Unmanned aerial vehicle cluster frequency domain equalization method

Abstract

The invention provides an unmanned aerial vehicle cluster frequency domain equalization method. Firstly, timing is performed by using a synchronization sequence, channel frequency domain response is estimated by using an inserted cyclic prefix, and channel noise is estimated in a frequency domain, so that the accuracy of channel frequency domain response estimation is improved, inter-symbol interference is reduced, the channel transmission quality is improved, and the method is suitable for unmanned aerial vehicle cluster data links and most multipath fading channels. Signal single carrier frequency domain equalization in different scenes can be realized, and the method is used for high-speed signal crosstalk-free receiving.

Description

Technical Field

[0001] The present invention belongs to the technical field of UAV swarm communication, and particularly relates to a UAV swarm frequency domain equalization method. Background Art

[0002] UAV swarms form a horizontal network through weapon platforms to achieve resource sharing and task coordination, improving the combat effectiveness of weapon platforms and playing an important role in future joint operations. UAV networks achieve real-time sharing and interaction of information through a networking data link in a complex combat environment, and then adjust task execution and iterate autonomous control to quickly adapt to the new environment, reasonably plan paths, and efficiently complete tasks. With the increase in airborne mission payload devices, the amount of information data to be exchanged also increases. The broadband signal transmission is affected by the frequency selective fading and delay spread of the wireless channel, resulting in serious inter-symbol interference and reducing the system reliability.

[0003] Currently, the main methods used to combat channel ISI are: orthogonal frequency division multiplexing technology (OFDM), single carrier time domain equalization technology (SC-TDE), single carrier frequency domain equalization technology (SC-FDE), etc. When the ISI is very serious, SC-TDE requires too many tap coefficients of the time domain equalizer and has a high complexity; OFDM transmits information through multiple subcarriers in parallel, extending the signal time on each subcarrier and enhancing the resistance of OFDM to fast fading of the pulse channel. However, it is sensitive to frequency offset and phase noise and has a high peak-to-average ratio, increasing the system cost and not being suitable for the situation where the airborne power of UAVs is limited.

[0004] Traditional SC-FDE equalization algorithms include zero-forcing (ZF) equalization and minimum mean square error (MMSE) equalization. ZF equalization is theoretically the best in eliminating inter-symbol interference, but in a frequency selective channel, especially when the channel has deep fading poles in the frequency domain, it will amplify the influence of noise and deteriorate the performance; the purpose of MMSE equalization is to minimize the bit error rate (BER), which is equivalent to making a compromise between channel noise and residual inter-symbol interference, but there is still a certain amount of residual inter-symbol interference (RISI). The performance of channel noise estimation is an important parameter affecting the equalization effect of MMSE-based SC-FDE. Summary of the Invention

[0005] In order to overcome the problem of the separation of existing time domain noise estimation and signal processing, the present invention provides a UAV swarm frequency domain equalization method. Through the frequency domain estimation of noise and using the single carrier frequency domain equalization method, it reduces inter-symbol interference and improves the channel transmission quality. It is applicable to UAV swarm data links and most multipath fading channels, and can achieve single carrier frequency domain equalization of signals in different scenarios for crosstalk-free reception of high-speed signals.

[0006] A frequency domain equalization method for an unmanned aerial vehicle (UAV) cluster, characterized by the following steps:

[0007] Step 1: After filtering, down-converting, and digital sampling the received radio frequency signal by the UAV, a baseband signal y is obtained;

[0008] Step 2: First, perform correlation processing on the local PN sequence p and the baseband signal y to obtain the autocorrelation variable C n , then, calculate the normalized function value P n , and finally, calculate the timing function value m n , where n represents the sampling point time;

[0009] Step 3: Let the sampling point time n take values within 1 to N, and calculate different timing function values m as in Step 2 n ;

[0010] Step 4: Take the position point corresponding to the maximum value among all the timing function values m n as the starting position of the training sequence, denoted as n_stat;

[0011] Step 5: Let the signal y′ = y(n_stat:n_stat + N CP ), N CP is the number of cyclic prefix sampling points, select the UW sequence as the cyclic prefix, N CP is greater than or equal to the maximum multipath delay; perform FFT transformation on the signal y′ to obtain the transformed signal Yk;

[0012] Step 6: Perform FFT transformation on the local UW sequence to obtain Xk;

[0013] Step 7: Calculate the channel frequency domain response estimate Hk according to Hk = Yk / Xk;

[0014] Step 8: Calculate the frequency domain response Yk′ of the input signal Xk according to Yk′ = Xk·Hk;

[0015] Step 9: Calculate the signal-to-noise ratio estimate value sigma at time n n :

[0016] Step 10: Perform α-β filtering on the signal-to-noise ratio estimate value sigma at time n n to obtain sigma nf ;

[0017] Step 11: Perform IFFT transformation on the channel frequency domain response estimate Hk to obtain the time domain estimate value hk of the channel response;

[0018] Step 12: Perform interpolation processing on the time domain estimate value hk of the channel response to obtain the interpolated time domain estimate value hk′ of the channel response;

[0019] Step 13: Perform an FFT transformation on the time-domain estimated value hk′ of the interpolated channel response to obtain the estimated value of the interpolated channel frequency-domain response

[0020] Step 14: Calculate the MMSE equalizer equalization coefficient W k ;

[0021] Step 15: Let the signal ym = y(n_stat:n_stat+N FFT ), perform an FFT transformation on the signal ym to obtain its frequency-domain response Ym; where N FFT takes a value that is a multiple of 2 to the power of n, n ≥ 1;

[0022] Step 16: Calculate the data S after MMSE equalization k :

[0023] Step 17: Perform an IFFT transformation on the frequency-domain equalized data S k to obtain the equalized time-domain signal ym′.

[0024] Specifically, the autocorrelation variable C described in Step 2 n is calculated as follows:

[0025]

[0026] where n represents the sampling point time, N represents the total number of sampling points, y n+k+N represents the value of the (n + k + N)-th point of the baseband signal, p n+k represents the value of the (n + k)-th point of the local PN sequence, p k represents the value of the k-th point of the local PN sequence, and the superscript * represents the conjugate operation.

[0027] Specifically, the normalized function value P described in Step 2 n is calculated as follows:

[0028]

[0029] where N represents the total number of sampling points.

[0030] Specifically, the timing function value m described in Step 2 n is calculated as follows:

[0031]

[0032] Specifically, the signal-to-noise ratio estimated value sigma at time n described in Step 9 n is calculated as follows:

[0033]

[0034] Among them, l represents the sequence number of the frequency-domain sampling point, and Yk l ′ represents the value of the l-th sampling point of the frequency-domain response Yk′, and Yk l represents the l-th sampling value of the signal Yk after transformation.

[0035] Specifically, the equalization coefficient W of the MMSE equalizer described in step 14 k is calculated according to the following formula:

[0036]

[0037] Specifically, the data S after MMSE equalization described in step 16 k is calculated according to the following formula:

[0038] S k = W k ·Ym (6)

[0039] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of a method for frequency-domain equalization of an unmanned aerial vehicle (UAV) cluster disclosed in the present invention.

[0040] A program product includes a computer program. When the computer program is run, it is used to execute the steps of a method for frequency-domain equalization of an unmanned aerial vehicle (UAV) cluster disclosed in the present invention.

[0041] A storage medium stores a computer program. When the computer program is run, it is used to execute the steps of a method for frequency-domain equalization of an unmanned aerial vehicle (UAV) cluster disclosed in the present invention.

[0042] The beneficial effects of the present invention are as follows: First, timing is performed using a synchronization sequence, the channel frequency-domain response is estimated using the inserted cyclic prefix, and the channel noise is estimated in the frequency domain, which improves the accuracy of the channel frequency-domain response estimation, reduces inter-symbol interference, improves the channel transmission quality, is applicable to the data link of the UAV cluster and most multipath fading channels, and can achieve single-carrier frequency-domain equalization of signals in different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flowchart of a method for frequency-domain equalization of an unmanned aerial vehicle (UAV) cluster according to the present invention;

[0044] Figure 2 is a schematic diagram of the data frame structure according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The present invention will be further described below in conjunction with the drawings and embodiments. The present invention includes but is not limited to the following embodiments.

[0046] The present invention provides a frequency-domain equalization method for an unmanned aerial vehicle (UAV) cluster, as Figure 1 shown, and its specific implementation process is as follows:

[0047] Step 1: After filtering, down-converting, and digital sampling the received radio frequency signal by the UAV, a baseband signal y is obtained.

[0048] Step 2: Perform correlation processing on the local PN sequence p and the baseband signal y to obtain the autocorrelation variable C n :

[0049]

[0050] where n represents the sampling point time, N represents the total number of sampling points, y n+k+N represents the value of the (n + k + N)-th point of the baseband signal, p n+k represents the value of the (n + k)-th point of the local PN sequence, p k represents the value of the k-th point of the local PN sequence, and the superscript * represents the conjugate operation;

[0051] Then, calculate the normalized function value P according to the following formula n :

[0052]

[0053] Finally, calculate the timing function value m according to the following formula n :

[0054]

[0055] Step 3: Let n take values within 1 to N, and calculate different timing function values m as in Step 2 n .

[0056] Step 4: Take the position point corresponding to the maximum value among all the timing function values m n as the starting position of the training sequence, denoted as n_stat.

[0057] Step 5: Let the signal y′ = y(n_stat:n_stat + N CP ), N CP is the number of sampling points of the cyclic prefix, select the UW sequence as the cyclic prefix, N CP is greater than or equal to the maximum multipath delay; perform FFT transformation on the signal y′ to obtain the transformed signal Yk.

[0058] Step 6: Perform FFT transformation on the local UW sequence to obtain Xk.

[0059] Step 7: Calculate the channel frequency-domain response estimate Hk according to Hk = Yk / Xk.

[0060] Step 8: Obtain the frequency-domain response Yk' of the input signal Xk according to Yk' = Xk · Hk.

[0061] Step 9: Calculate the SNR estimation value sigma at time n according to the following formula n :

[0062]

[0063] where l represents the frequency-domain sampling point sequence number, Yk l ' represents the l-th sampling point value of the frequency-domain response Yk', and Yk l represents the l-th sampling value of the transformed signal Yk.

[0064] Step 10: Perform α-β filtering on the SNR estimation value sigma at time n n to obtain sigma nf .

[0065] Step 11: Perform an IFFT transform on the channel frequency-domain response estimation Hk to obtain the time-domain estimation value hk of the channel response.

[0066] Step 12: Perform interpolation processing on the time-domain estimation value hk of the channel response to obtain the time-domain estimation value hk' of the interpolated channel response.

[0067] Step 13: Perform an FFT transform on the time-domain estimation value hk' of the interpolated channel response to obtain the frequency-domain response estimation value of the interpolated channel

[0068] Step 14: Calculate the MMSE equalizer equalization coefficient W according to the following formula k :

[0069]

[0070] Step 15: Let the signal ym = y(n_stat:n_stat+N FFT ), perform an FFT transform on the signal ym to obtain its frequency-domain response Ym; where N FFT takes a value that is a multiple of 2 to the power of n, n ≥ 1.

[0071] Step 16: Calculate the data S after MMSE equalization according to the following formula k :

[0072] S k = W k ·Ym (12)

[0073] Step 17: The frequency-domain equalized data S kPerform IFFT transformation to obtain the equalized time-domain signal ym'.

[0074] Figure 2 The signal frame structure of the present invention is given. In the figure, SYN represents the PN synchronization sequence, CP represents the cyclic prefix, and data represents the transmitted data. Timing synchronization is performed using SYN, and signal estimation is performed using CP.

[0075] An electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of a method for frequency-domain equalization of an unmanned aerial vehicle cluster disclosed by the present invention.

[0076] A program product includes a computer program that, when run, is used to execute the steps of a method for frequency-domain equalization of an unmanned aerial vehicle cluster disclosed by the present invention.

[0077] A storage medium stores a computer program that, when run, is used to execute the steps of a method for frequency-domain equalization of an unmanned aerial vehicle cluster disclosed by the present invention.

Claims

1. A method for frequency domain equalization of drone clusters, characterized in that Here are the steps: Step 1: The drone filters, down-converts, and digitally samples the received RF signal to obtain a baseband signal y; Step 2: First, the local PN sequence p is correlated with the baseband signal y to obtain the autocorrelation variable C n , then, calculate the normalized function value P n Finally, calculate the timing function value m n , where n represents the sampling point time; Step 3: Let the sampling point time n take a value between 1 and N, and calculate different timing function values ​​m as in step 2 n ; Step 4: Take all the timing function values ​​m n The position point corresponding to the maximum value in is the starting position of the training sequence, denoted as n_stat; Step 5: Set signal y′=y(n_stat:n_stat+N CP ), N CP is the number of cyclic prefix sampling points, select UW sequence as cyclic prefix, N CP is greater than or equal to the maximum multipath delay; perform FFT transformation on the signal y′ to obtain the transformed signal Yk; Step 6: Perform FFT transformation on the local UW sequence to obtain Xk; Step 7: Calculate the channel frequency domain response estimate Hk according to Hk=Yk / Xk; Step 8: Calculate the frequency domain response Yk′ of the input signal Xk according to Yk′=Xk·Hk; Step 9: Calculate the estimated signal-to-noise ratio sigma at time n n : Step 10: Estimate the signal-to-noise ratio sigma at time n n Perform α-β filtering to get sigma nf ; Step 11: Perform IFFT transformation on the channel frequency domain response estimate Hk to obtain the time domain estimate hk of the channel response; Step 12: interpolate the time domain estimation value hk of the channel response to obtain the interpolated time domain estimation value hk′ of the channel response; Step 13: Perform FFT transformation on the interpolated channel response time domain estimate hk′ to obtain the interpolated channel frequency domain estimate Step 14: Calculate the MMSE equalizer equalization coefficient W k ; Step 15: Set signal ym=y(n_stat:n_stat+N FFT ), perform FFT transformation on the signal ym to obtain its frequency domain response Ym; where N FFT The value is 2 times the power of n, n ≥ 1; Step 16: Calculate the data S after MMSE equalization k : Step 17: The frequency-domain equalized data S k Perform IFFT transformation to obtain the equalized time domain signal ym′.

2. A method for frequency domain equalization of a drone cluster as claimed in claim 1, characterized in that: The autocorrelation variable C described in step 2 n Calculated as follows: Among them, n represents the sampling point time, N represents the total number of sampling points, and y n+k+N Indicates the value of the baseband signal at point n+k+N, p n+k Indicates the value of the n+kth point of the local PN sequence, p k It represents the value of the kth point of the local PN sequence, and the superscript * represents the conjugate operation.

3. A method for frequency domain equalization of a drone cluster as claimed in claim 1, characterized in that: The normalized function value P described in step 2 n Calculated as follows: Where N represents the total number of sampling points.

4. A method for frequency domain equalization of a drone cluster as claimed in claim 1, characterized in that: The timing function value m described in step 2 n Calculated as follows:

5. The method for frequency domain equalization of a drone cluster as claimed in claim 1, characterized in that: The estimated signal-to-noise ratio sigma at time n described in step 9 n Calculated as follows: Among them, l represents the frequency domain sampling point number, Yk l ′ represents the lth sampling point value of the frequency domain response Yk′, Yk l Represents the lth sampling value of the transformed signal Yk.

6. A method for frequency domain equalization of a drone cluster as claimed in claim 1, characterized in that: The MMSE equalizer equalization coefficient W described in step 14 k Calculated as follows:

7. The method for frequency domain equalization of a drone cluster as claimed in claim 1, characterized in that: The data S after MMSE equalization described in step 16 k Calculated as follows: S k =W k ·Ym (6) 8. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the computer program implements the steps of the method according to any one of claims 1 to 7 when executed by the processor.

9. A program product, characterized in that: The invention comprises a computer program, which is used to execute the steps of the method according to any one of claims 1 to 7 when the computer program is executed.

10. A storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, it is used to execute the steps of the method according to any one of claims 1 to 7.

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