An adaptive filtering method based on time-varying multi-channel self-interference elimination

Abstract

The present invention proposes an adaptive filtering method based on time-varying multi-channel self-interference elimination, which belongs to the field of satellite communication reception. Aiming at the shortage of satellite communication spectrum resources, the single antenna receiving left-handed in-phase signal, left-handed orthogonal signal, right-handed in-phase signal, and right-handed orthogonal signal easily causes mutual interference between different signals, especially the mutual interference problem under the condition of certain time-varying received signals. The present invention adopts a method of real-time estimation of the received signal-to-noise ratio combined with the current channel filtering and other channel information assistance, and performs adaptive filtering through the mutual constraints of multiple channel signals, thereby reducing the influence of the time-varying environment on the self-interference filtering of satellite communication signal reception, improving the self-interference elimination performance of satellite communication reception in complex environments, and reducing the bit error rate of satellite communication signal reception in complex environments.

Description

Technical Field

[0001] The invention relates to an adaptive filtering method based on time-varying multi-channel self-interference elimination, which belongs to the field of satellite communication reception and is a synchronous elimination method of multi-channel self-interference, and is particularly suitable for eliminating multi-channel self-interference in communication under complex and changeable environments. Background Art

[0002] In the remote sensing satellite high-speed satellite-to-ground data transmission system, factors such as unsatisfactory channel characteristics such as amplitude-frequency / group delay, I / Q orthogonal imbalance, low isolation between left and right polarization, and mismatch of multiple AD sampling channels have caused signal self-interference between the previous and next code elements in the same channel, between orthogonal channels and polarization channels, reducing the bit error rate performance of the receiving system and affecting the quality of information transmission. At present, the methods of baseband self-interference mainly include single-channel or multi-channel adaptive linear cancellation and nonlinear cancellation. The adaptive cancellation method achieves self-interference cancellation by dynamically adjusting the step size and gain; the nonlinear cancellation method improves the ability to suppress self-interference by modeling and compensating the power amplifier. However, these traditional methods have poor compensation capabilities for time-varying channels, and the performance of traditional multi-channel self-interference elimination technology has seriously declined, and even cannot achieve self-interference suppression. Summary of the invention

[0003] The purpose of the present invention is to solve the problem that spectrum resources are becoming increasingly scarce in satellite communications and spectrum reuse easily forms self-interference, and propose an adaptive filtering method based on time-varying multi-channel self-interference elimination, and adopt an adaptive filtering method based on real-time estimation of channel signal-to-noise ratio to suppress the self-interference between code elements in the satellite-to-ground transmission channel, and increase the noise tolerance limit of the dynamic receiving carrier. The method adopts other channel data to assist the current channel in self-interference elimination, which is a new breakthrough in the satellite communication receiving end self-interference elimination technology.

[0004] The object of the present invention is achieved in that:

[0005] An adaptive filtering method based on time-varying multi-channel self-interference elimination comprises the following steps:

[0006] (1) Initialization of filter parameters: Set the initial filter weight coefficient vector W(0) = [w L1 (0),w L2 (0),w R1 (0),w R2 (0)], where w L1 (0) represents the initial weighting coefficient of the left-handed in-phase signal, w L2 (0) represents the initial weighting coefficient of the left-handed orthogonal signal, w R1 (0) represents the initial weighting coefficient of the right-handed in-phase signal, w R2(0) represents the initial weighting coefficient of the right-handed orthogonal signal; assuming that M is the order of the single-channel filter, the total order of the filter is 4M, and the initial filter cost function is P(0) = I 4M ,in w L1 (0) = w L2 (0) = w R1 (0) = w R2 (0) = [01,…,0 M ], where I 4M It means there are 4M 1s on the diagonal, [01,…,0 M ] indicates that the vector consists of M zeros;

[0007] (2) Receive a left-handed in-phase received signal, a left-handed orthogonal received signal, a right-handed in-phase received signal, and a right-handed orthogonal received signal, and discretize them; the four received signals are expressed as X(n)=[X L1 (n),X L2 (n),X R1 (n),X R2 (n)], n represents a discrete moment, n ≥ 1, where:

[0008] X L1 (n) = [x L1 (n),…,x L1 (nm),…x L1 (n-M+1)]

[0009] X L2 (n) = [x L2 (n),…,x L2 (nm),…x L2 (n-M+1)]

[0010] X R1 (n) = [x R1 (n),…,x R1 (nm),…x R1 (n-M+1)]

[0011] X R2 (n) = [x R2 (n),…,x R2 (nm),…x R2 (n-M+1)]

[0012] If nm≤0, then x L1 (nm) = x L2 (nm) = x R1 (nm) = x R2(nm) = 0, m represents the mth element of the received left-handed in-phase received signal, left-handed orthogonal received signal, right-handed in-phase received signal, and right-handed orthogonal received signal, 0 <m≤M-1;

[0013] For the left-handed in-phase receiving channel, the left-handed orthogonal receiving channel, the right-handed in-phase receiving channel, and the right-handed orthogonal receiving channel, the processing flow of steps (3)-(8) is adopted in parallel to achieve the elimination of self-interference signals of different receiving channels:

[0014] (3) Calculate the weight adjustment coefficient at time n

[0015]

[0016] Among them, J SNR is the signal-to-noise ratio of the received signal, 0<λ<1, X(n)=[X L1 (n),X L2 (n),X R1 (n),X R2 (n)], ξ(n) = 1 + nμ is the filter adjustment coefficient, μ is the filter adjustment factor;

[0017] (4) Calculate the output of the left-hand in-phase, left-hand quadrature, right-hand in-phase, and right-hand quadrature received signals after filtering at time n

[0018]

[0019] Where H represents the conjugate transpose of the vector;

[0020] (5) Let the expected signals of the left-handed in-phase, left-handed quadrature, right-handed in-phase, and right-handed quadrature received signals at time n be d L1 (n), d L2 (n), d R1 (n), d R2 (n), calculate the difference between the expected signal and the filtered estimated signal output:

[0021]

[0022] (6) The difference between the expected signal and the filtered estimated signal output, e(n), and the set threshold T th In comparison, if e(n) <T th , then the output signal at time n is Self-interference filtering has been achieved, if e(n)≥T th , then the nth filtering has not achieved convergence, and the signal will be filtered again after the n+1th filtering.

[0023] (7) According to the filter weight coefficient vector at time n-1 and the difference e(n) between the expected signal and the filter estimation signal output, the filter weight coefficient vector of the left-handed in-phase receiving channel at time n is calculated, that is:

[0024] W(n)=W(n-1)+k(n)e * (n)

[0025] Where * represents the complex conjugate of the vector;

[0026] (8) Calculate the filtering cost function at time n, that is:

[0027] P(n)=λ -1 P(n-1)(1-k(n)X H (n))

[0028] (9) Set

[0029] X L1 (n+1)=[x L1 (n+1),…,x L1 (nm),…x L1 (n-M+2)]

[0030] X L2 (n+1)=[x L2 (n+1),…,x L2 (nm),…x L2 (n-M+2)]

[0031] X R1 (n+1)=[x R1 (n+1),…,x R1 (nm),…x R1 (n-M+2)]

[0032] X R2 (n+1)=[x R2 (n+1),…,x R2 (nm),…x R2 (n-M+2)]

[0033] Repeat steps (3)-(8) to achieve dynamic filtering of the left-hand in-phase, left-hand quadrature, right-hand in-phase, and right-hand quadrature received signals as time progresses.

[0034] The beneficial effects of the present invention are:

[0035] 1. The present invention aims at the self-interference problem of multi-channel reception of satellite communication receiving terminals in a time-varying environment. It adopts real-time estimation of the carrier-to-noise ratio of the received signal, and performs auxiliary filtering on the current channel signal according to the signals of other channels, so as to realize the filtering of self-interference of communication signals in parallel through multiple channels.

[0036] 2. The present invention adopts a method of real-time estimation of the signal-to-noise ratio of the received signal combined with the current channel filtering assisted by other channel information, and performs adaptive filtering through the mutual constraints of multiple channel signals to reduce the impact of the time-varying environment on the self-interference filtering of satellite communication signal reception.

[0037] 3. The present invention can achieve faster convergence in a complex receiving environment by estimating the signal-to-noise ratio of the received signal in real time in combination with the current channel filtering and the assistance of other channel information, and is suitable for eliminating self-interference in time-varying complex channels.

[0038] 4. The present invention is implemented in an iterative form, the amount of calculation is relatively small, and the engineering implementation is relatively simple. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The schematic diagram is a principle block diagram of an adaptive filtering method based on time-varying multi-channel self-interference elimination.

[0040] Figure 2 It is a flow chart of an adaptive filtering method based on time-varying multi-channel self-interference elimination. DETAILED DESCRIPTION

[0041] The present invention will be further described below in conjunction with the accompanying drawings.

[0042] An adaptive filtering method based on time-varying multi-channel self-interference elimination, the principle of which is as follows Figure 1 As shown, the process is as follows Figure 2 As shown, the specific steps include:

[0043] (1) Initialization of filter parameters: Set the initial filter weight coefficient vector W(0) = [w L1 (0),w L2 (0),w R1 (0),w R2 (0)], where w L1 (0) represents the initial weighting coefficient of the left-handed in-phase signal, w L2 (0) represents the initial weighting coefficient of the left-handed orthogonal signal, w R1 (0) represents the initial weighting coefficient of the right-handed in-phase signal, w R2 (0) represents the initial weighting coefficient of the right-handed orthogonal signal. Assuming M is the order of the single-channel filter, the total order of the filter is 4M, and the initial filter cost function is P(0) = I 4M ,in w L1 (0) = w L2 (0) = w R1 (0) = w R2 (0) = [01,…,0 M ], where I 4MIndicates that there are 4M 1s on the diagonal, [01,…,0 M indicates that the vector consists of M zeros.

[0044] (2) Receive the left - hand circular in - phase received signal, left - hand circular quadrature received signal, right - hand circular in - phase received signal, and right - hand circular quadrature received signal, and discretize them; the four received signals are expressed as X(n)=[X L1 (n),X L2 (n),X R1 (n),X R2 (n)], where n represents the discrete time, n≥1, and among them:

[0045] X L1 (n)=[x L1 (n),…,x L1 (n - m),…x L1 (n - M + 1)]

[0046] X L2 (n)=[x L2 (n),…,x L2 (n - m),…x L2 (n - M + 1)]

[0047] X R1 (n)=[x R1 (n),…,x R1 (n - m),…x R1 (n - M + 1)]

[0048] X R2 (n)=[x R2 (n),…,x R2 (n - m),…x R2 (n - M + 1)]

[0049] If n - m≤0, then x L1 (n - m)=x L2 (n - m)=x R1 (n - m)=x R2 (n - m)=0, where m represents the m - th element of the left - hand circular in - phase received signal, left - hand circular quadrature received signal, right - hand circular in - phase received signal, and right - hand circular quadrature received signal, 0 < m≤M - 1. Usually, when the order of the filter is greater than 5, fast convergence can be achieved, that is, M≥5. The higher the order, the better the filtering effect, but the dynamic performance becomes worse and the computational amount increases. It is necessary to select an appropriate filter order according to the actual situation.

[0050] (3) Calculate the weight adjustment coefficient at time n

[0051]

[0052] Among them, J SNR is the signal-to-noise ratio of the received signal, 0<λ<1, X(n) = [X L1 (n),X L2 (n),X R1 (n),X R2 (n)], ξ(n) = 1 + nμ is the filter adjustment coefficient, μ is the filter adjustment factor, usually J SNR The larger the μ, the faster the convergence speed; the smaller the μ, the faster the convergence speed;

[0053] (4) Calculate the output of the left-handed in-phase received signal after filtering at time n

[0054]

[0055] Where H represents the conjugate transpose of the vector;

[0056] (5) Let the expected signal of the left-handed in-phase received signal at time n be d L1 (n), calculate the difference between the expected signal and the filtered estimated signal output:

[0057]

[0058] (6) The difference between the expected signal and the filtered estimated signal output is e L1 (n) and the set threshold T th In comparison, 0 <T th <δ, δ is a very small number close to zero, and in practical applications, δ = 0.05 is usually taken. L1 (n) <T th , then the output signal at time n is Self-interference filtering has been achieved. If e L1 (n)≥T th , then the nth filtering has not achieved convergence, and the signal will be filtered again after the n+1th filtering.

[0059] (7) According to the filter weight coefficient vector at time n-1, the difference between the expected signal and the filter estimation signal output e L1 (n), calculate the filter weight coefficient vector of the left-handed in-phase receiving channel at time n, that is:

[0060]

[0061] Where * represents the complex conjugate of the vector;

[0062] (8) Calculate the filtering cost function at time n, that is:

[0063] P(n)=λ -1 P(n-1)(1-k(n)X H(n))

[0064] (9) Set

[0065] X L1 (n+1)=[x L1 (n+1),…,x L1 (nm),…x L1 (n-M+2)]

[0066] X L2 (n+1)=[x L2 (n+1),…,x L2 (nm),…x L2 (n-M+2)]

[0067] X R1 (n+1)=[x R1 (n+1),…,x R1 (nm),…x R1 (n-M+2)]

[0068] X R2 (n+1)=[x R2 (n+1),…,x R2 (nm),…x R2 (n-M+2)]

[0069] Repeat steps (3)-(8) to achieve dynamic filtering of the left-handed in-phase received signal as time progresses;

[0070] (10) Different expected signals d are set in the left-handed orthogonal receiving channel, the right-handed in-phase receiving channel, and the right-handed orthogonal receiving channel. L2 (n), d R1 (n), d R2 (n), the processing flow of steps (3)-(9) is adopted in parallel to eliminate the self-interference signals of different receiving channels.

[0071] In summary, the present invention aims at the problem that due to the shortage of satellite communication spectrum resources, a single antenna receiving left-handed in-phase signals, left-handed orthogonal signals, right-handed in-phase signals, and right-handed orthogonal signals easily causes mutual interference between different signals, especially the mutual interference problem under the condition that the received signals have a certain time variation. The present invention adopts a method of real-time estimation of the signal-to-noise ratio of the received signal combined with the current channel filtering assisted by other channel information, and performs adaptive filtering through the mutual constraints of multiple channel signals, thereby reducing the influence of the time-varying environment on the self-interference filtering of satellite communication signal reception, improving the self-interference elimination performance of satellite communication reception in complex environments, and reducing the bit error rate of satellite communication signal reception in complex environments.

Claims

1. An adaptive filtering method based on time-varying multi-channel self-interference elimination, characterized in that: The following steps are involved: (1) Initialization of filter parameters: Set the initial filter weight coefficient vector W(0) = [w L1 (0),w L2 (0),w R1 (0),w R2 (0)], where w L1 (0) represents the initial weighting coefficient of the left-handed in-phase signal, w L2 (0) represents the initial weighting coefficient of the left-handed orthogonal signal, w R1 (0) represents the initial weighting coefficient of the right-handed in-phase signal, w R2 (0) represents the initial weighting coefficient of the right-handed orthogonal signal; assuming that M is the order of the single-channel filter, the total order of the filter is 4M, and the initial filter cost function is P(0) = I 4M ,in w L1 (0) = w L2 (0) = w R1 (0) = w R2 (0) = [01,…,0 M ], where I 4M It means there are 4M 1s on the diagonal, [01,…,0 M ] indicates that the vector consists of M zeros; (2) Receive a left-handed in-phase received signal, a left-handed orthogonal received signal, a right-handed in-phase received signal, and a right-handed orthogonal received signal, and discretize them; the four received signals are expressed as X(n)=[X L1 (n),X L2 (n),X R1 (n),X R2 (n)], n represents a discrete moment, n ≥ 1, where: X L1 (n)=[x L1 (n),…,x L1 (n-m),…x L1 (n-M+1)] X L2 (n)=[x L2 (n),…,x L2 (n-m),…x L2 (n-M+1)] X R1 (n)=[x R1 (n),…,x R1 (n-m),…x R1 (n-M+1)] X R2 (n)=[x R2 (n),…,x R2 (n-m),…x R2 (n-M+1)] If nm≤0, then x L1 (nm) = x L2 (nm) = x R1 (nm) = x R2 (nm) = 0, m represents the mth element of the received left-handed in-phase received signal, left-handed orthogonal received signal, right-handed in-phase received signal, and right-handed orthogonal received signal, 0 <m≤M-1; For the left-handed in-phase receiving channel, the left-handed orthogonal receiving channel, the right-handed in-phase receiving channel, and the right-handed orthogonal receiving channel, the processing flow of steps (3)-(8) is adopted in parallel to achieve the elimination of self-interference signals of different receiving channels: (3) Calculate the weight adjustment coefficient at time n Among them, J SNR is the signal-to-noise ratio of the received signal, 0<λ<1, X(n)=[X L1 (n),X L2 (n),X R1 (n),X R2 (n)], ξ(n) = 1 + nμ is the filter adjustment coefficient, μ is the filter adjustment factor; (4) Calculate the output of the left-hand in-phase, left-hand quadrature, right-hand in-phase, and right-hand quadrature received signals after filtering at time n Where H represents the conjugate transpose of the vector; (5) Let the expected signals of the left-handed in-phase, left-handed quadrature, right-handed in-phase, and right-handed quadrature received signals at time n be d L1 (n), d L2 (n), d R1 (n), d R2 (n), calculate the difference between the expected signal and the filtered estimated signal output: (6) The difference between the expected signal and the filtered estimated signal output, e(n), and the set threshold T th In comparison, if e(n) <T th , then the output signal at time n is Self-interference filtering has been achieved, if e(n)≥T th , then the nth filtering has not achieved convergence, and the signal will be filtered again after the n+1th filtering. (7) According to the filter weight coefficient vector at time n-1 and the difference e(n) between the expected signal and the filter estimation signal output, the filter weight coefficient vector of the left-handed in-phase receiving channel at time n is calculated, that is: W(n)=W(n-1)+k(n)e * (n) Where * represents the complex conjugate of the vector; (8) Calculate the filtering cost function at time n, that is: P(n)=λ -1 P(n-1)(1-k(n)X H (n)) (9) Set X L1 (n+1)=[x L1 (n+1),…,x L1 (n-m),…x L1 (n-M+2)] X L2 (n+1)=[x L2 (n+1),…,x L2 (n-m),…x L2 (n-M+2)] X R1 (n+1)=[x R1 (n+1),…,x R1 (n-m),…x R1 (n-M+2)] X R2 (n+1)=[x R2 (n+1),…,x R2 (n-m),…x R2 (n-M+2)] Repeat steps (3)-(8) to achieve dynamic filtering of the left-hand in-phase, left-hand quadrature, right-hand in-phase, and right-hand quadrature received signals as time progresses.

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