Fractional time delay self-interference signal high-precision estimation and correction method

Abstract

The invention relates to a fractional time delay self-interference signal high-precision estimation and correction method, and the method employs the Kalman filtering fractional time delay estimation of frequency domain data and a fractional time delay filter of a Farrow structure to carry out fractional time delay compensation, and achieves the fractional time delay alignment of a local reference signal and a received signal. The time domain coherence of a local reference signal and a received signal is improved, the self-interference elimination suppression ratio is improved, and the requirements of low-orbit navigation GNSS signal receiving and low-orbit navigation enhanced signal broadcasting are met.

Description

Technical Field

[0001] The present application relates to the field of ground orbit navigation enhancement, and in particular, to a high-precision estimation and correction method for fractionally delayed self-interference signals. Background Art

[0002] With the booming development of commercial aerospace, navigation enhancement technology based on low-orbit satellite constellations is becoming a hot topic. Low-orbit satellite navigation constellations represented by Xona Space and Satelles in the United States are committed to promoting global LEO PNT (Positioning, Navigation and Timing) services, which are characterized by high precision, strong signals, fast convergence and high security. In addition to being used independently, another important use of LEO PNT is to enhance the navigation of the GNSS system. Low-orbit satellites achieve signal enhancement and information enhancement by downlinking navigation enhancement signals and broadcasting precise ephemeris and precise clock difference navigation enhancement information of GNSS satellites. In order to enable the low-orbit navigation signals broadcast by the ultra-large-scale LEO constellation to complete the LEO PNT service, it is necessary to solve the two major challenges of maintaining the time reference and maintaining the space reference. Generally, low-orbit navigation enhancement satellites will carry GNSS receivers to achieve clock difference determination and precise orbit determination, which are used to unify the time and space reference of low-orbit satellites with the time and space reference of GNSS satellites. When the frequency band of the downlink navigation signal overlaps with the frequency band of the GNSS signal, the downlink transmission signal will cause self-interference on the GNSS receiving signal in the same frequency band, causing the GNSS receiver to fail to work normally, thereby affecting the establishment and maintenance of the low-orbit satellite time and space reference.

[0003] Self-interference elimination can generally be divided into spatial domain, RF domain and digital domain interference suppression. In reality, it is impossible to achieve a high self-interference suppression ratio only through spatial domain and RF domain interference elimination. This is mainly due to the accuracy limitations of RF devices, which leads to the failure of these self-interference elimination performance requirements. Digital domain interference elimination uses a high-precision adaptive algorithm in the digital domain to eliminate self-interference. Its channel estimation accuracy is high and can achieve the purpose of completely eliminating self-interference signals as much as possible. However, there is a problem that the delay of the self-interference transmission reference signal and the received signal cannot be aligned. Summary of the invention

[0004] In order to overcome at least one deficiency in the prior art, the present application provides a method for high-precision estimation and correction of a fractionally delayed self-interference signal.

[0005] In a first aspect, a method for high-precision estimation and correction of a fractionally delayed self-interference signal is provided, comprising:

[0006] Cross-correlating the baseband self-interference received signal and the local reference branch baseband signal to obtain an integer multiple time delay of the baseband self-interference received signal;

[0007] Adjusting the shift register generated by the local reference branch baseband signal according to the integer multiple time delay to achieve alignment of the integer multiple time delay of the baseband self-interference received signal and the local reference branch baseband signal;

[0008] Calculate the frequency domain complex conjugate multiplication signal between the baseband self-interference received signal and the local reference branch baseband signal after integer delay alignment; determine the phase data of the fractional delay according to the frequency domain complex conjugate multiplication signal; and obtain the fractional delay by using Kalman filtering based on the phase data of the fractional delay;

[0009] A fractional delay filter with a Farrow structure is used to adjust the local reference branch baseband signal based on the fractional delay to obtain a local reference branch baseband signal after the fractional delay adjustment.

[0010] A recursive least squares adaptive filtering method is used for the baseband self-interference received signal to obtain a posteriori estimate of the self-interference channel; the local reference branch baseband signal after fractional delay adjustment is convolved with the posteriori estimate of the self-interference channel to obtain a reconstructed signal; the reconstructed signal is subtracted from the baseband self-interference received signal to obtain a signal after self-interference elimination.

[0011] In one embodiment, cross-correlating a baseband self-interference received signal with a local reference branch baseband signal to obtain an integer multiple time delay of the baseband self-interference received signal includes:

[0012] The baseband self-interference received signal and the local reference branch baseband signal are cross-correlated, and the objective function is constructed:

[0013]

[0014] Where, T is the integer multiple delay estimate, ζ is the delay, E is the expectation, y1(n) is the baseband self-interference received signal, y2(n) is the local reference branch baseband signal, n is the sampling point; * is the complex conjugate;

[0015] Solve the objective function to obtain the integer multiple delay estimate τ;

[0016] Correct the integer multiple delay estimate τ to obtain the final integer multiple delay:

[0017] τ int =τ-1

[0018] Among them, τ int An integer multiple of the delay.

[0019] In one embodiment, determining fractional-time-delayed phase data according to the frequency-domain complex conjugate multiplication signal includes:

[0020] Calculate the total phase data including integer delay and fractional delay:

[0021]

[0022] Among them, Φ Y is the total phase data, atan2 is the four-quadrant inverse tangent function, Y is the complex conjugate multiplication signal in the frequency domain, Im(Y) is the imaginary part of Y, and Re(Y) is the real part of Y;

[0023] Remove the phase data of integer multiple delay from the total phase data to obtain the phase data of fractional multiple delay:

[0024]

[0025] Φ int =-2πτ int *(1:N2) / Np

[0026] Among them, Φ frac is the phase data of the fractional delay, mod is the modulo operation, Indicates Y from 2 to The data corresponding to the sampling points, Np is the number of sampling points, Φ int is the phase data of integer multiple delay, is Φ int 1 to The data corresponding to the sampling point, τ int is an integer multiple of the delay,

[0027] In one embodiment, a fractional delay filter of a Farrow structure is used to adjust a local reference branch baseband signal based on a fractional delay to obtain a local reference branch baseband signal after the fractional delay adjustment, including:

[0028] Determine the fractional delay adjustment Δ=τ of the fractional delay filter using the Farrow structure according to the fractional delay frac , τ frac It is a fractional delay;

[0029] The local reference branch baseband signal y2(n) is adjusted according to the fractional delay adjustment amount Δ to obtain the local reference branch baseband signal y2(n-Δ) after fractional delay adjustment, where n is the sampling point.

[0030] In a second aspect, a high-precision estimation and correction device for a fractional-time-delayed self-interference signal is provided, comprising:

[0031] An integer multiple delay acquisition module is used to perform cross-correlation on the baseband self-interference received signal and the local reference branch baseband signal to obtain the integer multiple delay of the baseband self-interference received signal;

[0032] An integer delay correction module, used for adjusting the shift register generated by the local reference branch baseband signal according to the integer delay, so as to achieve alignment of the integer delay of the baseband self-interference received signal and the local reference branch baseband signal;

[0033] The fractional delay determination module is used to calculate the frequency domain complex conjugate multiplication signal between the baseband self-interference received signal and the local reference branch baseband signal after the integer delay is aligned; determine the phase data of the fractional delay according to the frequency domain complex conjugate multiplication signal; and obtain the fractional delay by using Kalman filtering based on the phase data of the fractional delay;

[0034] A fractional delay adjustment module, which is used to use a fractional delay filter of a Farrow structure to adjust a local reference branch baseband signal based on the fractional delay to obtain a local reference branch baseband signal after the fractional delay adjustment;

[0035] The self-interference elimination module is used to adopt a recursive least squares adaptive filtering method for the baseband self-interference receiving signal to obtain a posteriori estimation value of the self-interference channel; convolve the local reference branch baseband signal after fractional delay adjustment with the posteriori estimation value of the self-interference channel to obtain a reconstructed signal; and subtract the reconstructed signal from the baseband self-interference receiving signal to obtain a signal after self-interference elimination.

[0036] Compared with the prior art, the present application has the following beneficial effects: the present application proposes a high-precision estimation and correction method for fractional-delay self-interference signals, adopts the Kalman filtering fractional-delay estimation of frequency domain data, and uses the fractional-delay filter of the Farrow structure to perform fractional-delay compensation, thereby realizing the fractional-delay alignment of the local reference signal and the received signal, improving the time domain coherence of the local reference signal and the received signal, and improving the self-interference elimination suppression ratio, so as to meet the requirements of low-orbit navigation GNSS signal reception and low-orbit navigation enhanced signal broadcasting. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present application may be better understood by referring to the following description given in conjunction with the accompanying drawings, which together with the following detailed description are included in this specification and form a part of this specification. In the drawings:

[0038] Figure 1 The schematic diagram of the high-precision estimation and correction method of the fractional-time-delay self-interference signal is shown;

[0039] Figure 2 The structure diagram of the fractional delay filter of the Farrow structure is shown;

[0040] Figure 3 A schematic diagram showing the delay estimation deviation obtained by using the traditional cross-correlation delay estimation and the method of the present application is shown;

[0041] Figure 4 A performance comparison diagram of the method of the present application and the cross-correlation delay estimation method is shown;

[0042] Figure 5 A comparison diagram of the root mean square error estimated by the method of the present application and the Cramer-Rao CRLB lower bound is shown;

[0043] Figure 6 The total delay result of the reference branch signal by the Farrow structure fractional delay filter is shown;

[0044] Figure 7 The time domain convergence results of fractional delay estimation and correction self-interference elimination are shown;

[0045] Figure 8 The self-interference cancellation results without fractional delay alignment are shown;

[0046] Fig. 9 The self-interference cancellation results after fractional delay alignment are shown. DETAILED DESCRIPTION

[0047] The exemplary embodiments of the present application will be described below in conjunction with the accompanying drawings. For the sake of clarity and conciseness, not all features of the actual embodiments are described in the specification. However, it should be understood that many implementation-specific decisions can be made in the process of developing any such actual embodiments in order to achieve the specific goals of the developer, and these decisions may vary from embodiment to embodiment.

[0048] It is also necessary to explain here that, in order to avoid obscuring the present application due to unnecessary details, only the device structure closely related to the scheme according to the present application is shown in the drawings, while other details that are not closely related to the present application are omitted.

[0049] It should be understood that the present application is not limited to the described implementation forms due to the following description with reference to the accompanying drawings. In this article, where feasible, the embodiments can be combined with each other, features between different embodiments can be replaced or borrowed, and one or more features can be omitted in one embodiment.

[0050] The present invention provides a method for high-precision estimation and correction of a fractionally delayed self-interference signal. Figure 1 The schematic diagram of the high-precision estimation and correction method of the fractional-time-delay self-interference signal is shown in FIG. Figure 1 , methods include:

[0051] Step S1, cross-correlate the baseband self-interference received signal and the local reference branch baseband signal to obtain an integer multiple time delay of the baseband self-interference received signal.

[0052] The signal transmitted by the ground rail downlink signal transmitting unit is the local reference branch baseband signal y2(n). The signal transmitted by the ground rail downlink signal transmitting unit passes through the DAC (digital-to-analog converter) and the transmitting RF front end, and is transmitted through the transmitting antenna TX. After passing through the self-interference channel, it is received by the receiving antenna RX, and after passing through the receiving RF front end and ADC (analog-to-digital converter), a baseband self-interference receiving signal y1(n) is formed.

[0053] Specifically, the baseband self-interference received signal and the local reference branch baseband signal are cross-correlated, and the objective function is constructed:

[0054]

[0055] Wherein, τ is the integer multiple delay estimation value, ζ is the delay, E is the expectation, y1(n) is the baseband self-interference received signal, y2(n) is the local reference branch baseband signal, n is the sampling point; * is the complex conjugate;

[0056] Solve the objective function to obtain the integer multiple delay estimate τ;

[0057] Since the integer multiple delay estimate τ generally starts from 1, while the actual delay starts from 0, the integer multiple delay estimate τ is corrected to obtain the final integer multiple delay:

[0058] τ int =τ-1

[0059] Among them, τ int An integer multiple of the delay.

[0060] Step S2, adjusting the shift register generated by the local reference branch baseband signal according to the integer multiple time delay, so as to achieve alignment of the integer multiple time delay of the baseband self-interference received signal and the local reference branch baseband signal.

[0061] After obtaining the final integer multiple delay, it is necessary to align the baseband self-interference received signal and the integer multiple delay of the local reference branch baseband signal to ensure that the delays of the two signals are as consistent as possible, so that the two signals maintain good coherence. This can be achieved through shift alignment or a combination of multiple D flip-flops.

[0062] Step S3, calculate the frequency domain complex conjugate multiplication signal between the baseband self-interference received signal and the local reference branch baseband signal after integer delay alignment; determine the phase data of the fractional delay according to the frequency domain complex conjugate multiplication signal; and use Kalman filtering to estimate the fractional delay based on the phase data of the fractional delay.

[0063] Specifically, the frequency domain complex conjugate multiplied signal:

[0064] Y=FFT{y1(n)}conj(FFT{y2(n)})

[0065] Wherein, Y is the complex conjugate multiplication signal in the frequency domain, conj represents complex conjugate, and FFT represents fast Fourier transform.

[0066] Based on the symmetry of the spectrum of the real baseband signal about zero frequency, when extracting the frequency domain phase data, we can choose to use only the data of the positive frequency part of the first half to reduce the computational complexity. Without loss of generality, assume that the number of sampling points of the baseband self-interference signal and the local reference branch baseband signal is Np, and Np is an even number of points. The fractional delay data used to estimate y1(n) relative to y2(n) is the non-zero frequency positive frequency data Y(2:Np / 2), with a total of N2=Np / 2-1 points.

[0067] Specifically, determining the phase data of the fractional delay according to the complex conjugate multiplication signal in the frequency domain includes:

[0068] Calculate the total phase data including integer delay and fractional delay:

[0069]

[0070] Among them, Φ Y is the total phase data, atan2 is the four-quadrant inverse tangent function, Y is the complex conjugate multiplication signal in the frequency domain, Im(Y) is the imaginary part of Y, and Re(Y) is the real part of Y;

[0071] Remove the phase data of integer multiple delay from the total phase data to obtain the phase data of fractional multiple delay:

[0072]

[0073] Φ int =-2πτ int *(1:N2) / Np

[0074] Among them, Φ frac is the phase data of the fractional delay, mod is the modulo operation, Indicates Y from 2 to The data corresponding to the sampling points, Np is the number of sampling points, Φ int is the phase data of integer multiple delay, is Φ int 1 to The data corresponding to the sampling point, τ int is an integer multiple of the delay,

[0075] Specifically, the fractional time delay is estimated by using Kalman filtering based on the phase data of the fractional time delay, including:

[0076] Construct the Kalman filter model:

[0077] x(n)=Ax(n)+W(n)

[0078] Φ frac (n) = Hx(n) + v(n)

[0079] Among them, x(n) is the state variable at time n, φ(n) and are the phase and phase change rate respectively, A and H are the state transfer matrix and measurement matrix respectively, where A and H can be expressed as:

[0080]

[0081] H = [1 0]

[0082] Where ΔT is the sampling interval, W(n) is the random process noise of the state variable x(n), and v(n) is the fractional time-delay phase data observation noise.

[0083] The second-order constant gain tracking method is used to perform a posteriori estimation of the state variables, which is divided into two parts: time update and state update.

[0084] The time update part is:

[0085] x - (n) = Ax(n-1)

[0086] Among them, x - (n) is a priori estimate.

[0087] The status update part is:

[0088]

[0089] in, is the posterior estimation, K is the Kalman gain matrix, and is a fixed constant matrix.

[0090] K = [αβ / ΔT] T

[0091] α=1-ρ 3

[0092] β=1.5(1+ρ)(1-ρ) 2

[0093] Among them, α and β are filter parameters, parameter ρ = 0.999, and the initial value of the state variable can be selected as the zero value vector x(0) = [0 0] T .

[0094] The fractional delay estimate can be determined by the following formula:

[0095]

[0096] Step S4, using a fractional delay filter with a Farrow structure, adjusts the local reference branch baseband signal based on the fractional delay to obtain a local reference branch baseband signal after the fractional delay adjustment, thereby realizing closed-loop time alignment of the local reference branch baseband signal and the baseband self-interference received signal.

[0097] Specifically, the fractional delay adjustment amount Δ=τ of the fractional delay filter using the Farrow structure is determined according to the fractional delay: frac , τ frac It is a fractional delay;

[0098] The local reference branch baseband signal y2(n) is adjusted according to the fractional delay adjustment amount Δ to obtain the local reference branch baseband signal y2(n-Δ) after fractional delay adjustment, where n is the sampling point. Figure 2 The structure diagram of the fractional delay filter of the Farrow structure is shown.

[0099] Step S5, using a recursive least squares adaptive filtering method for the baseband self-interference received signal to obtain a posterior estimation value of the self-interference channel; convolving the local reference branch baseband signal after fractional delay adjustment with the posterior estimation value of the self-interference channel to obtain a reconstructed signal; subtracting the reconstructed signal from the baseband self-interference received signal to obtain a signal after self-interference elimination.

[0100] Specifically, the local reference branch baseband signal after fractional delay adjustment is convolved with the posterior estimation value of the self-interference channel to obtain a reconstructed signal, which is expressed by the following formula:

[0101]

[0102] in, is the reconstructed signal, y2(n-Δ) is the local reference branch baseband signal after fractional delay adjustment, represents convolution, w(n) is the posterior estimate of the self-interference channel, and w * (n) is the complex conjugate of w(n).

[0103] Signal after self-interference cancellation

[0104]

[0105] In order to further verify the effectiveness of the method of this application, the following specific experiments are given.

[0106] Experiment 1: Estimation of fractional delay of received signal and delay compensation of Farrow filter.

[0107] Assume that the baseband self-interference received signal is the BPSK (10) signal of BeiDou B2a, the spreading code rate is 10.23Mcps, and the sampling frequency is 160MHz. The local reference branch baseband signal is the BPSK (10) signal coherent with the baseband self-interference received signal.

[0108] The fractional delay of the received signal branch relative to the reference branch is set to change from 0 sample point, with an interval of 0.05 sample points and an end point of 0.95 sample points, and the delay estimation errors of the traditional cross-correlation delay estimation method and the method of the present application are observed. Figure 3 A schematic diagram showing the delay estimation deviation obtained by using the traditional cross-correlation delay estimation and the method of the present application is shown.

[0109] from Figure 3 It can be seen that the received signal branch has an integer multiple delay of two sample points relative to the reference branch. The cross-correlation delay estimation can accurately estimate the delay of integer multiple sampling points, but there is an obvious deviation in the estimation of fractional delays. The estimated deviation is zero at the integer multiple sample points, and the deviation is largest at the position between the two sample points, with the maximum error amplitude of 0.5 sample points. The delay estimation error is centered at 0.5 sample points and decreases monotonically to the left and right. This shows that the traditional cross-correlation delay estimation has obvious delay estimation deviations for the time estimation of received signals with fractional delays. Using the method of the present application, the delay estimation deviations are all zero during the delay estimation process of 0 to 0.95 times the fractional sample points. This shows that the method of the present application can accurately estimate the fractional delay of the received signal relative to the local reference signal.

[0110] Furthermore, the characteristics of the estimated root mean square error of the method of the present application are analyzed as the signal-to-noise ratio changes. Assuming that the integer delay is 2 sampling points, it has been estimated that the fractional delay is 0.15 sampling points. When the signal-to-noise ratio changes from 20 to 60 dB, Figure 4 The performance comparison diagram of the method of the present application and the cross-correlation delay estimation method is shown. With the method of the present application, the accuracy of fractional delay estimation increases with the increase of signal-to-noise ratio. This shows that the stronger the self-interference signal of the received signal, the higher the accuracy of the self-interference delay estimation.

[0111] In order to analyze the optimality of the proposed method, for a BPSK(10) signal, B = 20.46 MHz, Figure 5 The figure shows the comparison of the root mean square error estimated by the method of the present application and the Cramer-Rao CRLB lower bound. Figure 5 It can be seen that with the increase of the signal-to-noise ratio, the root mean square error of the method of the present application gradually approaches the CRLB lower bound, and the fractional delay estimation of the method of the present application has asymptotic optimality.

[0112] In the simulation, the received signal is delayed by 3.84 samples relative to the local reference signal. In order to align the local reference signal with the received signal in terms of delay, a fractional delay filter with a Farrow structure is used to compensate for the delay of the local reference signal. After the fractional delay correction, the local reference signal is a delayed BPSK (10) signal with slight high-frequency component distortion at the transition point. Figure 6 The total delay result of the Farrow structure fractional delay filter on the reference branch signal is shown. Figure 6 It can be seen that by delaying the reference branch signal using a fractional delay filter with a Farrow structure, the delay of the local reference signal can be aligned with the delay of the received signal by a fractional delay, thereby ensuring the coherence of the reference branch signal and the received signal in the time domain, thereby creating favorable conditions for subsequent interference cancellation processing.

[0113] After estimating the integer delay and fractional delay of the received signal relative to the reference signal, integer delay correction and Farrow filter fractional delay correction are performed. Figure 7 The time domain convergence results of fractional delay estimation and self-interference elimination are shown. Figure 7 It can be seen that the signal amplitude of the signal after self-interference elimination is greatly reduced compared with the original received signal. The signal after self-interference elimination converges quickly, and the amplitude of the self-interference signal after convergence is stable at the zero-mean noise level, which shows that the adaptive interference cancellation after fractional delay self-interference estimation and correction of the method of the present application has a near-optimal cancellation performance.

[0114] Experiment 2: The impact of fractional delay estimation and correction on the ability to cancel self-interference.

[0115] Assuming that the transmitted signal propagates through the satellite multipath channel, the maximum signal-to-noise ratio of the self-interference signal is 70dB, and the received signal delay is 3.84 sampling points compared to the reference signal delay. Figure 8 The self-interference cancellation results without fractional delay alignment are shown. Figure 8 It can be seen that without fractional delay estimation and alignment for self-interference elimination, there is an obvious residual signal in the main lobe of the BPSK (10) signal spectrum that has not been eliminated, and the self-interference elimination ratio is 58.8 dB.

[0116] Using fractional delay estimation and Farrow filter fractional delay correction technology, Fig. 9 The self-interference cancellation result after fractional delay alignment is shown. Fig. 9 It can be seen that after using fractional delay alignment, the self-interference signal after adaptive interference cancellation is close to the noise level, and the self-interference suppression ratio reaches 69.6dB. Compared with the self-interference cancellation without fractional delay alignment, the self-interference cancellation capability is improved by 10.8dB.

[0117] Based on the same inventive concept as the method for high-precision estimation and correction of fractional-time-delay self-interference signals, this embodiment also provides a corresponding high-precision estimation and correction device for fractional-time-delay self-interference signals, including:

[0118] An integer multiple delay acquisition module is used to perform cross-correlation on the baseband self-interference received signal and the local reference branch baseband signal to obtain the integer multiple delay of the baseband self-interference received signal;

[0119] An integer delay correction module, used for adjusting the shift register generated by the local reference branch baseband signal according to the integer delay, so as to achieve alignment of the integer delay of the baseband self-interference received signal and the local reference branch baseband signal;

[0120] The fractional delay determination module is used to calculate the frequency domain complex conjugate multiplication signal between the baseband self-interference received signal and the local reference branch baseband signal after the integer delay is aligned; determine the phase data of the fractional delay according to the frequency domain complex conjugate multiplication signal; and obtain the fractional delay by using Kalman filtering based on the phase data of the fractional delay;

[0121] A fractional delay adjustment module, which is used to use a fractional delay filter of a Farrow structure to adjust a local reference branch baseband signal based on the fractional delay to obtain a local reference branch baseband signal after the fractional delay adjustment;

[0122] The self-interference elimination module is used to adopt a recursive least squares adaptive filtering method for the baseband self-interference receiving signal to obtain a posteriori estimation value of the self-interference channel; convolve the local reference branch baseband signal after fractional delay adjustment with the posteriori estimation value of the self-interference channel to obtain a reconstructed signal; and subtract the reconstructed signal from the baseband self-interference receiving signal to obtain a signal after self-interference elimination.

[0123] The device for high-precision estimation and correction of fractional-time delayed self-interference signals of this embodiment has the same inventive concept as the method for high-precision estimation and correction of fractional-time delayed self-interference signals described above. Therefore, the specific implementation method of the device can be seen in the implementation example part of the method for high-precision estimation and correction of fractional-time delayed self-interference signals described above, and its technical effect corresponds to the technical effect of the above method, which will not be repeated here.

[0124] The above are only various implementations of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A high-precision estimation and correction method for fractionally delayed self-interference signals, characterized in that: include: Cross-correlating a baseband self-interference received signal with a local reference branch baseband signal to obtain an integer multiple time delay of the baseband self-interference received signal; Adjusting the shift register generated by the local reference branch baseband signal according to the integer multiple time delay to achieve alignment of the integer multiple time delay of the baseband self-interference received signal and the local reference branch baseband signal; Calculating a frequency domain complex conjugate multiplication signal between the baseband self-interference received signal and the local reference branch baseband signal after integer multiple time delay alignment; determining phase data of the fractional multiple time delay according to the frequency domain complex conjugate multiplication signal; and obtaining the fractional multiple time delay by using Kalman filtering estimation based on the phase data of the fractional multiple time delay; A fractional delay filter of a Farrow structure is adopted to adjust the local reference branch baseband signal based on the fractional delay to obtain a local reference branch baseband signal after the fractional delay adjustment; A recursive least squares adaptive filtering method is used for the baseband self-interference received signal to obtain a posterior estimation value of the self-interference channel; the local reference branch baseband signal after the fractional delay adjustment is convolved with the posterior estimation value of the self-interference channel to obtain a reconstructed signal; and the reconstructed signal is subtracted from the baseband self-interference received signal to obtain a signal after self-interference elimination.

2. The method according to claim 1, characterized in that in, Cross-correlating a baseband self-interference received signal and a local reference branch baseband signal to obtain an integer multiple time delay of the baseband self-interference received signal, including: The baseband self-interference received signal and the local reference branch baseband signal are cross-correlated, and the objective function is constructed: Wherein, τ is the integer multiple delay estimation value, ζ is the delay, E is the expectation, y1(n) is the baseband self-interference received signal, y2(n) is the local reference branch baseband signal, n is the sampling point; * is the complex conjugate; Solving the objective function to obtain an integer multiple of the delay estimation value τ; The integer multiple delay estimate τ is corrected to obtain the final integer multiple delay: t int =τ-1 Among them, τ int An integer multiple of the delay.

3. The method according to claim 1, characterized in that in, Determining fractional-time-delayed phase data according to the frequency-domain complex conjugate multiplication signal includes: Calculate the total phase data including integer delay and fractional delay: Among them, Φ Y is the total phase data, atan2 is the four-quadrant inverse tangent function, Y is the complex conjugate multiplication signal in the frequency domain, Im(Y) is the imaginary part of Y, and Re(Y) is the real part of Y; The phase data of integer multiple time delay is removed from the total phase data to obtain the phase data of fractional multiple time delay: F int =-2pts int *(1:N2) / Np Among them, Φ frac is the phase data of the fractional delay, mod is the modulo operation, Indicates Y from 2 to The data corresponding to the sampling points, Np is the number of sampling points, Φ int is the phase data of integer multiple delay, is Φ int 1 to The data corresponding to the sampling point, τ int is an integer multiple of the delay, 4. The method according to claim 1, characterized in that in, A fractional delay filter of a Farrow structure is adopted, and the local reference branch baseband signal is adjusted based on the fractional delay to obtain a local reference branch baseband signal after the fractional delay adjustment, including: Determine the fractional delay adjustment amount Δ=τ of the fractional delay filter using the Farrow structure according to the fractional delay frac , τ frac It is a fractional delay; The local reference branch baseband signal y2(n) is adjusted according to the fractional delay adjustment amount Δ to obtain a local reference branch baseband signal y2(n-Δ) after fractional delay adjustment, where n is a sampling point.

5. A high-precision estimation and correction device for fractional-time delayed self-interference signals, characterized in that: include: An integer multiple delay acquisition module, used to perform cross-correlation on a baseband self-interference received signal and a local reference branch baseband signal to obtain an integer multiple delay of the baseband self-interference received signal; An integer multiple delay correction module, used for adjusting the shift register generated by the local reference branch baseband signal according to the integer multiple delay, so as to achieve alignment of the integer multiple delay of the baseband self-interference received signal and the local reference branch baseband signal; A fractional delay determination module is used to calculate the frequency domain complex conjugate multiplication signal between the baseband self-interference received signal and the local reference branch baseband signal after integer delay alignment; determine the phase data of the fractional delay according to the frequency domain complex conjugate multiplication signal; and obtain the fractional delay by using Kalman filtering based on the phase data of the fractional delay; A fractional delay adjustment module, configured to use a fractional delay filter of a Farrow structure to adjust the local reference branch baseband signal based on the fractional delay to obtain a local reference branch baseband signal after the fractional delay adjustment; The self-interference elimination module is used to adopt a recursive least squares adaptive filtering method for the baseband self-interference received signal to obtain a posterior estimation value of the self-interference channel; convolve the local reference branch baseband signal after the fractional delay adjustment with the posterior estimation value of the self-interference channel to obtain a reconstructed signal; and subtract the reconstructed signal from the baseband self-interference received signal to obtain a signal after self-interference elimination.

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