System and method for constructing shortwave channel prediction model based on Chirp detection signal

Abstract

A system and method for constructing a shortwave channel prediction model based on chirp detection signals belongs to the field of artificial intelligence technology. The system includes a time window division module, a measurement module based on chirp detection signals, and a shortwave channel prediction model construction module. The time window division module divides the Earth's rotation period into N time windows. The chirp detection signal-based measurement module measures the ionization state and optimal shortwave frequency in each time window at a set location within M Earth rotation periods. The shortwave channel prediction model construction module establishes a shortwave channel prediction model for each of the N time windows based on the measurement data provided by the measurement module. The shortwave channel prediction model is trained using a neural network. The present invention can obtain the optimal shortwave channel for different time windows.

Description

Technical Field

[0001] The present invention relates to a system and method for constructing a shortwave channel prediction model based on a Chirp detection signal, and belongs to the technical field of artificial intelligence. Background Art

[0002] Chinese invention patent application publication number CN103117823A discloses a shortwave channel modeling method, comprising: extracting M samples of a link from a database storing shortwave channel parameter samples, and obtaining the multipath stretch parameter and Doppler stretch parameter from each sample; constructing a channel parameter matrix using the multipath stretch parameter and Doppler stretch parameter of each sample as column vectors, and normalizing each vector in the matrix; defining each column of the channel parameter matrix as an array point, and calculating the cluster center of each array point using a two-dimensional clustering combination algorithm; and detecting whether the number of array points covered within the neighborhood radius of each cluster center meets the clustering requirement. If so, denormalizing the cluster center, and using the multipath stretch and Doppler stretch parameters corresponding to the denormalized cluster center as the shortwave channel model. This invention addresses the problem that existing channel modeling techniques produce large model errors and cannot accurately describe link channel characteristics.

[0003] However, the shortwave channel propagated by the ionosphere is seriously affected by time, and the invention does not provide any guidance on how to select the best shortwave channel in different time periods. Summary of the Invention

[0004] In order to overcome the shortcomings of the prior art, the object of the present invention is to provide a system and method for constructing a shortwave channel prediction model based on Chirp detection signals, which can establish corresponding optimal shortwave channels for different time windows.

[0005] To achieve the above-mentioned object of the invention, the present invention provides a shortwave channel prediction model construction system based on Chirp detection signals, which includes: a time window division module, a measurement module and a shortwave channel prediction model construction module, wherein the time window division module divides the earth's rotation period into N time windows; the measurement module includes an ionospheric ionization state measurement module and an optimal shortwave frequency measurement module based on Chirp detection signals, the ionospheric ionization state measurement module is used to measure the ionospheric ionization state of each time window at a set location within M earth rotation periods; the optimal shortwave frequency measurement module based on Chirp detection signals is used to measure the optimal shortwave frequency at a set location within M earth rotation periods; the shortwave channel prediction model construction module constructs a shortwave channel prediction model for each of the N time windows according to the measurement data provided by the measurement module, and the shortwave channel prediction model is trained by a neural network, and the neural network includes an LLM model, N=2 k , k is a positive integer greater than or equal to 3, and M is a positive integer greater than or equal to 364.

[0006] To achieve the above-mentioned object, the present invention further provides a method for constructing a shortwave channel prediction model based on a Chirp detection signal, which comprises the following steps:

[0007] The Earth's rotation period is divided into N time windows through the time window division module, N=2 k , k is a positive integer greater than or equal to 3;

[0008] Measuring the ionospheric ionization state of each time window at a set location within M Earth rotation periods by an ionospheric ionization state measurement module, where M is a positive integer greater than or equal to 364;

[0009] The optimal shortwave frequency measurement module based on Chirp detection signals is used to measure the optimal shortwave frequency at a set location within M earth rotation periods;

[0010] A shortwave channel prediction model building module is used to build a shortwave channel prediction model for each of the N time windows based on the measurement data provided by the measurement module. The shortwave channel prediction model is trained by a neural network.

[0011] Compared with the prior art, the system and method for constructing a shortwave channel prediction model based on Chirp detection signals provided by the present invention have the following beneficial effects:

[0012] The present invention divides the earth's rotation period into N time windows and constructs a shortwave channel prediction model for each of the N time windows, thereby obtaining the best shortwave channel for different time windows. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a block diagram of the shortwave channel prediction model construction system based on Chirp detection signals provided by the present invention.

[0014] Figure 2 This is a block diagram of the composition of the ionospheric ionization state measurement module provided by the present invention.

[0015] Figure 3 The present invention provides a block diagram of the optimal shortwave frequency measurement module based on Chirp detection signals. DETAILED DESCRIPTION

[0016] It should be noted that preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Advantages and features of the present invention and methods for achieving these advantages and features will become clear with reference to the accompanying drawings and the embodiments described below in detail.

[0017] However, the present invention is not limited to the embodiments disclosed below and can be implemented in a variety of different forms. This embodiment is only used to make the disclosure of the present invention more complete and to fully inform ordinary technicians in the technical field to which the present invention belongs of the scope of the invention. The present invention is only defined by the scope of protection claimed in the invention.

[0018] Although "first," "second," and the like are used to describe various elements, components, and / or parts, these elements, components, and / or parts are not limited by these terms. These terms are only used to distinguish one element, constituent element, or part from other elements, constituent elements, or parts. Therefore, it is obvious that within the technical spirit of the present disclosure, the first element, first component, or first part mentioned below may also be the second element, second component, or second part, and the terms used in this specification are only used to describe the embodiments and are not intended to limit the present disclosure.

[0019] Figure 1 The present invention provides a shortwave channel prediction model construction system based on Chirp detection signals, such as Figure 1 As shown, the shortwave channel prediction model construction system based on the Chirp detection signal of the present invention includes: a time window division module, a measurement module and a shortwave channel prediction model construction module, wherein the time window division module divides the earth's rotation period into N time windows; the measurement module includes an ionospheric ionization state measurement module and an optimal shortwave frequency measurement module based on the Chirp detection signal, the ionospheric ionization state measurement module is used to measure the ionospheric ionization state of each time window at a set location within M earth rotation periods; the optimal shortwave frequency measurement module based on the Chirp detection signal is used to measure the optimal shortwave frequency at a set location within M earth rotation periods; the shortwave channel prediction model construction module constructs a shortwave channel prediction model for each of the N time windows according to the measurement data provided by the measurement module, and the shortwave channel prediction model is trained by a neural network, N=2 k , k is a positive integer greater than or equal to 3.

[0020] In the present invention, if k = 3, the Earth's 24-hour rotation period is divided into eight time windows: 0-3 a.m. is the first time window, 3-6 a.m. is the second time window, 6-9 a.m. is the third time window, 9-12 a.m. is the fourth time window, 12-15 a.m. is the fifth time window, 15-18 a.m. is the sixth time window, 18-21 a.m. is the seventh time window, and 21-24 a.m. is the eighth time window. Shortwave channel prediction models for the first to eighth time windows are established, respectively.

[0021] In the present invention, in order to make the shortwave frequency selected by the shortwave channel prediction model more accurate, the earth's rotation period can be divided into more time windows. For example, when k=4, the earth's rotation period of 24 hours is divided into 16 time windows; when k=5, the earth's rotation period of 24 hours is divided into 32 time windows, etc. A shortwave channel prediction model is established for each time window. Since the angle between the shortwave communication location and the sun is different in each time window, the ionization state of the ionosphere above the communication location is also different. Therefore, the communication effect is also different. Establishing different shortwave channel prediction models for different time windows to select shortwave frequencies can greatly improve the quality of shortwave communication.

[0022] In the present invention, training a shortwave channel prediction model using a neural network includes the following steps:

[0023] S1-1: Measure the state of the ionosphere at a set location in each time window within M Earth rotation periods;

[0024] S1-2: Order ;

[0025] S1-3: Get M n-th time windows for each Earth rotation period Ionospheric status data at the set communication location And measure the corresponding series of optimal shortwave frequencies ;

[0026] S1-4: Command ;

[0027] S1-5, from ionospheric state data and select the mth group of data from the optimal shortwave frequency ( , and Input to the input layer of the neural network, and output vector from the output layer of the neural network ;

[0028] ,

[0029] is the current parameter vector of the neural network;

[0030] S1-6: Set the loss function for:

[0031] ,

[0032] The gradient is calculated according to the following formula:

[0033] ;

[0034] S1-7: Update the current parameter vector of the neural network according to the following formula:

[0035] ;

[0036] Where, is the learning rate, Express gradient;

[0037] S1-8: Determine the loss function Is it the minimum, if so, then get the n-th shortwave channel prediction model, and then perform step S1-9; if not, make the new parameter vector replace the current parameter vector, so , and return to S1-5;

[0038] S1-9: Determine whether n is greater than or equal to N. If n is greater than or equal to N, obtain N shortwave channel prediction models and end; if n is less than N, n , return to step S1-3.

[0039] Figure 2 This is a block diagram of the ionospheric ionization state measurement module provided by the present invention; Figure 2 As shown, a laser source generator, an FM / AM array, an FM / AM signal generator, a splitter, a laser projector, an optical detector array, an acquisition device and a first processor are provided, wherein the FM / AM array performs FM / AM modulation on multiple carriers of the laser source generator using multiple modulation signals generated by the FM / AM signal generator to obtain multiple FM / AM waves; the optical splitter divides the multiple FM / AM waves generated by the modulator into two paths, one for the transmitted light signal and the other for the reference light signal; the laser projector projects the transmitted light signal toward the ionosphere, and the light signal reflected or scattered by the ionosphere and the reference light signal are input together into the optical detector array; the acquisition device records the amplitude modulation signal and the double frequency signal of the frequency modulation signal of each detector in the optical detector array during a unit duration τ to obtain P×2 samples, wherein P is the number of detectors in the optical detector array; wherein P is the number of detectors in the optical detector array, which is a positive integer greater than or equal to 6; the first processor obtains the ionization state of the ionosphere based on the P×2 samples.

[0040] In the first embodiment, the following matrix is ​​generated based on P×2 samples:

[0041] .

[0042] The first processor includes a data preprocessing module, which calculates the concentration of the pth component contained in the ionosphere according to the following formula: :

[0043] , where The carrier frequency is The amplitude modulation degree of the FM / AM wave; The carrier frequency is The optical path length of the FM / AM wave; The receiving carrier frequency is The amplitude of the amplitude modulation signal of the FM wave; The received carrier frequency is The amplitude of the 2-fold frequency signal of the FM / AM wave; Frequency The ionosphere below contains the absorbance of the pth component, It is the frequency modulation degree of FM / AM wave; , The carrier frequency is The modulation amplitude of the FM wave; is the full width at half maximum of the absorption spectrum of the pth component contained in the ionosphere. In the present invention, yes An integer multiple of p = 1, .., P, where P is greater than or equal to 6. The ionosphere contains ions and molecules.

[0044] The carrier frequency signal of the FM / AM wave is the center frequency of the mth component absorption spectrum of the ionosphere ; 2.2 times the full width at half maximum of the absorption spectrum of the mth component contained in the ionosphere.

[0045] Figure 3 This is a block diagram of the optimal shortwave frequency measurement module based on Chirp detection signal provided by the present invention. Figure 3 As shown, the optimal shortwave frequency measurement module based on the Chirp detection signal includes a transmitting device and a receiving device, and the transmitting device includes: a shortwave frequency generator, a modulator array, a Chirp detection signal generator, a power amplifier array and a transmitting antenna array, wherein the modulator array uses the Chirp detection signal generated by the Chirp detection signal generator to modulate the frequencies of multiple carriers generated by the short frequency generator to generate multiple frequency-modulated pulse electrical signals of pulses with bandwidth B and duration τ; the power amplifier array amplifies the multiple frequency-modulated pulse electrical signals and transmits electromagnetic waves to the ionosphere through each of the transmitting antenna arrays.

[0046] The receiving device includes a receiving antenna array, a mixer array, a detector array and a second processor, wherein the receiving antenna array receives a series of multiple frequency-modulated pulse electrical signals refracted from the ionosphere; the mixer array mixes the multiple frequency-modulated pulse electrical signals refracted from the ionosphere with local oscillator telecommunications to generate multiple beat frequency signals; the detector array detects Chirp detection signals from the multiple beat frequency signals, and the second processor compares the parameters of each Chirp detection signal with the local Chirp reference signal. The carrier signal of the frequency-modulated pulse electrical signal with the maximum power and the most similarity to the local Chirp reference signal is the optimal shortwave channel corresponding to the transmission time window.

[0047] According to another embodiment of the present invention, a small signal amplifier array is provided between the receiving antenna array and the mixer array, which performs small signal amplification on the refracted wave received by each antenna in the receiving antenna array and provides the amplifier to the corresponding mixer of the mixer array.

[0048] According to another embodiment of the present invention, an intermediate frequency signal amplifier array is provided between the mixer array and the detector array to amplify the intermediate frequency signal generated by each mixer of the mixer array and provide the signal to the corresponding detector of the detector array.

[0049] The present invention also provides a method for constructing a shortwave channel prediction model based on a Chirp detection signal, which comprises the following steps:

[0050] The Earth's rotation period is divided into N time windows through the time window division module, N=2 k , k is a positive integer greater than or equal to 3;

[0051] Measuring the ionospheric ionization state of each time window at a set location within M Earth rotation periods by an ionospheric ionization state measurement module, where M is a positive integer greater than or equal to 364;

[0052] The optimal shortwave frequency measurement module based on Chirp detection signals is used to measure the optimal shortwave frequency at a set location within M earth rotation periods;

[0053] A shortwave channel prediction model building module is used to build a shortwave channel prediction model for each of the N time windows based on the measurement data provided by the measurement module. The shortwave channel prediction model is trained by a neural network.

[0054] When applying the shortwave channel prediction model provided by the present invention, the longitude difference between the preset communication location and the measurement location set when establishing the shortwave channel prediction model can be calculated first, and the angle between the communication location and the measurement location set when establishing the shortwave channel prediction model relative to the sun is calculated based on the longitude difference, and the time required for the earth to rotate by the angle is calculated based on the angle; according to the relationship between the communication location and the set measurement location, the time is added or subtracted, and the shortwave channel prediction model corresponding to the time window is selected. For example, a shortwave channel prediction model with N time windows is established by measuring the ionization state of the ionosphere at a position of 150 degrees longitude, and the communication location is located at 180 degrees longitude. The communication time should be subtracted from the time taken for the earth to rotate 30 degrees, that is, 2 hours, and then the corresponding shortwave channel prediction model is used for the time window that falls.

[0055] When applying the shortwave channel prediction model provided by the present invention, the mapping relationship between the climate at the preset communication time and the climate when the shortwave channel prediction model is established can be calculated first, and the shortwave channel prediction models of different time windows can be selected according to the mapping relationship. For example, if the climate at the preset communication time is cloudy and the shortwave channel prediction model established in the same time window is sunny, the shortwave channel prediction model of the evening time window is selected as the shortwave channel prediction model.

[0056] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A shortwave channel prediction model construction system based on Chirp detection signals, characterized in that: include: A time window division module, a measurement module and a shortwave channel prediction model construction module, wherein the time window division module divides the earth's rotation period into N time windows; the measurement module includes an ionospheric ionization state measurement module and an optimal shortwave frequency measurement module based on a Chirp detection signal, the ionospheric ionization state measurement module is used to measure the ionospheric ionization state of each time window at a set location within M earth rotation periods; the optimal shortwave frequency measurement module based on a Chirp detection signal is used to measure the optimal shortwave frequency at a set location within M earth rotation periods; the shortwave channel prediction model construction module constructs a shortwave channel prediction model for each of the N time windows based on the measurement data provided by the measurement module, N=2 k , k is a positive integer greater than or equal to 3, and M is a positive integer greater than or equal to 364.

2. The shortwave channel prediction model construction system based on Chirp detection signal according to claim 1 is characterized in that: The shortwave channel prediction model building module establishes a shortwave channel prediction model for each of the N time windows based on the measurement data provided by the measurement module, including the following steps: S1-1: Measure the state of the ionosphere at a set location in each time window within M Earth rotation periods; S1-2: Make ; S1-3: Get the nth time window of M Earth rotation periods Ionospheric status data at the set communication location And measure the corresponding series of optimal shortwave frequencies ; S1-4: Make ; S1-5, from ionospheric state data and select the mth group of data from the optimal shortwave frequency ( , and Input to the input layer of the neural network, and output vector from the output layer of the neural network : ， is the current parameter vector of the neural network; S1-6: Set the loss function for: ， The gradient is calculated according to the following formula: ； S1-7: Update the current parameter vector of the neural network according to the following formula: ； Where, is the learning rate, Express gradient; S1-8: Determine the loss function Is it the minimum, if so, then get the n-th shortwave channel prediction model, and then perform step S1-9; if not, make the new parameter vector replace the current parameter vector, so , and return to S1-5; S1-9: Determine whether n is greater than or equal to N. If n is greater than or equal to N, obtain N shortwave channel prediction models and end; if n is less than N, n , return to step S1-3.

3. The shortwave channel prediction model construction system based on Chirp detection signal according to claim 2, characterized in that: The ionospheric ionization state measurement module includes: a laser source generator, an FM / AM array, an FM / AM signal generator, a splitter, a laser projector, an optical detector array, an acquisition device and a first processor, wherein the FM / AM array performs FM / AM modulation on multiple carriers of the laser source generator using multiple modulation signals generated by the FM / AM signal generator to obtain multiple FM / AM waves; the optical splitter divides the multiple FM / AM waves generated by the modulator into two paths, one for the transmitted light signal and the other for the reference light signal; the laser projector projects the transmitted light signal toward the ionosphere, and the light signal reflected or scattered by the ionosphere and the reference light signal are input together into the optical detector array; the acquisition device records the amplitude modulation signal and the double frequency signal of the frequency modulation signal of each detector in the optical detector array during a unit duration τ to obtain P×2 samples, wherein P is the number of detectors in the optical detector array; the first processor obtains the ionization state of the ionosphere based on the P×2 samples, where P is a positive integer greater than or equal to 6.

4. The shortwave channel prediction model construction system based on Chirp detection signals according to claim 2, characterized in that: The optimal shortwave frequency measurement module based on the Chirp detection signal includes a transmitting device and a receiving device, wherein the transmitting device includes: a shortwave frequency generator, a modulator array, a Chirp detection signal generator, a power amplifier array and a transmitting antenna array, wherein the modulator array uses the Chirp detection signal generated by the Chirp detection signal generator to modulate the frequencies of multiple carriers generated by the short frequency generator to generate multiple frequency-modulated pulse electrical signals of pulses with a bandwidth B and a duration τ; the power amplifier array amplifies the multiple frequency-modulated pulse electrical signals and transmits electromagnetic waves to the ionosphere through each of the transmitting antenna arrays.

5. The shortwave channel prediction model construction system based on Chirp detection signals according to claim 4 is characterized in that: The receiving device includes a receiving antenna array, a mixer array, a detector array and a second processor, wherein the receiving antenna array receives a series of multiple frequency-modulated pulse electrical signals refracted from the ionosphere; the mixer array mixes the multiple frequency-modulated pulse electrical signals refracted from the ionosphere with local oscillator telecommunications to generate multiple beat frequency signals; the detector array detects Chirp detection signals from the multiple beat frequency signals, and the second processor compares the parameters of each Chirp detection signal with the local Chirp reference signal. The carrier signal of the frequency-modulated pulse electrical signal with the maximum power and the most similarity to the local Chirp reference signal is the optimal shortwave channel corresponding to the transmission time window.

6. A method for constructing a shortwave channel prediction model based on Chirp detection signals, characterized in that: The steps include: The Earth's rotation period is divided into N time windows through the time window division module, N=2 k , k is a positive integer greater than or equal to 3; Measuring the ionospheric ionization state of each time window at a set location within M Earth rotation periods by an ionospheric ionization state measurement module, where M is a positive integer greater than or equal to 364; The optimal shortwave frequency measurement module based on Chirp detection signals is used to measure the optimal shortwave frequency at a set location within M Earth rotation periods; A shortwave channel prediction model building module is used to build a shortwave channel prediction model for each of the N time windows according to the measurement data provided by the measurement module.

7. The method for constructing a shortwave channel prediction model based on Chirp detection signals according to claim 6, wherein: The shortwave channel prediction model building module establishes a shortwave channel prediction model for each of the N time windows based on the measurement data provided by the measurement module, including the following steps: S1-1: Measure the state of the ionosphere at a set location in each time window within M Earth rotation periods; S1-2: Make ; S1-3: Get the nth time window of M Earth rotation periods Ionospheric status data at the set communication location And measure the corresponding series of optimal shortwave frequencies ; S1-4: Make ; S1-5, from ionospheric state data and select the mth group of data from the optimal shortwave frequency ( , and Input to the input layer of the neural network, and output vector from the output layer of the neural network : ， is the current parameter vector of the neural network; S1-6: Set the loss function for: ， The gradient is calculated according to the following formula: ； S1-7: Update the current parameter vector of the neural network according to the following formula: ； Where, is the learning rate, Express gradient; S1-8: Determine the loss function Is it the minimum, if so, then get the n-th shortwave channel prediction model, and then perform step S1-9; if not, make the new parameter vector replace the current parameter vector, so , and return to S1-5; S1-9: Determine whether n is greater than or equal to N. If n is greater than or equal to N, obtain N shortwave channel prediction models and end; if n is less than N, n , return to step S1-3.

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