Short-wave channel modeling method and system based on signal wavefront recovery

Abstract

The invention belongs to the technical field of short-wave communication channel modeling, and discloses a short-wave channel modeling method and system based on signal wavefront recovery, and the method comprises the steps: projecting standard plane waves to an antenna array from different angles through a cooperative target radiation source, collecting antenna array signals in different directions under the condition of no disturbance, and storing the signals as calibration data; calculating the time delay of each antenna before receiving the wavefront relative to the center of the antenna array, calculating the relative time delay of the time delay and the calibration data, and multiplying the relative time delay by the wavelength to realize the time domain-space domain conversion of wavefront distortion to obtain wavefront slope distribution; and calculating the size of a Zernike polynomial coefficient of each order based on wavefront slope distribution, analyzing specific components of aberration contained in signal wavefront, and performing wavefront fitting reconstruction so as to complete short-wave channel modeling. According to the method, the Zernike polynomial is adopted to fit the wavefront disturbance quantity, the influence of the ionized layer on the wavefront of the short-wave signal is quantified in real time, and a high-precision channel model is constructed.

Description

Technical Field

[0001] The present invention relates to the technical field of shortwave communication channel modeling, and particularly to a shortwave channel modeling method and system based on signal wavefront restoration. Background Art

[0002] The research on shortwave channel modeling and simulation began with the exploration of ionospheric characteristics. The multipath effect is a significant factor affecting shortwave communication signals by the ionosphere. Existing modeling methods mainly include three categories: statistical modeling, semi-deterministic modeling, and deterministic modeling.

[0003] In terms of statistical modeling: Since 1970, C. Watterson et al. have long measured and studied the ionospheric reflection channel, established the Watterson channel statistical model, adopted the tapped delay line structure, and simulated the multipath effect by manually setting multiple time delays τ. Subsequently, Lacaze et al. proposed the random Gaussian delay model by assuming that the delay of each path in the shortwave channel propagation follows a Gaussian distribution. In 2000, Milson connected a parabolic phase filter at the output end to represent the group delay of the channel, realizing the simulation of a 1MHz bandwidth shortwave channel.

[0004] In terms of semi-deterministic modeling: Since the United States Navy developed broadband shortwave channel detection equipment in the mid-1980s, researchers have accumulated a large amount of measured data of shortwave channels; Vogler and Hoffmeyer first considered environmental factors such as location, time, and season, and proposed the Vogler shortwave channel model that combines the ionospheric prediction model, the deterministic model (multipath time delay), and the random model (Doppler frequency shift and spread). In 1997, J. Mastrangelo et al. of the International Telecommunication Society (ITS) launched the ITS model, and obtained parameters such as path time delay, Doppler frequency shift, and Doppler spread through actual channel detection on the basis of the Vogler model.

[0005] In terms of deterministic models: Both the Watterson statistical model and the Vogler semi-deterministic model set the time delay parameters of the multipath at the signal level, and did not explore the actual path of electromagnetic waves at the physical layer. In 2015, Gao Zhibin et al. used the ray tracing modeling method based on angle search to model the wireless signal path of an airborne platform carrying a wireless 2.5GHz transceiver, with a distance of 95 - 135 meters and a height of 100 meters. In 2020, the team proposed an improved channel simulation algorithm based on space division ray tracing for tracking the propagation path of electromagnetic waves on the sea surface.

[0006] The above-mentioned deterministic models combine prior knowledge of the channel, but the shortwave channel is longer and more complex.

[0007] The current mainstream Watterson, Vogler, and ITS models play a certain role in various simulation and actual measurement verification experiments. However, existing modeling methods are difficult to accurately characterize the non-linear dynamic characteristics of short-wave channels. It is necessary to accurately describe the mechanism of the ionosphere's effect on short-wave electromagnetic wave propagation and establish a channel model with higher accuracy. Summary of the Invention

[0008] In view of the problem that existing modeling methods are difficult to accurately characterize the non-linear dynamic characteristics of short-wave channels, by analyzing the mathematical and physical processes of the ionosphere's effect on short-wave signal electromagnetic wave propagation, from the perspective of physical approximation, the present invention proposes a short-wave channel modeling method and system based on signal wavefront restoration, accurately simulating the complex characteristics of short-wave channels and providing a theoretical basis for high-precision signal inversion.

[0009] To achieve the above object, the present invention adopts the following technical solutions:

[0010] On the one hand, the present invention proposes a short-wave channel modeling method based on signal wavefront restoration, including:

[0011] Step 1: Use a cooperative target radiation source to project a standard plane wave onto the antenna array from different angles, collect the antenna array signals without disturbance in different directions, and store them as calibration data;

[0012] Step 2: Calculate the time delay of the received wavefront of each antenna relative to the center of the antenna array, calculate the relative time delay between the time delay and the calibration data, and multiply the relative time delay by the wavelength to achieve the time-domain to spatial-domain conversion of wavefront distortion, obtaining the wavefront slope distribution;

[0013] Step 3: Calculate the magnitude of the Zernike polynomial coefficients of each order based on the wavefront slope distribution, fit the Zernike polynomial coefficients to calculate the wavefront, analyze the specific components of the aberration contained in the signal wavefront according to the magnitude of the Zernike polynomial coefficients of each order, complete the wavefront fitting and reconstruction, and thus complete the short-wave channel modeling.

[0014] Further, the method further includes:

[0015] Step 4: Optimize and select the order of the Zernike polynomial to improve the wavefront restoration accuracy.

[0016] Further, in the above Step 2, the time-domain to spatial-domain conversion of wavefront distortion is achieved in the following manner to obtain the wavefront slope distribution:

[0017]

[0018] Wherein, It represents the wavefront slope of the i-th antenna, △Φ is the wavefront distortion, ρ is the radial direction of the circular antenna array, θ is the tangential direction of the circular antenna array, i is the antenna serial number, d is the distance between the i-th antenna and the center of the antenna array, △ρ and △θ are respectively the components of the overall distortion amount △t·λ in the radial and tangential directions, △t is the relative time delay of the time delay and the calibration data, and λ is the wavelength.

[0019] Further, in step 3, the wavefront is calculated by fitting the Zernike polynomial coefficients in the following manner:

[0020]

[0021] where, Z k (ρ,θ) is the k-th order Zernike polynomial, C k is the coefficient of the k-th order Zernike polynomial, n is the order of the Zernike polynomial, Φ0(ρ,θ) = 0 indicates that the reference wavefront is a plane wave, and Φ(ρ,θ) = △Φ(ρ,θ) indicates the wavefront containing aberration.

[0022] On the other hand, the present invention proposes a short-wave channel modeling system based on signal wavefront restoration, including:

[0023] A calibration module, which is used to project a standard plane wave from a cooperative target radiation source to the antenna array from different angles, collect the antenna array signals under different directions and without disturbance, and store them as calibration data;

[0024] A phase difference calculation module, which is used to calculate the time delay of the received wavefront of each antenna relative to the center of the antenna array, calculate the relative time delay of the time delay and the calibration data, and multiply the relative time delay by the wavelength to realize the time-domain to space-domain conversion of the wavefront distortion, and obtain the wavefront slope distribution;

[0025] A wavefront restoration module, which is used to calculate the magnitude of the coefficients of each order of Zernike polynomial based on the wavefront slope distribution, fit the Zernike polynomial coefficients to calculate the wavefront, analyze the specific components of the aberration contained in the signal wavefront according to the magnitude of the coefficients of each order of Zernike polynomial, complete the wavefront fitting and reconstruction, and further complete the short-wave channel modeling.

[0026] Further, the system further includes:

[0027] An optimization module, which is used to optimize the order of the selected Zernike polynomial to improve the wavefront restoration accuracy.

[0028] Further, in the phase difference calculation module, the time-domain to space-domain conversion of the wavefront distortion is realized in the following manner to obtain the wavefront slope distribution:

[0029]

[0030] where, It represents the wavefront slope of the i-th antenna, △Φ is the wavefront distortion, ρ is the radial direction of the circular antenna array, θ is the tangential direction of the circular antenna array, i is the antenna serial number, d is the distance between the i-th antenna and the center of the antenna array, △ρ and △θ are respectively the components of the overall distortion amount △t·λ in the radial and tangential directions, △t is the relative time delay between the time delay and the calibration data, and λ is the wavelength.

[0031] Furthermore, in the wavefront restoration module, the wavefront is calculated by fitting the Zernike polynomial coefficients in the following manner:

[0032]

[0033] Among them, Z k (ρ,θ) is the k-th order Zernike polynomial, C k is the coefficient of the k-th order Zernike polynomial, n is the order of the Zernike polynomial, Φ0(ρ,θ) = 0 indicates that the reference wavefront is a plane wave, and Φ(ρ,θ) = △Φ(ρ,θ) indicates the wavefront containing aberration.

[0034] Compared with the prior art, the beneficial effects of the present invention are:

[0035] The present invention proposes a short-wave channel modeling method and system based on signal wavefront restoration, which breaks through the problem of insufficient characterization of channel characteristics in the original modeling method, attempts to overcome the influence of the ionosphere on signal fingerprints, and provides a modeling method close to the physical layer; by accurately modeling the short-wave channel, it ensures the high-precision restoration of short-wave signals and provides technical support for signal detection, recognition, and signal interpretation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is the basic flowchart of a short-wave channel modeling method based on signal wavefront restoration according to an embodiment of the present invention;

[0037] Figure 2 is the schematic diagram of the restored signal wavefront provided by an embodiment of the present invention;

[0038] Figure 3 is the schematic diagram of the architecture of a short-wave channel modeling system based on signal wavefront restoration according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The following further explains the present invention with reference to the drawings and specific embodiments:

[0040] During the propagation of short-wave signals in the ionosphere, affected by multiple factors such as the electron density gradient and geomagnetic disturbance, the wavefront phase is distorted. To accurately quantify the distortion, it is first necessary to establish a reference of the array response under non-disturbed conditions. The present invention uses the Zernike polynomial to fit the wavefront disturbance amount, quantifies the influence of the ionosphere on the short-wave signal wavefront in real time, and constructs a high-precision channel model. AsFigure 1 As shown in the figure, the present invention specifically proposes a short-wave channel modeling method based on signal wavefront restoration, including the following steps:

[0041] S101: Use a cooperative target radiation source to project a standard plane wave onto the antenna array from different angles, collect the antenna array signals under different directions and without disturbance, and store them as calibration data;

[0042] S102: Calculate the time delay of the received wavefront of each antenna relative to the center of the antenna array, calculate the relative time delay between the time delay and the calibration data, and multiply the relative time delay by the wavelength to achieve the time-domain to space-domain conversion of the wavefront distortion, and obtain the wavefront slope distribution;

[0043] S103: Calculate the magnitude of the Zernike polynomial coefficients of each order based on the wavefront slope distribution, fit the Zernike polynomial coefficients to calculate the wavefront, and analyze the specific components of the aberration contained in the signal wavefront according to the magnitude of the Zernike polynomial coefficients of each order, complete the wavefront fitting and reconstruction, and then complete the short-wave channel modeling.

[0044] Further, after the S103, it further includes:

[0045] S104: Optimize the order of the selected Zernike polynomial to achieve high-precision restoration of the radiation source wavefront under ionospheric disturbance, and accurately characterize the signal amplitude and phase changes brought about by the ionospheric disturbance.

[0046] Further, in S101, the plane wave reference calibration method is adopted for the antenna array, and through space-frequency joint calibration, the interference of the antenna array hardware error on the channel modeling is eliminated. During the calibration process, use a cooperative target radiation source to project a standard plane wave onto the antenna array from different angles, collect the antenna array signals under different directions and without disturbance, and store them as calibration data.

[0047] Further, in S102, when actually receiving short-wave signals passing through the ionosphere, since the direction-finding station has completed rough positioning, first calculate the time delay of the received wavefront of each antenna relative to the center of the antenna array, then calculate the relative time delay relationship between the time delay and the calibration data, and multiply the relative time delay by the wavelength to achieve the time-domain to space-domain conversion of the wavefront distortion.

[0048]

[0049] Among them, represents the wavefront slope of the i-th antenna, △Φ is the wavefront distortion, ρ is the radial direction of the circular antenna array, θ is the tangential direction of the circular antenna array, i is the antenna serial number, d is the distance between the i-th antenna and the center of the antenna array, △ρ and △θ are respectively the components of the overall distortion amount △t·λ in the radial and tangential directions, △t is the relative time delay between the time delay and the calibration data, and λ is the wavelength.

[0050] Further, in S103, after obtaining the wavefront slope distribution by using Equation (1), calculate the magnitude of the Zernike polynomial coefficients of each order based on the wavefront slope distribution, and fit the Zernike polynomial coefficients to calculate the wavefront:

[0051]

[0052] where Z k (ρ, θ) is the k-th order Zernike polynomial, C k is the coefficient of the k-th order Zernike polynomial, n is the order of the Zernike polynomial. Assuming the reference wavefront is a plane wave, i.e., Φ0(ρ, θ) = 0, the wavefront Φ(ρ, θ) containing aberrations can be obtained as Φ(ρ, θ) = △Φ(ρ, θ).

[0053] During wavefront reconstruction, analyze the specific components of the aberrations contained in the signal wavefront based on the magnitude of the Zernike polynomial coefficients of each order.

[0054] Further, in S104, optimize and select the order of the Zernike polynomial through experiments to achieve high-precision restoration of the radiation source wavefront under ionospheric disturbances, as Figure 2 shown, accurately characterizing the signal amplitude and phase changes brought about by ionospheric disturbances.

[0055] Based on the above embodiments, as Figure 3 shown, the present invention also proposes a short-wave channel modeling system based on signal wavefront restoration, including:

[0056] A calibration module, configured to project a standard plane wave from a cooperative target radiation source onto the antenna array from different angles, collect the antenna array signals under different directions and without disturbances, and store them as calibration data;

[0057] A phase difference calculation module, configured to calculate the time delay of the wavefront received by each antenna relative to the center of the antenna array, calculate the relative time delay between the time delay and the calibration data, and multiply the relative time delay by the wavelength to achieve the time-domain to space-domain conversion of the wavefront distortion, and obtain the wavefront slope distribution;

[0058] A wavefront restoration module, configured to calculate the magnitude of the Zernike polynomial coefficients of each order based on the wavefront slope distribution, fit the Zernike polynomial coefficients to calculate the wavefront, analyze the specific components of the aberrations contained in the signal wavefront based on the magnitude of the Zernike polynomial coefficients of each order, complete the wavefront fitting and reconstruction, and further complete the short-wave channel modeling.

[0059] Further, the system further includes:

[0060] An optimization module, configured to optimize and select the order of the Zernike polynomial to improve the wavefront restoration accuracy.

[0061] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A short-wave channel modeling method based on signal wavefront restoration, characterized in that Including: Step 1: Use a cooperative target radiation source to project a standard plane wave onto the antenna array from different angles, collect the antenna array signals under different directions and without disturbance, and store them as calibration data. Step 2: Calculate the time delay of the received wavefront of each antenna relative to the center of the antenna array, calculate the relative time delay between the time delay and the calibration data, and multiply the relative time delay by the wavelength to achieve the time-domain to space-domain conversion of the wavefront distortion, and obtain the wavefront slope distribution. Step 3: Calculate the magnitude of the Zernike polynomial coefficients of each order based on the wavefront slope distribution, fit the Zernike polynomial coefficients to calculate the wavefront, analyze the specific components of the aberration contained in the signal wavefront according to the magnitude of the Zernike polynomial coefficients of each order, complete the wavefront fitting and reconstruction, and then complete the short-wave channel modeling.

2. The short-wave channel modeling method based on signal wavefront restoration according to claim 1, wherein, This method further includes: Step 4: Optimize the order of the selected Zernike polynomial to improve the wavefront restoration accuracy.

3. A short-wave channel modeling method based on signal wavefront restoration according to claim 1, characterized in that, In the said Step 2, the time-domain to space-domain conversion of the wavefront distortion is achieved in the following manner to obtain the wavefront slope distribution: Among them, represents the wavefront slope of the i-th antenna, △Φ is the wavefront distortion, ρ is the radial direction of the circular antenna array, θ is the tangential direction of the circular antenna array, i is the antenna serial number, d is the distance between the i-th antenna and the center of the antenna array, △ρ and △θ are respectively the components of the overall distortion amount △t·λ in the radial and tangential directions, △t is the relative time delay between the time delay and the calibration data, and λ is the wavelength.

4. A short-wave channel modeling method based on signal wavefront restoration according to claim 3, characterized in that, In the said Step 3, the Zernike polynomial coefficients are fitted to calculate the wavefront in the following manner: Among them, Z k (ρ,θ) is the k-th order Zernike polynomial, and C k is the coefficient of the k-th order Zernike polynomial. n is the order of the Zernike polynomial. Φ0(ρ,θ) = 0 indicates that the reference wavefront is a plane wave, and Φ(ρ,θ) = △Φ(ρ,θ) indicates the wavefront containing aberration.

5. A shortwave channel modeling system based on signal wavefront restoration, characterized in that, Including: A calibration module, which is used to use a cooperative target radiation source to project a standard plane wave onto the antenna array from different angles, collect the antenna array signals under different directions and without disturbance, and store them as calibration data. A phase difference calculation module, which is used to calculate the time delay of the received wavefront of each antenna relative to the center of the antenna array, calculate the relative time delay between the time delay and the calibration data, and multiply the relative time delay by the wavelength to achieve the time-domain to space-domain conversion of the wavefront distortion, and obtain the wavefront slope distribution. A wavefront restoration module, which is used to calculate the magnitude of the Zernike polynomial coefficients of each order based on the wavefront slope distribution, fit the Zernike polynomial coefficients to calculate the wavefront, analyze the specific components of the aberration contained in the signal wavefront according to the magnitude of the Zernike polynomial coefficients of each order, complete the wavefront fitting and reconstruction, and then complete the short-wave channel modeling.

6. The short-wave channel modeling system based on signal wavefront restoration according to claim 5, characterized in that, This system further includes: An optimization module, which is used to optimize the order of the selected Zernike polynomial to improve the wavefront restoration accuracy.

7. The short-wave channel modeling system based on signal wavefront restoration according to claim 5, characterized in that In the said phase difference calculation module, the time-domain to space-domain conversion of the wavefront distortion is achieved in the following manner to obtain the wavefront slope distribution: Among them, represents the wavefront slope of the i-th antenna, △Φ is the wavefront distortion, ρ is the radial direction of the circular antenna array, θ is the tangential direction of the circular antenna array, i is the antenna serial number, d is the distance between the i-th antenna and the center of the antenna array, △ρ and △θ are respectively the components of the overall distortion amount △t·λ in the radial and tangential directions, △t is the relative time delay between the time delay and the calibration data, and λ is the wavelength.

8. A short-wave channel modeling system based on signal wavefront restoration according to claim 7, characterized in that, In the said wavefront restoration module, the Zernike polynomial coefficients are fitted to calculate the wavefront in the following manner: Among them, Z k (ρ, θ) is the k-th order Zernike polynomial, and C k is the coefficient of the k-th order Zernike polynomial. n is the order of the Zernike polynomial. Φ0(ρ, θ) = 0 indicates that the reference wavefront is a plane wave, and Φ(ρ, θ) = △Φ(ρ, θ) indicates the wavefront containing aberrations.

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