WO2022126844A1 - Watterson model-based short-wave aviation mobile channel modeling method - Google Patents

Abstract

The present invention relates to the technical field of short-wave data communication, and relates in particular to a Watterson model-based short-wave aviation mobile channel modeling method, comprising acquiring the operating state of an aircraft, the operating state of the aircraft comprising the maximum moving speed, acceleration, maneuvering frequency, and motion trajectory of the aircraft; constructing a short-wave aviation mobile channel model according to the operating state of the aircraft and an improved Watterson model; and in the improved Watterson model, by re-modeling the Doppler shift of a channel, constructing the Doppler shift into a time-varying Doppler shift related to the motion state. By studying the effect of the maximum moving speed and acceleration, maneuvering frequency, motion trajectory and other factors of an aircraft on a short-wave aviation mobile channel, the present invention combines the complexity of a moving scene with channel fading, reflects all features of short-wave mobile communication, and has important reference value for further research into short-wave aviation mobile communication systems.

Description

Modeling method of shortwave aeronautical mobile channel based on Watson model

This application claims the priority of the Chinese patent application filed on December 17, 2020 with the application number 202011491353.8 and the invention titled "Modeling Method for Short-wave Aviation Mobile Channels Based on Watson Model", the entire contents of which are approved by Reference is incorporated in this application.

technical field

The invention belongs to the technical field of short-wave data communication, in particular to a short-wave aviation mobile channel modeling method based on Watson model.

Background technique

Short-wave communication (3-30MHz) utilizes the mechanism of the ionosphere to reflect high-frequency electromagnetic waves to achieve over-the-horizon communication up to thousands of kilometers. After multiple reflections, global coverage can be achieved. It is a national and military long-distance communication, mobile communication and emergency communication. basic means. When the satellite communication system fails, short-wave communication is the main means, and sometimes the only means, for remote command and control of the aircraft beyond the visual range. In aeronautical mobile communication systems, when the aircraft performs variable-speed linear motion or circular motion, the relative motion of the receiving and sending ends will cause the time-varying Doppler frequency shift and spread of the received signal. Combined with the signal distortion and multipath delay caused by the random multipath of the shortwave channel itself, the fading characteristics of the shortwave aeronautical mobile channel are also more complicated. Establishing an accurate and effective short-wave aviation mobile channel model can effectively speed up the technical iteration and reduce the experimental cost, which is conducive to the further development and application of short-wave communication technology in the aviation field.

Short-wave propagation is mainly through sky wave propagation, and communication over hundreds or even thousands of kilometers can be achieved across complex terrains by means of ionospheric reflection. Therefore, the research of shortwave channel modeling and simulation begins with the exploration of the characteristics of the ionosphere. Due to the complexity and time variability of the ionosphere itself, it is difficult to describe the shortwave channel with an accurate model. At present, two typical shortwave channel models, the Watterson model and the ITS model, are statistical models based on measured data. Among them, the Watterson model adopts a tapped delay line structure, which has relatively low implementation complexity and comprehensively describes various characteristics of the short-wave channel, so it has become the standard channel model for short-wave communication performance testing recommended by ITU.

However, due to the imperfect functionality of the existing Watterson model, when establishing the shortwave channel model, the model structure is simple, and it does not combine the complexity of the mobile scene with the channel fading, and cannot reflect all the characteristics of the shortwave mobile channel. . Therefore, there is a lack of a standard channel model for short-wave aeronautical mobile channels.

SUMMARY OF THE INVENTION

In order to solve the above problems in the prior art, the present invention proposes a short-wave aeronautical mobile channel modeling method based on the Watson model. The method includes: acquiring the operating state of the aircraft; the operating state of the aircraft includes the maximum movement of the aircraft. Speed, acceleration, maneuvering frequency, and motion trajectory; build a short-wave aeronautical mobile channel model according to the operational state of the aircraft and the improved Watterson model; the improvement of the Watterson model includes converting the fixed channel Doppler shift in the Watterson model into a motion state Correlated time-varying Doppler shift. Among them, the Watterson model is the Watson model.

Preferably, the time-varying Doppler frequency shift includes the Doppler frequency shift f _iB produced by the ionosphere and the Doppler frequency shift f _iA produced by the relative motion; the Doppler frequency shift f _iB produced by the ionosphere and the relative motion The generated Doppler frequency shift f _iA is added to obtain the Doppler frequency shift of the short-wave aeronautical mobile channel.

Further, the process of Doppler frequency shift f _iA generated by relative motion includes:

Further, the formula of the Doppler frequency shift of the shortwave channel includes: f _i =f _iA +f _iB .

Preferably, the short-wave aviation channel model further includes two band-pass filters; the two band-pass filters use a finite-length unit impulse response FIR low-pass filter, and the passband of the filter is set to the required filter. 1/2 of the passband of the FIR filter, and the coefficient conversion formula is used to convert the coefficients of the FIR low-pass filter into the I and Q coefficients of the FIP band-pass filter.

Further, the process of designing the low-pass filter includes: setting the Doppler spread to d, the noise sampling frequency to Fs, and the minimum interval Δf ≥ d between two signals of different frequencies in the signal truncation process, which is determined according to the above information. The order of the Gaussian filter is N, and the coefficient of the low-pass Gaussian filter is obtained according to the order of the Gaussian filter, and the design of the low-pass filter is completed.

Further, the formula for determining the order N of the Gaussian filter is:

Further, the coefficients of the low-pass Gaussian filter are:

Further, the coefficient conversion formula includes:

Preferably, the time-varying frequency response of the signal is:

The invention improves the channel model of Watson model by combining the influence of the relative motion Doppler effect, and realizes the formulation of short-wave aeronautical mobile action in a specific scenario; The frequency shift and expansion optimize the model, combine the complexity of the mobile scene with the channel fading, reflect all the characteristics of shortwave mobile communication, and have important reference value for further research on shortwave aeronautical mobile communication systems.

Description of drawings

Fig. 1 is the schematic diagram of Watterson model shortwave channel model;

Fig. 2 is the realization block diagram of Watterson shortwave aviation mobile channel model;

Fig. 3 is the aircraft trajectory diagram of the present invention;

Fig. 4 is the upper limit change diagram of the aircraft speed of the present invention;

Fig. 5 is the simulation block diagram of the sub-path of the shortwave aviation mobile channel of the present invention;

Fig. 6 is the time-frequency diagram of the Watterson shortwave channel model of the present invention;

Fig. 7 is the flight speed diagram of the randomly generated aviation aircraft of the present invention;

8 is a time-frequency diagram of a low-mobility short-wave aeronautical mobile channel of the present invention;

9 is a time-frequency diagram of a medium-mobility short-wave aeronautical mobile channel of the present invention;

10 is a time-frequency diagram of a high-mobility short-wave aeronautical mobile channel of the present invention;

11 is a simulation diagram of a short-wave aeronautical mobile channel under the determination of the route trajectory of the present invention;

FIG. 12 is a spectrum comparison diagram of the single-path Watterson model and the aeronautical mobile channel model under different motion states of the present invention.

Detailed ways

In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings, and the described embodiments are only part of the implementation of the present invention. examples, but not all examples. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

A short-wave aviation mobile channel modeling method based on Watson model, comprising obtaining the operating state of the aircraft; the operating state of the aircraft includes the maximum moving speed, acceleration, maneuvering frequency and motion trajectory of the aircraft; The improved Watterson model builds the short-wave aeronautical mobile channel model; the improvement of the Watterson model includes converting the fixed channel Doppler shift in the Watterson model into a time-varying Doppler shift related to the motion state. The improved model is suitable for short-wave ground-to-air mobile communication channel and air-to-air mobile communication channel.

The shortwave ionospheric reflection channel is time-varying in both time and frequency. As shown in Figure 1, the Watterson model determines various characteristics such as multipath propagation, fading, Doppler shift and spread in the shortwave channel; As can be seen in 1, the input signal generates multiple paths after passing through the tapped delay line, and each tap is equivalent to an ionospheric propagation mode or path. The tap gain function G _i (t) modulates each delayed signal to simulate the fading experienced by the signal during propagation. Finally, each modulated signal and additive noise are added to form the final output signal.

In the Watterson model, the following provisions are made for the channel: 1) the gain of each path is a complex Gaussian random process; 2) the gain function of each path is independent of each other; 3) the corresponding spectrum of each path gain is the superposition of two Gaussian spectra . According to Figure 2, the expression of the tap gain of the path is:

G _i (t)=G _ia (t)exp(j2πf _ia t)+G _ib (t)exp(j2πf _ib t)

Among them, G _i (t) represents the tap gain of path i, G _ia (t) and G _ib (t) represent mutually independent complex Gaussian random processes, whose mean is 0, whose envelope obeys Rayleigh distribution, and whose phase obeys Uniform distribution; f _ia represents the Doppler frequency shift of the ith path, f _ib represents the Doppler frequency shift of the ith path, and t represents the time.

The autocorrelation function of the tap gain function is:

C _i (Δt)=C _ia (0)exp[-2π ² σ _ia ² (Δt) ² +j2πv _ia Δt]+C _ib (0)exp[-2π ² σ _ib ² (Δt) ² +j2πv _ib Δt ]

Among them, C _i (Δt) represents the autocorrelation function of the tap gain function, Δt represents the time interval, C _ia (0) represents the value of the autocorrelation function of G _ia (Δt) at Δt=0, σ _ia represents the i-th line The Doppler spread of the path, v _ia represents, C _ib (0) represents the value of the autocorrelation function of G _ib (Δt) at Δt=0, σ _ib represents the Doppler spread of the i-th path, and v _ib represents .

According to the autocorrelation function of the tap gain function, the tap gain spectral function is obtained as:

where f _i (f) is the tap gain spectral function, and f is the frequency.

When the carrier frequency is low, the Doppler frequency shift and spread of the two magnetic ion components are almost the same, and their power spectra are almost coincident, which is often simplified to one in simulation.

As shown in Figure 2, the tap gain spectral function is mainly determined by the Doppler frequency shift and Doppler frequency spread, and also needs to consider the realization of multipath and various interference and noise existing in the actual shortwave channel.

It can be seen from Figure 2 that the input signal must first be combined by two linear bandpass filters to generate an analytical signal. The bandpass filter is used to remove the frequency components in the input signal that cannot pass the high frequency channel, and the Hilbert transform provides a 90° phase shift, which is used to generate analytical signals for signal processing.

After the above processing, the signal has become a complex signal. Let the frequency shift Δf of a complex signal be equivalent to multiplying e ^j2πΔft in the time domain. Assuming that the input signal is e ^j2πft , the frequency-shifted signal should be e ^j2π(f+Δf)t . By Euler's formula, the real part and the imaginary part can be combined Partially written in the following form:

e ^j2π(f+Δf)t =(cos2πftcos2πΔft-sin2πftsin2πΔft)+j(cos2πftsin2πΔft+sin2πftcos2πΔft)

Since the power spectrum of white Gaussian noise obeys a uniform distribution, the Gaussian white noise can be filtered by a Gaussian filter to obtain a noise sequence with a Gaussian power spectrum, which is then multiplied by the input signal. From the perspective of digital signal processing, the two signals Doing a multiplication operation in the time domain is equivalent to doing a convolution operation in the frequency domain, which can achieve spectrum expansion.

Preferably, the design of the band-pass filter includes: designing a finite-length unit impulse response FIR low-pass filter, setting the passband of the filter to 1/2 of the passband of the required filter; adopting a coefficient conversion formula to convert the FIR The coefficients of the low-pass filter are converted into the I and Q coefficients of the FIP band-pass filter to complete the design of the band-pass filter.

The coefficient conversion formula includes:

Among them, h _IBP (n) represents the I-channel coefficient of the n-order FIR low-pass filter, n represents the order of the low-pass filter, h _LP (n) represents the coefficient of the n-order FIR low-pass filter, and f ₀ represents the pass filter. With the center frequency, N represents the filter order, T represents the sampling period, and h _QBP (n) represents the Q coefficient of the n-order FIR low-pass filter.

The process of designing the low-pass filter includes: setting the Doppler expansion as d, the noise sampling frequency as Fs, and the minimum interval Δf ≥ d between two different frequency signals in the signal truncation process, and determining the Gaussian filter according to the above information. Order N, according to the order of the Gaussian filter to obtain the coefficient of the low-pass Gaussian filter, to complete the design of the low-pass filter.

The formula for determining the order N of the Gaussian filter is:

where K is the filter quality factor, Fs is the noise sampling frequency, d is the Doppler spread,

Indicates rounded down.

The coefficients of the low-pass Gaussian filter are:

where σ is the width of the Gaussian filter, n is the coefficient of the Gaussian filter, N is the order of the Gaussian filter, and

In order to ensure that the entire system maintains a constant power bandwidth after the signal passes through different extensions, an additional gain must be added before filtering the Gaussian white noise sequence:

Where Fs is the sampling frequency, K _ENB is the equivalent noise bandwidth when the Doppler extension is 1Hz, which is calculated as a constant 0.626657.

In the process of signal processing, the noise needs to be processed. The formula for calculating the noise root mean square value of the input signal is:

Among them, x _i represents the sampling of the input signal or noise, and N represents the number of sampling points.

The signal-to-noise ratio of the system can be expressed as:

Among them, G _s represents the signal gain factor, G _n represents the noise gain factor, RMS _s represents the rms value of the input signal, and RMS _n represents the rms value of the noise.

The traditional shortwave channel model often ignores the influence of the Doppler effect caused by the relative motion of the transmitter and the receiver. For shortwave aviation channels, the Doppler effect caused by the relative motion cannot be ignored; therefore, the improved Watterson model is used to establish the shortwave The aviation channel model can solve this problem.

The improvement of the Watterson model includes the Doppler frequency shift of the shortwave channel, the Doppler frequency shift of the shortwave channel includes the Doppler frequency shift f _iB produced by the ionosphere and the Doppler frequency shift f _iA produced by relative motion . That is, the shortwave channel Doppler frequency shift formula is:

f _i =f _iA +f _iB

Among them, f _i represents the Doppler frequency shift.

The Doppler frequency shift f _iB produced by the ionosphere is caused by the frequent and rapid movement of the ionosphere and the rapid change of the height of the reflector, resulting in the constant change of the length of the propagation path. In general, its value is around 0.1-1Hz, showing a relatively large value during sunrise and sunset. When the ionosphere is calm at night, there is no Doppler effect and the Doppler shift is zero. When a magnetic storm occurs, the frequency shift can be as high as 6 Hz. The calculation formula of the Doppler frequency shift f _iA caused by the relative motion of the transceiver ends is:

Among them, f _c represents the carrier frequency, c represents the speed of light, v represents the movement rate of the aircraft, and θ _i represents the angle between the incident radio wave at the receiving end and its movement direction. The angle between the incident radio wave at the receiving end and its moving direction under different paths is different. When cosθ=1, the maximum Doppler frequency shift f _{iA max} is obtained, and the value of the Doppler frequency shift is uniformly distributed. Each path of the shortwave channel itself is composed of a large number of inseparable electromagnetic wave rays. Due to the jitter of the ionosphere, a certain Doppler spread will be generated in each path. Under normal conditions, the Doppler spread value is between 0.1 and 1 Hz.

As shown in Fig. 3, the Doppler shift in each propagation mode is different due to the flight state of the aircraft. For the civil aviation channel, the maneuvering frequency of the aircraft is low, and the flight state of the aircraft remains unchanged for a long time. However, when the maneuvering frequency of the aircraft is high (such as unmanned aerial vehicles, etc.), the magnitude and direction of its movement speed change rapidly in a short time, and the trajectory of the aircraft is difficult to estimate. The trajectory can usually be divided into many small segments, each of which can be regarded as linear motion and partial circular motion.

In aeronautical mobile channels, when the aircraft performs variable-speed motion or circular motion, the rapid change of the Doppler frequency shift in each path will cause its Doppler spread to change sharply in a short period of time. When the aircraft is in motion, its speed formula is:

v(t)=v ₀ +a(t)cosα(t)*t

Among them, v ₀ represents the initial speed of the aircraft, a(t) represents the acceleration of the aircraft, α(t) represents the angle between the acceleration of the aircraft and the aircraft, and t represents the flight time.

When the acceleration of the aircraft is 0, that is, when a(t)=0, the aircraft moves at a uniform speed. α(t) is the angle between the speed and the direction of acceleration in the flat plane. When α=0, the aircraft moves in a straight line, and in other cases, the aircraft moves in a circle. The Doppler shift of the aircraft for each path is:

Since both a(t) and cosα(t) are time-varying values, they can be regarded as a ₁ (t), and f _i can be simplified to:

Since the aircraft has a maximum upward speed, it is impossible to accelerate all the time. When the aircraft flies at the acceleration a, the power of the engine is:

P=Fv=(f+ma)v=(kv ² +ma)v

Among them, F represents the traction force of the aircraft, v represents the current speed of the aircraft, f represents the air resistance encountered in flight, k represents the proportional coefficient, m represents the mass of the aircraft, and a represents the acceleration of the aircraft.

Since the acceleration and flight speed of the aircraft have a maximum value, when |v/v _max |≥0.8, the power of the aircraft engine remains constant at the maximum, and the maximum power is:

P _max =kv _max ³ =kv ³ +mav

Then the acceleration is:

Let |v/v _max |=x,

which is:

As shown in Figure 4, when |v/v _max |≥0.8, the acceleration gradually decreases, and when the aircraft reaches the maximum flight speed, its acceleration is 0.

As shown in Figure 5, a sub-path of a short-wave aeronautical mobile channel, the sub-path obtains the input signal, and after the path delay, the Doppler frequency shift of the ionosphere and the relative motion time-varying Doppler frequency shift are used to pair the time delay. The processed signal is processed, and the processed signal is subjected to Doppler expansion, and finally a signal is output.

In the channel simulation, there are three signal input paths, the input signal is a single tone signal with a carrier frequency of 200Hz, the delays of the three paths are no delay, 1ms, 2ms, and the Doppler spreads are 0.5Hz, 1Hz, 1.5Hz, the Doppler frequency shift is 1Hz, 2Hz, 5Hz respectively, and the signal-to-noise ratio is 10db; the simulation results of the Watterson channel model are shown in Figure 6, and the input signal can be seen from the time-domain waveform diagram After shortwave channel transmission, obvious fading occurs; while in the frequency domain waveform, the output signal undergoes frequency shift and spectrum spreading.

In the simulation of short-wave aeronautical mobile channel, the speed of the aircraft cannot exceed its maximum upward speed, and the Doppler frequency shift is often different under different motion modes. When the flight trajectory of an aircraft is uncertain, the Doppler frequency shift of the signal is also related to its maneuvering frequency. According to the level of maneuvering frequency, the reference value of maneuvering frequency under three typical scenarios is determined, as shown in Table 1.

Table 1 Parameter reference values in three typical scenarios

For aircraft with low maneuvering frequency, such as civil aviation aircraft, the maneuvering frequency is assumed to be 0.01Hz in the simulation here. For the aviation aircraft with moderate maneuvering frequency, such as small civil private planes, it is assumed that the maneuvering frequency is 0.1Hz. For aircraft with high maneuvering frequency, such as unmanned vehicles, the maneuvering frequency is assumed to be 1 Hz here. The speed of the aircraft is simulated for these three typical scenarios, assuming that the maximum acceleration of the aircraft is 80m/s2, the maximum flight speed is 600m/s, and the simulation results of randomly generated speed changes of 100s are shown in Figure 7. It can be seen from the figure that the aircraft can perform various types of motion, and as the maneuvering frequency increases, the motion state of the aircraft becomes more and more complex.

When simulating the short-wave aeronautical mobile channel, the Doppler frequency shift is actually 3-30 MHz, but the process of digital signal processing is often carried out in the baseband, and down-conversion processing is required. The simulation here assumes that the input signal is a single-tone signal downconverted from 15MHz to 200Hz, θ _i is uniformly distributed in [0, π/2], the speed of the aircraft is randomly generated as above, and the rest of the parameters are consistent with the simulation parameters of the Watterson model. The simulation results are shown in Figures 8 to 10. Compared with the simulation results of the Watterson model in Figure 6, the overall frequency shift and expansion of the signal after passing through the short-wave aeronautical mobile channel are significantly increased, and with the increase of the maneuvering rate, the signal's overall frequency shift and expansion are significantly increased. The decline also increased.

When the flight route trajectory of the aircraft is determined, referring to FIG. 3 , the speed of the aircraft at each moment is a determined value, and the simulation motion parameters are shown in Table 2. Except for the motion parameters of the aircraft, other simulation parameters are consistent with the simulation parameters of the Watterson model, and the simulation results of the output signal are shown in Figure 11. In order to facilitate the observation of the Doppler frequency shift and spread in each fixed motion state, a path of the Watterson model is taken to compare the spectrum within 5s of a path of the short-wave aeronautical mobile channel, as shown in Figure 12. It can be seen from the figure that in the case of no noise interference, the frequency shift and expansion of different motion states are very different. Compared with uniform linear motion, the uniform acceleration linear motion has a larger spectrum expansion, and the circular motion is also accompanied by the motion direction. The change of the frequency has positive and negative frequency offsets, which is consistent with the theory.

Table 2 Motion parameters when the flight path trajectory of the aircraft is determined

time(s)	sports	Initial speed (m/s)	Acceleration (m/s2)
0～5	uniform acceleration line	0	50
5 to 10	Uniform speed circle (R=1000m)	250	Size V2/R direction time-varying
10～15	Uniform straight line	250	0

The invention combines various motion states of the aircraft and the improved Watterson model to establish a short-wave aeronautical mobile channel model. The model can not only realize the simulation and simulation of the typical characteristics of the short-wave channel, but also can effectively describe the multiple effects caused by the relative motion of the transceiver ends. The present invention also performs differential channel simulation for different types of aircraft with different parameters. Finally, when the flight path of the aircraft is determined, the model can use it as a priori information to realize the customized simulation of the short-wave aeronautical mobile channel in a specific scenario, which is an important reference for further research on the short-wave aeronautical mobile communication system. value.

The above-mentioned embodiments further describe the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above-mentioned embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made to the present invention within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims (10)

1. A short-wave aviation mobile channel modeling method based on Watson model is characterized in that, comprising: obtaining the operating state of the aircraft; the operating state of the aircraft includes the maximum moving speed, acceleration, maneuvering frequency and motion trajectory of the aircraft; The operating state of the aircraft and the improved Watterson model are used to construct the short-wave aeronautical mobile channel model; the improvement of the Watterson model includes transforming the fixed channel Doppler shift in the Watterson model into a time-varying Doppler shift related to the motion state.
2. A shortwave aeronautical mobile channel modeling method based on Watson model according to claim 1, wherein the time-varying Doppler frequency shift comprises the Doppler frequency shift f _iB generated by the ionosphere and the relative motion generated The Doppler frequency shift f _iA is obtained; the Doppler frequency shift f _iB produced by the ionosphere and the Doppler frequency shift f _iA produced by the relative motion are added to obtain the Doppler frequency shift of the short-wave aeronautical mobile channel.
3. A kind of shortwave aeronautical mobile channel modeling method based on Watson model according to claim 2, is characterized in that, the process of Doppler frequency shift f _iA that relative motion produces comprises:

   

   Among them, f _c represents the carrier frequency, c represents the speed of light, v represents the movement rate of the aircraft, and θ _i represents the angle between the incident radio wave at the receiving end and its movement direction.
4. A kind of shortwave aeronautical mobile channel modeling method based on Watson model according to claim 2, is characterized in that, the formula of the Doppler frequency shift of shortwave channel comprises:

   f _i =f _iA +f _iB

   Among them, f _i represents the Doppler frequency shift.
5. The short-wave aviation mobile channel modeling method based on the Watson model according to claim 1, wherein the short-wave aviation channel model further includes two bandpass filters; the two bandpass filters adopt Finite-length unit impulse response FIR low-pass filter, set the passband of the filter to 1/2 of the desired filter passband, and use the coefficient conversion formula to convert the coefficients of the FIR low-pass filter into the FIP bandpass The I and Q coefficients of the filter.
6. The shortwave aviation mobile channel modeling method based on the Watson model according to claim 5, wherein the process of designing the low-pass filter comprises: setting the Doppler expansion to d, the noise sampling frequency is Fs, the minimum interval Δf≥d between two different frequency signals in the signal truncation process, determine the order N of the Gaussian filter according to the above information, and obtain the coefficient of the low-pass Gaussian filter according to the order of the Gaussian filter, complete Low-pass filter design.
7. A kind of shortwave aeronautical mobile channel modeling method based on Watson model according to claim 6, is characterized in that, the described formula for determining the order N of Gaussian filter is:

   

   where K is the filter quality factor, Fs is the noise sampling frequency, d is the Doppler spread,

   

   Indicates rounded down.
8. A kind of shortwave aviation mobile channel modeling method based on Watson model according to claim 6, is characterized in that, the coefficient of low pass Gaussian filter is:

   

   Among them, σ represents the width of the Gaussian filter, n represents the coefficient of the Gaussian filter, and N represents the order of the Gaussian filter.
9. The shortwave aviation mobile channel modeling method based on the Watson model according to claim 5, wherein the coefficient conversion formula comprises:

   

   

   Among them, h _IBP (n) represents the I channel coefficient of the n-order FIR low-pass filter, n represents the order of the low-pass filter, h _LP (n) represents the coefficient of the n-order FIR low-pass filter, and f ₀ represents the pass filter. With the center frequency, N represents the filter order, T represents the sampling period, and h _QBP (n) represents the Q coefficient of the n-order FIR low-pass filter.
10. A kind of shortwave aviation mobile channel modeling method based on Watson model according to claim 1, is characterized in that, the time-varying frequency response of described signal is:

    

    where f is the Doppler frequency shift of the shortwave channel, t is the time, i is the path label, n is the total number of paths, τ _i is the delay of the ith path, and G _i (t) is the tap of the ith path gain function.

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