A high-precision estimation and correction method for fractional-delay self-interference signals

Abstract

The application relates to a high-precision estimation and correction method for fractional multiple delay self-interference signals, which adopts Kalman filtering of frequency domain data and a fractional multiple delay estimation and a fractional delay filter of a Farrow structure to perform fractional multiple delay compensation, realizes fractional multiple delay alignment of a local reference signal and a received signal, improves time domain correlation of the local reference signal and the received signal, improves a self-interference cancellation suppression ratio, and meets low-orbit navigation GNSS signal receiving and low-orbit navigation enhancement signal broadcasting requirements.

Description

Technical Field

[0001] This application relates to the field of ground-based navigation enhancement, specifically to a method for high-precision estimation and correction of fractional time-delay self-interference signals. Background Technology

[0002] With the booming development of commercial spaceflight, navigation enhancement technology based on low-Earth orbit (LEO) satellite constellations is becoming a hot topic. LEO satellite navigation constellations, represented by companies like Xona Space and Satellite in the United States, are dedicated to promoting global LEO PNT (Positioning, Navigation, and Timing) services, characterized by high precision, strong signal, fast convergence, and high security. Besides being used independently, another important application of LEO PNT is navigation enhancement for GNSS systems. LEO satellites simultaneously enhance both signal and information by broadcasting downlink navigation enhancement signals and precise ephemeris and clock bias navigation enhancement information from GNSS satellites. To enable LEO navigation signals broadcast by a large-scale LEO constellation to provide LEO PNT services, two major challenges need to be addressed: maintaining the time reference and maintaining the spatial reference. Typically, LEO navigation enhancement satellites carry GNSS receivers to determine clock bias and perform precise orbit determination, thereby unifying the LEO satellite's spatiotemporal reference with that of the GNSS satellite. When the frequency band of the downlink navigation signal overlaps with that of the GNSS signal, it will cause self-interference between the downlink transmitted signal and the GNSS received signal in the same frequency band, causing the GNSS receiver to malfunction and thus affecting the establishment and maintenance of the time and space reference of low-orbit satellites.

[0003] Self-interference cancellation can generally be divided into spatial domain, radio frequency (RF) domain, and digital domain interference suppression. In reality, high self-interference suppression ratios cannot be achieved solely through spatial and RF domain interference cancellation. This is mainly due to the limitations in the precision of RF devices, which prevent these self-interference cancellation performances from meeting requirements. Digital domain interference cancellation employs high-precision adaptive algorithms in the digital domain, resulting in high channel estimation accuracy and the ability to eliminate self-interference signals as completely as possible. However, it suffers from the problem of misalignment between the time delays of the transmitted and received self-interference reference signals. Summary of the Invention

[0004] To overcome at least one deficiency in the prior art, this application provides a high-precision estimation and correction method for fractional time delay self-interference signals.

[0005] Firstly, a high-precision estimation and correction method for fractional time-delay self-interference signals is provided, including:

[0006] Cross-correlation is performed on the baseband self-interference received signal and the local reference branch baseband signal to obtain the integer multiple time delay of the baseband self-interference received signal;

[0007] The shift register generated by the local reference branch baseband signal is adjusted according to the integer multiple delay to achieve alignment of the baseband self-interference received signal and the local reference branch baseband signal with integer multiple delays;

[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 multiple time delay alignment; determine the phase data of the fractional time delay based on the frequency domain complex conjugate multiplication signal; and estimate the fractional time delay using Kalman filtering based on the phase data of the fractional time delay.

[0009] A fractional delay filter with a Farrow structure is used to adjust the local reference branch baseband signal based on a fractional delay, resulting in a local reference branch baseband signal adjusted by a fractional delay.

[0010] A recursive least squares adaptive filtering method is used to obtain the posterior estimate of the self-interference channel for the baseband self-interference received signal. The baseband signal of the local reference branch adjusted by fractional time delay is convolved with the posterior estimate of the self-interference channel to obtain the reconstructed signal. The reconstructed signal is subtracted from the baseband self-interference received signal to obtain the signal after self-interference cancellation.

[0011] In one embodiment, cross-correlation is performed on the baseband self-interference received signal and the local reference branch baseband signal to obtain an integer multiple of the delay of the baseband self-interference received signal, including:

[0012] Cross-correlation is performed on the baseband self-interference received signal and the local reference branch baseband signal, and an objective function is constructed:

[0013]

[0014] Where T is the integer multiple of the time delay estimate, ζ is the time 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] Solving the objective function yields integer multiples of the estimated time delay τ.

[0016] Correcting the integer multiple delay estimate τ yields the final integer multiple delay:

[0017] τ int =τ-1

[0018] Where, τ int The delay is an integer multiple of the time.

[0019] In one embodiment, determining the fractional time delay phase data based on the frequency domain complex conjugate multiplication signal includes:

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

[0021]

[0022] Where, Φ Y For the total phase data, atan2 is the arctangent function in the four quadrants, Y is the frequency domain complex conjugate multiplication signal, Im(Y) is the imaginary part of Y, and Re(Y) is the real part of Y;

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

[0024]

[0025] Φ int =-2πτ int *(1:N2) / Np

[0026] Where, Φ frac This represents the phase data with a fractional time delay; mod is the modulo operation. Indicates 2 to 3 in Y The data corresponding to the sampling points, where Np is the number of sampling points, Φ int Phase data with an integer multiple of the time delay. For Φ int Middle 1 to The data corresponding to the sampling points, τ int Delay is an integer multiple of the time.

[0027] In one embodiment, a fractional delay filter with a Farrow structure is used to adjust the local reference tributary baseband signal based on a fractional delay, resulting in a fractionally delayed local reference tributary baseband signal, including:

[0028] The fractional delay adjustment Δ = τ for a fractional delay filter using a Farrow structure is determined based on the fractional delay factor. frac , τ frac The delay is a fraction of a time.

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

[0030] Secondly, a high-precision estimation and correction device for fractional time delay self-interference signals is provided, comprising:

[0031] The integer multiple delay acquisition module is used to cross-correlate 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] The integer multiple delay correction module is used to adjust the shift register generated by the local reference branch baseband signal according to the integer multiple delay, so as to achieve the alignment of the baseband self-interference received signal and the local reference branch baseband signal with integer multiple delay.

[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 integer multiple delay alignment; determine the phase data of the fractional delay based on the frequency domain complex conjugate multiplication signal; and estimate the fractional delay using Kalman filtering based on the phase data of the fractional delay.

[0034] The fractional delay adjustment module is used to adjust the local reference branch baseband signal based on the fractional delay of the fractional delay filter with the Farrow structure, so as to obtain the local reference branch baseband signal after fractional delay adjustment.

[0035] The self-interference cancellation module is used to apply a recursive least squares adaptive filtering method to the baseband self-interference received signal to obtain the posterior estimate of the self-interference channel; the baseband signal of the local reference branch adjusted by fractional time delay is convolved with the posterior estimate of the self-interference channel to obtain the reconstructed signal; the reconstructed signal is subtracted from the baseband self-interference received signal to obtain the signal after self-interference cancellation.

[0036] Compared with the prior art, this application has the following beneficial effects: This application proposes a high-precision estimation and correction method for fractional time delay self-interference signals. It uses a Kalman filter for frequency domain data to estimate the fractional time delay using a Farrow structure fractional time delay filter for fractional time delay compensation, thereby achieving fractional time delay alignment between the local reference signal and the received signal, improving the time domain coherence of the local reference signal and the received signal, enhancing the self-interference cancellation and suppression ratio, and meeting the requirements for low-orbit navigation GNSS signal reception and low-orbit navigation enhancement signal broadcasting. Attached Figure Description

[0037] This application can be better understood by referring to the description given below in conjunction with the accompanying drawings, which, together with the detailed description below, are incorporated in and form part of this specification. In the drawings:

[0038] Figure 1 A schematic diagram of a high-precision estimation and correction method for fractional time delay self-interference signals is shown.

[0039] Figure 2 The diagram shows the structure of the fractional delay filter with the Farrow architecture;

[0040] Figure 3 A schematic diagram is shown illustrating the time delay estimation bias obtained using traditional cross-correlation time delay estimation and the method of this application;

[0041] Figure 4 The performance comparison chart of the method of this application and the cross-correlation delay estimation method is shown;

[0042] Figure 5 A comparison graph is shown of the root mean square error estimated by the method of this application with the lower bound of Cramer-Rao (CRLB).

[0043] Figure 6 The results of the fractional delay filter with Farrow structure for the total delay of the reference branch signal are shown.

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

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

[0046] Figure 9 The results of self-interference cancellation after fractional-time-multiple time-delay alignment are shown. Detailed Implementation

[0047] Exemplary embodiments of the present application will be described below with reference to the accompanying drawings. For clarity and brevity, not all features of the actual embodiments are described in the specification. However, it should be understood that many embodiment-specific decisions can be made in the development of any such actual embodiment to achieve the developer’s specific objectives, and these decisions may vary as the embodiments differ.

[0048] It should also be noted that, in order to avoid obscuring this application with unnecessary details, only the device structure closely related to the solution according to this application is shown in the accompanying drawings, while other details that are not closely related to this application are omitted.

[0049] It should be understood that this application is not limited to the described embodiments by virtue of the following description with reference to the accompanying drawings. In this document, embodiments may be combined with each other, features may be substituted or borrowed between different embodiments, and one or more features may be omitted in one embodiment, where feasible.

[0050] This application provides a method for high-precision estimation and correction of fractional time-delay self-interference signals. Figure 1 A schematic diagram of a high-precision estimation and correction method for fractional time-delay self-interference signals is shown. (See attached diagram.) Figure 1 The methods include:

[0051] Step S1: Perform cross-correlation between the baseband self-interference received signal and the local reference branch baseband signal to obtain the integer multiple time delay of the baseband self-interference received signal.

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

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

[0054]

[0055] Where τ is the integer multiple of the time delay estimate, ζ is the time 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] Solving the objective function yields integer multiples of the estimated time delay 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] Where, τ int The delay is an integer multiple of the time.

[0060] Step S2: Adjust the shift register generated by the local reference branch baseband signal according to the integer multiple delay to achieve alignment of the baseband self-interference received signal and the local reference branch baseband signal with integer multiple delays.

[0061] After obtaining the final integer multiple delay, the baseband self-interference received signal and the local reference branch baseband signal integer multiple delay need to be aligned to ensure that the delays of the two signals are as consistent as possible, so as to maintain good coherence between the two signals. This can be achieved by shift alignment or by combining 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 multiple time delay alignment; determine the phase data of fractional time delay based on the frequency domain complex conjugate multiplication signal; and estimate the fractional time delay using Kalman filtering based on the phase data of fractional time delay.

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

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

[0065] Where Y is the frequency domain complex conjugate multiplied signal, conj represents complex conjugate, and FFT represents Fast Fourier Transform.

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

[0067] Specifically, the phase data with fractional time delay is determined based on the frequency domain complex conjugate multiplication signal, including:

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

[0069]

[0070] Where, Φ Y For the total phase data, atan2 is the arctangent function in the four quadrants, Y is the frequency domain complex conjugate multiplication signal, Im(Y) is the imaginary part of Y, and Re(Y) is the real part of Y;

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

[0072]

[0073] Φ int =-2πτ int *(1:N2) / Np

[0074] Where, Φ frac This represents the phase data with a fractional time delay; mod is the modulo operation. Indicates 2 to 3 in Y The data corresponding to the sampling points, where Np is the number of sampling points, Φ int Phase data with an integer multiple of the time delay. For Φ int Middle 1 to The data corresponding to the sampling points, τ int Delay is an integer multiple of the time.

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

[0076] Constructing the Kalman filter model:

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

[0078] Φ frac (n) = Hx(n) + v(n)

[0079] Where x(n) is the state variable at time n, φ(n) and Let A be the phase and the rate of phase change, and let H be the state transition matrix and the measurement matrix, respectively. 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 observation noise of the fractional time-delay phase data.

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

[0084] The time update section is as follows:

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

[0086] Where, x - (n) is the prior estimate.

[0087] The status update section is as follows:

[0088]

[0089] in, For 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] Where α and β are filter parameters, parameter ρ = 0.999, and the initial value of the state variable can be chosen as the zero-value vector x(0) = [0 0]. T .

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

[0095]

[0096] Step S4: Using a fractional delay filter with a Farrow structure, the local reference branch baseband signal is adjusted based on the fractional delay to obtain the local reference branch baseband signal after fractional delay adjustment, which can realize the closed-loop time alignment between the local reference branch baseband signal and the baseband self-interference received signal.

[0097] Specifically, the fractional delay adjustment Δ = τ for the fractional delay filter using the Farrow structure is determined based on the fractional delay multiple. frac , τ frac The delay is a fraction of a time.

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

[0099] Step S5: A recursive least squares adaptive filtering method is used for the baseband self-interference received signal to obtain the posterior estimate of the self-interference channel; the baseband signal of the local reference branch adjusted by fractional time delay is convolved with the posterior estimate of the self-interference channel to obtain the reconstructed signal; the reconstructed signal is subtracted from the baseband self-interference received signal to obtain the signal after self-interference cancellation.

[0100] Specifically, the reconstructed signal is obtained by convolving the local reference branch baseband signal adjusted by a fractional time delay with the posterior estimate of the self-interference channel, and is expressed by the following formula:

[0101]

[0102] in, For the reconstructed signal, y2(n-Δ) is the local reference branch baseband signal adjusted by a fractional time delay. Let w(n) represent the convolution, and w(n) be the posterior estimate of the self-interference channel. * (n) is the complex conjugate of w(n).

[0103] Signal after self-interference cancellation

[0104]

[0105] To further verify the effectiveness of the method in this application, the following specific experiments are presented.

[0106] Experiment 1: Fractional delay estimation of received signal and delay compensation of Farrow filter.

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

[0108] The fractional delay of the received signal branch relative to the reference branch is varied starting from 0 sample points, with intervals of 0.05 sample points and ending at 0.95 sample points. The delay estimation errors of the traditional cross-correlation delay estimation method and the method of this application are observed. Figure 3 A schematic diagram is shown illustrating the time delay estimation bias obtained using traditional cross-correlation time delay estimation and the method of this application.

[0109] from Figure 3 As can be seen, the received signal branch has an integer multiple time delay relative to the reference branch of two sample points. Cross-correlation time delay estimation can accurately estimate the time delay of integer multiple sample points, but there is a significant deviation in the estimation of fractional multiple time delays. The estimation deviation is zero at integer multiple sample points, and the deviation is largest in the middle of the two sample points, with a maximum error of 0.5 sample points. The time delay estimation error monotonically decreases to the left and right of 0.5 sample points. This indicates that traditional cross-correlation time delay estimation has a significant time delay estimation deviation for received signals with fractional delays. Using the method of this application, the time delay estimation deviation is zero in the time delay estimation process from 0 to 0.95 fractional sample points. This shows that the method of this application can accurately estimate the fractional time delay of the received signal relative to the local reference signal.

[0110] Furthermore, the characteristics of the root mean square error (RMSE) estimation method in this application as a function of signal-to-noise ratio (SNR) are analyzed. Assuming the integer delay is 2 sampling points, which has already been estimated, and the fractional delay is 0.15 sampling points, when the SNR varies from 20 to 60 dB, Figure 4 A performance comparison chart of the method of this application and the cross-correlation delay estimation method is shown. Using the method of this application, the fractional delay estimation accuracy increases with the increase of the signal-to-noise ratio. This indicates that the stronger the self-interference signal of the received signal, the higher the accuracy of the self-interference delay estimation.

[0111] To analyze the optimality of the proposed method, for the BPSK(10) signal, B = 20.46MHz, Figure 5 A comparison plot of the root mean square error estimated by the method of this application with the lower bound of Cramérault's CRLB is shown. From Figure 5 It can be seen that as the signal-to-noise ratio increases, the root mean square error of the method in this application gradually approaches the lower bound of CRLB, and the fractional time delay estimation method in this application has asymptotic optimality.

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

[0113] After estimating the integer and fractional delays of the received signal relative to the reference signal, integer delay correction and fractional delay correction using the Farrow filter are performed. Figure 7 The fractional time delay estimation and the time-domain convergence results of the self-interference cancellation correction are shown. From Figure 7 As can be seen, the signal amplitude of the signal after self-interference cancellation is significantly reduced compared to the original received signal. The signal after self-interference cancellation converges quickly, and the amplitude of the converged self-interference signal stabilizes at a zero-mean noise level, indicating that the method of fractional time-delay self-interference estimation and corrected adaptive interference cancellation in this application has near-optimal cancellation performance.

[0114] Experiment 2: The impact of fractional time delay estimation and correction on self-interference cancellation capability.

[0115] Assuming the transmitted signal propagates through the on-board 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 later than the reference signal delay. Figure 8 The results of self-interference cancellation without fractional delay alignment are shown, from Figure 8 It can be seen that without fractional delay estimation and alignment for self-interference cancellation, there is a significant residual signal in the main lobe of the BPSK(10) signal spectrum that has not been completely eliminated, and the self-interference cancellation ratio is 58.8dB.

[0116] Fractional delay estimation and Farrow filter fractional delay correction techniques are employed. Figure 9 The results of self-interference cancellation after fractional delay alignment are shown, from Figure 9 It can be seen that after 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 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 high-precision estimation and correction method for 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] The integer multiple delay acquisition module is used to cross-correlate 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] The integer multiple delay correction module is used to adjust the shift register generated by the local reference branch baseband signal according to the integer multiple delay, so as to achieve the alignment of the baseband self-interference received signal and the local reference branch baseband signal with integer multiple delay.

[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 integer multiple delay alignment; determine the phase data of the fractional delay based on the frequency domain complex conjugate multiplication signal; and estimate the fractional delay using Kalman filtering based on the phase data of the fractional delay.

[0121] The fractional delay adjustment module is used to adjust the local reference branch baseband signal based on the fractional delay of the fractional delay filter with the Farrow structure, so as to obtain the local reference branch baseband signal after fractional delay adjustment.

[0122] The self-interference cancellation module is used to apply a recursive least squares adaptive filtering method to the baseband self-interference received signal to obtain the posterior estimate of the self-interference channel; the baseband signal of the local reference branch adjusted by fractional time delay is convolved with the posterior estimate of the self-interference channel to obtain the reconstructed signal; the reconstructed signal is subtracted from the baseband self-interference received signal to obtain the signal after self-interference cancellation.

[0123] The fractional time delay self-interference signal high-precision estimation and correction device of this embodiment has the same inventive concept as the fractional time delay self-interference signal high-precision estimation and correction method described above. Therefore, the specific implementation of this device can be found in the embodiment section of the fractional time delay self-interference signal high-precision estimation and correction method described above, and its technical effect corresponds to the technical effect of the above method, so it will not be repeated here.

[0124] The above descriptions are merely various embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for high-precision estimation and correction of fractional time-delay self-interference signals, characterized in that, include: Cross-correlation is performed on the baseband self-interference received signal and the local reference branch baseband signal to obtain an integer multiple of the delay of the baseband self-interference received signal; The shift register generated by the local reference branch baseband signal is adjusted according to the integer multiple delay to achieve alignment of the baseband self-interference received signal and the local reference branch baseband signal with integer multiple delays; Calculate the frequency domain complex conjugate multiplication signal between the baseband self-interference received signal and the local reference branch baseband signal after integer multiple delay alignment; determine the fractional delay phase data based on the frequency domain complex conjugate multiplication signal; and estimate the fractional delay using Kalman filtering based on the fractional delay phase data. 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 the local reference branch baseband signal adjusted by the fractional delay. A recursive least squares adaptive filtering method is used to obtain the posterior estimate of the self-interference channel for the baseband self-interference received signal; the baseband signal of the local reference branch adjusted by the fractional time delay is convolved with the posterior estimate of the self-interference channel to obtain the reconstructed signal; the reconstructed signal is subtracted from the baseband self-interference received signal to obtain the signal after self-interference cancellation.

2. The method as described in claim 1, characterized in that, in, Cross-correlation is performed on the baseband self-interference received signal and the local reference branch baseband signal to obtain an integer multiple of the time delay of the baseband self-interference received signal, including: Cross-correlation is performed on the baseband self-interference received signal and the local reference branch baseband signal, and an objective function is constructed: Where τ is an integer multiple of the time delay estimate, ζ is the time 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; y1(n+ζ) is the baseband self-interference received signal after the time delay ζ. Solving the objective function yields an integer multiple of the estimated time delay τ. The integer multiple delay estimate τ is corrected to obtain the final integer multiple delay: t int =τ-1 Where, τ int The delay is an integer multiple of the time.

3. The method as described in claim 1, characterized in that, in, Determining the phase data with a fractional time delay based on the frequency domain complex conjugate multiplication signal includes: Calculate the total phase data including both integer multiples and fractional multiples of delay: Where, Φ Y For the total phase data, atan2 is the arctangent function in the four quadrants, Y is the frequency domain complex conjugate multiplication signal, Im(Y) is the imaginary part of Y, and Re(Y) is the real part of Y; Removing phase data with integer multiples of delay from the total phase data yields phase data with fractional multiples of delay: F int =-2pts int *(1:N2) / Np Where, Φ frac This represents the phase data with a fractional time delay; mod is the modulo operation. Indicates 2 to 3 in Y The data corresponding to the sampling points, where Np is the number of sampling points, Φ int Phase data with an integer multiple of the time delay. For Φ int Middle 1 to The data corresponding to the sampling points, τ int Delay is an integer multiple of the time.

4. The method as described in claim 1, characterized in that, in, A fractional delay filter with a Farrow structure is used to adjust the local reference tributary baseband signal based on the fractional delay, resulting in a fractionally delayed local reference tributary baseband signal, including: The fractional delay adjustment Δ = τ for the fractional delay filter using the Farrow structure is determined based on the fractional delay multiplier. frac , τ frac The delay is a fraction of a time. 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.

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

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