between; like , then execute steps S37-S310; like , then execute step S311; Step S37: Encode the chromosome The smallest genetic gene As the basis for mutation, according to the set mutation rate Mutate and obtain the mutated genetic gene The remaining genes undergo mutations. , get the mutated chromosome code , Encoding for chromosomes The genetic genes in k Encoding for chromosomes The number of the genetic gene in Step S38: Encode the chromosome The corresponding divided N Sub-band input target optimization function , calculate the fitness value ; Step S39: Return to step S37 and encode the chromosome The smallest genetic gene As the basis for mutation, and execute steps S37-S38 to calculate the fitness value ; Step S310: until M After the genetic mutation, M Fitness value , and filter out M Fitness value The minimum fitness value in ; like , then the output fitness minimum value The corresponding chromosome code , the chromosome encoding The corresponding divided N sub-bands as the planned optimal continuous sub-bands; like , then the chromosome encoding The corresponding divided N sub-bands as the planned optimal continuous sub-bands; Step S311: Screening out chromosome codes The bandwidth combination with the second smallest difference in medium bandwidth ,and , Encoding for chromosomes The two genes with the second smallest difference in bandwidth, Encoding for chromosomes The numbers of the two genes with the second smallest bandwidth difference are obtained; and step S35 is executed to form the chromosome code after the second genetic gene crossover transformation. , calculate the fitness value ; Step S312: Compare fitness values With fitness value The size between; like , then use chromosome encoding Execute steps S37-S310; like , then return to step S311, in the chromosome encoding Continue to perform crossover transformation of genetic genes until the optimal continuous sub-band is output; Step S313: If the chromosome code After all the genetic genes in the crossover transformation are completed, if the optimal continuous sub-band cannot be output, then return to step S31 and randomly divide again N continuous sub-frequency bands, and execute steps S31-S312.

5. The method for planning segmented frequency conversion of radio frequency signals according to claim 4, characterized in that: The frequency conversion control signal ; ； in, is the gain coefficient, is the optimal continuous sub-band n The bandwidth of the sub-band, is the optimal continuous sub-band n The frequency of the sub-band, e is a natural constant.

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