Replay-resistant acquisition system based on iterative least mean square error filtering

Abstract

The application discloses an anti-retransmission interference capturing method based on fast iterative minimum mean square error filtering and relates to the technical field of satellite navigation and positioning. In the small-delay retransmission interference scene, iterative multi-parameter joint estimation is carried out on the basis of two-dimensional correlation, the parameter estimation precision is improved through RMMSE filtering, the identification difficulty of target signals and retransmission interference signals is reduced, and the signal detection probability is improved. The system used by the anti-retransmission interference capturing method based on fast iterative minimum mean square error filtering comprises the following modules: a global two-dimensional correlation and peak detection module, a single-path signal parameter estimation initialization module, a single-path signal iterative multi-parameter joint estimation module, an interference reconstruction and elimination module, a local two-dimensional correlation and peak detection module, a double-path signal parameter estimation initialization module, a double-path signal iterative multi-parameter joint estimation module and a signal detection and decision module.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation and positioning technology, and specifically to an anti-forwarding interference acquisition system based on iterative minimum mean square error filtering. Background Technology

[0002] Direct Sequence Spread Spectrum (DSSS) signals possess advantages such as high concealment, strong resistance to interception and interference, and are widely used in satellite navigation and positioning systems, aerospace telemetry and communication systems, and satellite communications. They form the foundation for code division multiple access and pseudorange positioning, playing a crucial role. Therefore, in electronic warfare, DSSS systems are highly vulnerable to electronic interference. With the development of jamming technology, forwarding jamming has emerged. Forwarding jamming utilizes jamming equipment to receive DSSS signals, delay them, and then forward them. When the jammer's forwarding delay of the target signal is less than two chips, the autocorrelation main lobes of the forwarding jamming and the target signal overlap in the received signal, making it difficult for conventional acquisition algorithms to detect and identify the forwarding jamming and the target signal.

[0003] If the signal acquisition stage is spoofed by forwarding interference, it will output incorrect pseudo-code phase information. The tracking loop is prone to loss of lock or incorrect lock, making it difficult for the receiver to complete the synchronization and demodulation of the target signal in a short time, thus reducing communication or measurement efficiency.

[0004] The article "Spoofing and Anti-Spoofing Technologies of Global Navigation Satellite System: A Survey" published by Zhijun Wu et al. in IEEE Access Vol. 8, pp. 165444-165496 in 2020 provides a review of subspace projection algorithms. These algorithms divide the space into subspaces based on the differences in the frequency domain distribution characteristics of signals and interference, thereby suppressing interference. However, the computational complexity of this algorithm is high. In the article "Multistatic adaptive pulse compression" published by Shannon D. Blunt et al. in IEEE Transactions on Aerospace and Electronic Systems Vol. 42, No. 3, pp. 891-903 in 2006, a multistatic adaptive pulse compression (MAPC) algorithm based on iterative minimum mean square error (RMMSE) filtering was proposed. This algorithm combines matched filtering and RMMSE filtering to simultaneously achieve energy accumulation and parameter estimation, thus providing anti-multiple access interference for linear frequency modulated (LFM) signal systems. Modeling the direct-sequence spread spectrum (DSSS) signal according to the applicable signal model of RMMSE filtering allows the RMMSE filtering algorithm in radar signal range sidelobe suppression to be applied to DSS signal anti-interference.

[0005] The RMMSE filtering algorithm divides the parameter uncertainty space into a multi-dimensional search grid and performs RMMSE filtering within each search cell, resulting in high parameter estimation accuracy. Therefore, RMMSE filtering can be applied to anti-forwarding interference for direct-sequence spread spectrum signals. However, the calculation of the filtering coefficients in the RMMSE filtering algorithm is complex, and the large number of search cells increases the algorithm's complexity when the parameter uncertainty space is large. In addition, when constructing the model, RMMSE filtering treats the center position of the search cell (i.e., the grid point) as the parameter estimate, which introduces model errors, affecting the performance of RMMSE filtering and leading to a decrease in the algorithm's interference suppression capability.

[0006] Therefore, in low-delay forwarding interference scenarios (i.e., when the forwarding delay of the jammer to the target signal is less than 2 chips), the identification of the target signal and the forwarding interference signal is more difficult, and the signal detection probability decreases. In this case, due to the large parameter uncertainty space and the large number of search units, the RMMSE filtering algorithm still cannot solve the problem. Summary of the Invention

[0007] In view of this, the present invention provides an anti-forwarding interference acquisition method based on fast iterative minimum mean square error filtering. For small-delay forwarding interference scenarios, it performs iterative multi-parameter joint estimation based on two-dimensional correlation, improves parameter estimation accuracy through RMMSE filtering, reduces the difficulty of identifying target signals and forwarding interference signals, and improves signal detection probability.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows: an anti-forwarding interference acquisition system based on iterative minimum mean square error filtering, comprising a global two-dimensional correlation and peak detection module, a single-channel signal parameter estimation initialization module, a single-channel signal iterative multi-parameter joint estimation module, an interference reconstruction and elimination module, a local two-dimensional correlation and peak detection module, a dual-channel signal parameter estimation initialization module, a dual-channel signal iterative multi-parameter joint estimation module, and a signal detection and decision module.

[0009] First, in the receiving channel, based on the phase search interval Δ T =T s and frequency search interval △ F The uncertainties in phase and frequency are divided into a two-dimensional search grid, with each grid denoted as a two-dimensional search unit, where T... s The sampling interval is denoted as .

[0010] The global two-dimensional correlation and peak detection module is used to perform zero-padding matched filtering and non-coherent accumulation on the received signal in each phase-frequency two-dimensional search unit to obtain the correlation value, and to perform peak detection on the correlation value to obtain the local correlation peak. The pseudo-code phase value and Doppler frequency value of the search unit where the peak value of the local correlation peak is located are recorded as the pseudo-code phase coarse estimate and Doppler frequency coarse estimate of the forwarding interference signal.

[0011] The single-channel signal parameter estimation initialization module is used to set the initial values ​​of the parameters to be estimated during the iterative multi-parameter joint estimation process of a single-channel signal; it sets the complex amplitude, phase deviation, and frequency deviation of the target signal to 0, sets the initial values ​​of the phase deviation and frequency deviation of the forwarding interference signal to 0, and sets the initial value of the complex amplitude of the forwarding interference signal according to the pseudo-code phase coarse estimate and Doppler frequency coarse estimate of the forwarding interference signal.

[0012] The single-channel signal iterative multi-parameter joint estimation module uses the RMMSE filtering model after deviation compensation to iteratively estimate the phase deviation, frequency deviation, and complex amplitude of the forwarding interference signal.

[0013] The interference reconstruction and elimination module reconstructs the interference signal based on the estimated values ​​of the complex amplitude vector, phase deviation, and frequency deviation of the forwarding interference signal obtained by the single-channel signal iterative multi-parameter joint estimation module, eliminates the reconstructed interference signal from the received signal, and outputs the received signal after interference elimination to the local two-dimensional correlation and peak detection module.

[0014] The local two-dimensional correlation and peak detection module performs local two-dimensional correlation within two chip ranges to the left and right of the global correlation peak, and performs peak detection to obtain the local two-dimensional correlation peak. The detected local two-dimensional correlation peak is used as the detection decision quantity for detection decision, and the pseudo-code phase and Doppler frequency corresponding to the local two-dimensional correlation peak are regarded as the coarse estimates of the pseudo-code phase and Doppler frequency of the target signal.

[0015] The dual-channel signal parameter estimation initialization module is used to set the complex amplitude, phase deviation, and frequency deviation of the forwarded interference signal to the output of the single-channel signal iterative multi-parameter joint estimation module; set the initial values ​​of the phase deviation and frequency deviation of the target signal to 0, and set the initial value of the complex amplitude of the target signal according to the coarse estimate of the pseudocode phase and the coarse estimate of the Doppler frequency of the target signal.

[0016] The dual-channel signal iterative multi-parameter joint estimation module uses the RMMSE filtering model after deviation compensation to simultaneously perform iterative estimation of phase deviation, frequency deviation, and complex amplitude of the forwarding interference signal and the target signal, and outputs the pseudo-code phase and complex amplitude estimates of the two signals to the signal detection and decision module.

[0017] After obtaining the pseudo-code phase and complex amplitude estimates of the two signals, the signal detection and decision module uses the 2-norm of the complex amplitude estimate and the pseudo-code phase estimate as detection and decision quantities to make a detection decision and outputs the signal acquisition result.

[0018] Furthermore, the single-channel signal iterative multi-parameter joint estimation module performs iterative estimation of phase deviation, frequency deviation, and complex amplitude of the signal. It includes a complex amplitude estimation submodule, a signal cancellation submodule for deviation estimation, a phase deviation estimation submodule, a frequency deviation estimation submodule, a parameter estimation correction and update submodule, and a convergence decision submodule.

[0019] The complex amplitude estimation submodule is used to perform complex amplitude estimation on the forwarding interference signal based on RMMSE filtering with off-scale compensation. The complex amplitude estimate of the forwarding interference signal is then sent to the parameter estimation correction and update submodule.

[0020] The signal cancellation submodule is used to reconstruct the target signal and remove the reconstructed target signal from the received signal. The calculation results are output to the phase deviation estimation module and the frequency deviation estimation module.

[0021] The phase deviation estimation submodule is used to estimate the phase deviation of the forwarding interference signal. The estimated phase deviation value of the forwarding interference signal is sent to the parameter estimation correction and update submodule.

[0022] The frequency deviation estimation submodule is used to estimate the frequency deviation of the forwarding interference signal. The estimated frequency deviation of the forwarding interference signal is sent to the parameter estimation correction and update submodule.

[0023] The parameter estimation correction and update submodule is used to update the complex amplitude estimate, phase deviation estimate, and frequency deviation estimate of the forwarding interference signal and output them to the convergence decision module.

[0024] The convergence decision submodule determines whether the multi-parameter joint estimation iteration has converged and performs logic control. If the difference between the parameter estimates in two consecutive iterations is less than the convergence decision threshold, the iteration is considered to have converged. The amplitude estimate obtained in the last iteration is used as the final complex amplitude estimate, the phase deviation estimate obtained in the last iteration is used as the final phase deviation estimate, and the frequency deviation estimate obtained in the last iteration is used as the final frequency deviation estimate. The final complex amplitude estimate, phase deviation estimate, and frequency deviation estimate are output to the interference reconstruction and cancellation module.

[0025] If the difference between the parameter estimates in two consecutive iterations is not less than the convergence decision threshold, the iteration is considered not to have converged. The iteration counter j is incremented by 1, and the parameter estimation results are fed back to the single-channel signal iterative multi-parameter joint estimation module for the next iteration.

[0026] Furthermore, the signal detection and decision module specifically includes:

[0027] If the 2-norm of the complex amplitude estimate of one signal is higher than the set power detection threshold 1, and if only one signal's power estimate is higher than the power detection threshold 1, then that signal is determined to be a forwarding interference signal, and the result of target signal acquisition failure is output. If the 2-norm of the complex amplitude estimates of both signals is higher than the detection threshold, but the pseudo-code phase estimate of the signal with the larger 2-norm of the complex amplitude estimate is smaller than that of the other signal, then the signal with the larger 2-norm of the complex amplitude estimate is considered to be forwarding interference, and the result of target signal acquisition failure is output. If the 2-norm of the complex amplitude estimates of both signals is higher than the power detection threshold 1, and the pseudo-code phase estimate of the signal with the larger 2-norm of the complex amplitude estimate is greater than that of the other smaller signal, then the signal with the smaller 2-norm of the complex amplitude estimate is considered the target signal, and the result of target signal acquisition success is output.

[0028] If the 2-norm of the complex amplitude estimates of both signals is less than the power detection threshold 1, and the power estimates of both signals are higher than the power detection threshold 2, where the power detection threshold 2 is less than the power detection threshold 1, then forwarding interference is considered to exist. The signal with the smaller pseudocode phase estimate is considered the target signal, and the result of successful target signal acquisition is output. If the 2-norm of the complex amplitude estimate of only one signal is higher than the power detection threshold 2, then the target signal is considered to be detected, and the result of successful target signal acquisition is output. If the 2-norm of the complex amplitude estimates of both signals is lower than the power detection threshold 2, then there is no interference, and the result of failed target signal acquisition is output.

[0029] Beneficial effects:

[0030] 1. The proposed anti-small-delay forwarding interference method applies RMMSE filtering to suppress forwarding interference. First, the pseudocode phase and Doppler frequency are divided into a two-dimensional search grid. In each search unit, the correlation value between the received signal and the characteristic waveform is calculated, and global correlation peak detection is performed. Then, the global correlation peak is temporarily considered as the interference signal correlation peak, and precise estimates of phase deviation (the difference between the true parameter value and the search grid points is called deviation) and frequency deviation are performed. The estimated values ​​are used to correct the RMMSE filtering model, and a precise estimate of the signal complex amplitude is obtained through RMMSE filtering with deviation compensation. Then, based on the phase, Doppler frequency, and complex amplitude estimates, the forwarding interference signal is reconstructed and removed from the received signal. Local two-dimensional correlation and peak detection are performed near the global correlation peak, and the local correlation peak is considered as the target signal correlation peak. Next, precise parameter estimation of both the forwarding interference and the target signal is performed through RMMSE filtering with deviation compensation. After completing the parameter estimation of both signals, signal detection and identification are performed based on the pseudocode phase and complex amplitude estimates of the two signals, and the acquisition result is output. Therefore, this invention improves the accuracy of parameter estimation by using RMMSE filtering, reduces the difficulty of identifying target signals and forwarding interference signals, and increases the probability of signal detection.

[0031] 2. The anti-forwarding interference method proposed in this invention based on fast iterative minimum mean square error filtering is designed for low-delay forwarding interference scenarios. It performs iterative multi-parameter joint estimation based on two-dimensional correlation, improves parameter estimation accuracy through RMMSE filtering, reduces the difficulty of identifying target signals and forwarding interference signals, and increases signal detection probability.

[0032] 3. The anti-forwarding interference method based on fast iterative minimum mean square error filtering proposed in this invention uses the estimated values ​​of phase deviation and frequency deviation to correct the RMMSE filtering model, thereby improving the estimation accuracy of phase, frequency, and complex amplitude. The RMMSE filtering input and coefficient matrix are calculated using a two-dimensional parallel correlation method, reducing the computational load of RMMSE filtering.

[0033] 4. The anti-forwarding interference method proposed in this invention based on fast iterative minimum mean square error filtering achieves high parameter estimation accuracy, high interference reconstruction accuracy, and good interference cancellation effect through iterative adaptive filtering. Even under adverse operating conditions where the zero-forwarding Doppler frequency and forwarding interference power are close to or much higher than the target signal, the proposed algorithm can effectively improve pseudocode phase estimation performance and signal detection performance. Attached Figure Description

[0034] Figure 1 The diagram shows the structure of the anti-forwarding interference acquisition method based on fast iterative minimum mean square error filtering.

[0035] Figure 2 This is a block diagram of the single-channel signal iterative multi-parameter joint estimation module.

[0036] Figure 3 This is a flowchart of the signal detection and decision method. Detailed Implementation

[0037] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0038] The purpose of this invention is to provide an anti-forwarding interference acquisition method based on fast iterative minimum mean square error filtering, which solves the problem of decreased detection performance of conventional anti-interference methods when the forwarding delay is less than or equal to 2 chips.

[0039] The proposed anti-small-delay forwarding interference method applies RMMSE filtering to suppress forwarding interference. First, the pseudocode phase and Doppler frequency are divided into a two-dimensional search grid. In each search cell, the correlation value between the received signal and the characteristic waveform is calculated, and global correlation peak detection is performed. Then, the global correlation peak is temporarily considered as the interference signal correlation peak, and precise estimates of phase deviation (the difference between the true parameter value and the search grid points is called deviation) and frequency deviation are performed. The RMMSE filtering model is corrected using these estimates, and a precise estimate of the signal complex amplitude is obtained through RMMSE filtering with deviation compensation. Then, based on the phase, Doppler frequency, and complex amplitude estimates, the forwarding interference signal is reconstructed and removed from the received signal. Local two-dimensional correlation and peak detection are performed near the global correlation peak, and the local correlation peak is considered as the target signal correlation peak. Next, precise parameter estimation of both the forwarding interference and the target signal is performed using RMMSE filtering with deviation compensation. After parameter estimation of both signals, signal detection and identification are performed based on the pseudocode phase and complex amplitude estimates of the two signals, and the acquisition result is output.

[0040] The technical solution for implementing the present invention is as follows:

[0041] The system used in the anti-forwarding interference acquisition method based on fast iterative minimum mean square error filtering consists of the following components: Figure 1 As shown, it includes a global two-dimensional correlation and peak detection module, a single-channel signal parameter estimation and initialization module, a single-channel signal iterative multi-parameter joint estimation module, an interference reconstruction and cancellation module, a local two-dimensional correlation and peak detection module, a dual-channel signal parameter estimation and initialization module, a dual-channel signal iterative multi-parameter joint estimation module, and a signal detection and decision module. It includes the following steps (where bold letters represent sets or matrices):

[0042] (1) In the receiving channel, according to the phase search interval Δ T =T s (T s (sampling interval) and frequency search interval Δ F The uncertainty range of phase and frequency is divided into a two-dimensional search grid.

[0043] (2) The global two-dimensional correlation and peak detection module performs zero-padding matched filtering and non-coherent accumulation on the received signal in each phase-frequency two-dimensional search unit to obtain the correlation value. The sampled data for two-dimensional correlation is divided into P segments at equal intervals. Coherent accumulation is first performed within each segment. Then, the P coherent accumulation values ​​are squared to remove the symbol phase. Finally, the squared values ​​are summed to complete the cross-symbol energy accumulation and obtain the correlation value. Peak detection is performed on the obtained correlation value. To ensure high energy accumulation efficiency, the coherent accumulation time must be divisible by the symbol duration.

[0044] The scalar form of the baseband received signal can be expressed as:

[0045]

[0046] Where: y l T represents the baseband received signal corresponding to sampling point l, where l represents the sampling point index; s Indicates the sampling interval; A D τ D and f D Represent the complex amplitude, pseudocode phase, and Doppler frequency of the target signal, respectively; A I τ I and f I The complex amplitude, pseudocode phase, and Doppler frequency of the relayed interference signal are represented respectively; d(·)∈{-1,1} represents the data symbol, and c(·) represents the spreading pseudocode; g Base (·) represents the baseband waveform of the pseudocode, with a length of (2N). B +1)T s ;v l This represents complex Gaussian white noise with power σ. 2 ;T code κ is the length of a single chip; κ is an integer that satisfies -NB ≤κ≤N B .

[0047] Define the coherent accumulation time as T coh The received signal sampling sequence during the p-th coherent accumulation time period can be represented as:

[0048]

[0049] Where: y p The received signal sampling sequence during the p-th coherent accumulation time period, They represent y respectively p L in s The received signal value corresponding to each sampling point, where the superscript T indicates transpose, and L... s =T coh / T s The number of sampling points within the coherent accumulation time period, For noise vectors, L respectively s The noise value corresponding to each sampling point, s +,p (τ,f) and s -,p (τ,f) represent the baseband characteristic waveforms when there is an unsigned bit transition and when there is a signed bit transition, respectively. The values ​​of τ include τ D and τ I The values ​​of f include f D and f I s +,p The l-th element of (τ,f) can be represented as

[0050]

[0051] s -,p The l-th element of (τ,f) can be represented as

[0052]

[0053] Where θ=T coh -mod(τ,T coh ) represents the sign transition time within the coherent accumulation time interval, sign[·] is the sign function, and x +,D,p x -,D,p x +,I,p and x -,I,p Let y be the complex amplitude corresponding to each characteristic waveform in the received signal. p If there is no sign transition, then

[0054]

[0055] If y p If a sign transition exists, then

[0056]

[0057] Based on the above definition, the expression for calculating the energy accumulation value within a search unit is:

[0058]

[0059] Where: q=l(2K+1)+K+k is a simplified representation of the two-dimensional search unit index of phase and frequency; l is the index of the phase-dimensional search unit, L is the number of phase-dimensional search units, k is the index of the frequency-dimensional search unit, 2K+1 is the number of frequency-dimensional search units, the superscript H indicates the conjugate transpose, P is the number of non-coherent accumulations, and w MF,q,p The matched filter coefficients in the search unit (l,k) are expressed as follows:

[0060]

[0061] in: For pseudocode phase segment search index, This indicates rounding down to the nearest integer.

[0062] After the energy accumulation value is calculated, it is expressed in h. n,l,k The peak value is used as the detection decision quantity for detection and decision-making, and the pseudocode phase value of the search unit where the peak value is located is recorded. and Doppler frequency value And temporarily regard it as a coarse estimate of the pseudocode phase and a coarse estimate of the Doppler frequency of the relay interference signal.

[0063] (3) The single-channel signal parameter estimation initialization module sets the initial values ​​of the parameters to be estimated during the iterative multi-parameter joint estimation of the single-channel signal. It sets the complex amplitude, phase deviation, and frequency deviation of the target signal to 0, and sets the initial values ​​of the phase deviation and frequency deviation of the forwarding interference signal to 0. Based on the coarse estimate of the pseudo-code phase and the coarse estimate of the Doppler frequency of the forwarding interference signal, it sets the initial value of the complex amplitude of the forwarding interference signal, specifically as follows:

[0064]

[0065] in: The initial complex amplitude value corresponding to the baseband characteristic waveform of the unsigned bit transition of the forwarding interference signal; The initial complex amplitude value corresponding to the baseband characteristic waveform when there is a sign bit transition in the forwarding interference signal.

[0066] (4) The single-channel signal iterative multi-parameter joint estimation module performs iterative estimation of phase deviation, frequency deviation and complex amplitude of the signal, including complex amplitude estimation module (including correlation operation submodule, filter coefficient calculation submodule and filtering submodule), signal elimination module for deviation estimation, phase deviation estimation module, frequency deviation estimation module, parameter estimation value correction and update module and convergence decision module.

[0067] To rewrite the received signal model into a format suitable for RMMSE filtering, the following definitions are made:

[0068] The coarse estimates of the pseudocode phase and Doppler frequency of the forwarding interference signal obtained through global correlation peak detection are: and The coarse estimates of the pseudocode phase and Doppler frequency of the target signal obtained by local correlation peak detection are: and The phase deviation of the relayed interference signal is Frequency deviation is The phase deviation of the target signal is Frequency deviation is The phase deviation estimate of the forwarded interference signal in the search unit of the unsigned transition is defined as follows: The estimated value of frequency out-of-range is The estimated phase deviation of the target signal is The estimated value of frequency out-of-range is The estimated phase deviation of the relayed interference signal in the search unit with sign transition is: The estimated value of frequency out-of-range is The estimated phase deviation of the target signal is The estimated value of frequency out-of-range is

[0069] Based on the above definition, the received signal sampling sequence within the p-th (p=0,1,…,P-1) coherent accumulation time interval can be represented as:

[0070]

[0071] Based on the signal model of Equation (10), the single-channel signal iterative multi-parameter joint estimation module has the following structure: Figure 2 As shown, it includes the following steps:

[0072] (4.1) The complex amplitude estimation module estimates the signal complex amplitude based on RMMSE filtering with off-scale compensation. The following example uses a forwarded interference signal in an unsigned transition search unit to illustrate the complex amplitude estimation process. In search units with sign transitions, simply replace "+,I" with "-,I". The complex amplitude estimation module includes a correlation operation submodule, a filter coefficient calculation submodule, and a filtering submodule, specifically:

[0073] (4.1.1) The related operation submodule calculates the related operation result z. +,I,p The result is then output to the filtering module. To reduce the complexity of RMMSE filtering, a low-complexity two-dimensional parallel correlation calculation is used to compute the pseudocode phase-Doppler frequency two-dimensional characteristic waveform correlation matrix.

[0074] The pseudocode phase dimension filter of the RMMSE filter is defined as order 2l. T +1, the Doppler frequency-dimensional filter order is 2l. F +1. First, with The received signal is truncated to a length of 2l starting from the beginning. T If the signal segment is +1, then the q-th segment can be represented as

[0075]

[0076] Then Overlapping arrangement yields

[0077]

[0078] Where: n C =N T -2(2l T +1) represents the zero-padding number, N T For greater than 2l T +1 and much smaller than L s powers of 2 The number of segments to truncate the received signal. This indicates rounding up to the nearest integer.

[0079] Next, the correlation coefficient matrix of the j-th (j=1,……) iteration will be... Rearranging the elements in the array yields the following result:

[0080]

[0081] in:

[0082]

[0083] Finally, the coherent accumulation value is calculated using a two-dimensional Fast Fourier Transform (FFT) to obtain the two-dimensional parallel correlation value.

[0084]

[0085] Among them: FFT R (·) and FFT C (·) indicates performing an FFT on the matrix by rows and columns, respectively; Cut R [C,a,b] and Cut C [C,a,b] represents extracting a portion of rows and columns from matrix C, where a is the lower bound of the index of the extracted row or column, and b is the upper bound of the index of the extracted row or column. For the partial correlation value of the phase dimension of the pseudocode, the expression is:

[0086]

[0087] (4.1.2) The filter coefficient calculation submodule calculates the filter coefficients for the j-th iteration of filtering of the forwarding interference signal according to equation (17). It is then output to the filtering module.

[0088]

[0089] in, Let be the two-dimensional correlation matrix of the characteristic waveform in the j-th iteration, expressed as:

[0090]

[0091] Where r(·) represents the calculation of the correlation value, Let be the correlation coefficient matrix in the unsigned jump search unit of the forwarding interference signal in the j-th iteration. The correlation coefficient matrix exists in the symbol transition search unit for the forwarding interference signal in the j-th iteration. Let be the correlation coefficient matrix in the unsigned jump search unit of the target signal in the j-th iteration. For the target signal in the j-th iteration, there exists a correlation coefficient matrix in the sign-jump search unit. The noise power after coherent accumulation. The expression is

[0092]

[0093] in The expression is V p The expression is:

[0094]

[0095] The matrix related to the second moment of the complex amplitude is specifically...

[0096]

[0097] In the first iteration, the initial value set in step (3) is used to calculate... and filter coefficients Subsequently, the filter coefficients are calculated based on the complex amplitude estimate of the filter output from the j-th iteration.

[0098] (4.1.3) The filtering submodule processes the correlation results according to equation (22). Filtering is performed to obtain the complex amplitude estimate in the unsigned jump search unit of the j-th iteration output. The result is then output to the parameter estimation correction and update module.

[0099]

[0100] (4.2) When estimating the outlier of the forwarding interference signal, the signal cancellation module used for outlier estimation reconstructs the target signal and removes it from the received signal (similarly, when estimating the outlier of the target signal, the forwarding interference signal needs to be reconstructed and removed). The calculation result is output to the phase outlier estimation module and the frequency outlier estimation module. The signal stripped of the target signal in the unsigned transition search unit can be represented as:

[0101]

[0102] in, This is equivalent noise. Due to the unsigned transition, x... -,D,p =x -,I,p =0, It can be represented as

[0103]

[0104] The signal after stripping the target signal in the sign-jump search unit can be represented as:

[0105]

[0106] When a sign transition exists, x +,D,p =x +,I,p =0, It can be represented as

[0107]

[0108] (4.3) The calculation principle of the phase deviation estimation module is as follows:

[0109] In the search unit of the unsigned transition, the phase is generated as and (δT The local characteristic waveform (with step value) is calculated, and the local characteristic waveform is compared with... The amplitude of the normalized correlation value is then calculated, and the difference between the two is calculated. The output is sent to the parameter estimation correction and update module. The calculation formula is as follows:

[0110]

[0111] In the search unit where a sign transition exists, the generated phase is and (δ T The local characteristic waveform (with step value) is calculated, and the local characteristic waveform is compared with... The amplitude of the normalized correlation value is then calculated, and the difference between the two is calculated. The output is sent to the parameter estimation correction and update module. The calculation formula is as follows:

[0112]

[0113] (4.4) The calculation principle of the frequency deviation estimation module is as follows:

[0114] In the search unit of an unsigned transition, the generation frequency is and (δ F The local characteristic waveform (with step value) is calculated, and the local characteristic waveform is compared with... The amplitude of the normalized correlation value is then calculated, and the difference between the two is calculated. The output is sent to the parameter estimation correction and update module. The calculation formula is as follows:

[0115]

[0116] In the search unit where sign transitions exist, the generation frequency is and (δ F The local characteristic waveform (with step value) is calculated, and the local characteristic waveform is compared with... The amplitude of the normalized correlation value is then calculated, and the difference between the two is calculated. The output is sent to the parameter estimation correction and update module. The calculation formula is as follows:

[0117]

[0118] (4.5) The parameter estimation correction and update module includes the following three steps:

[0119] (4.5.1) Complex amplitude estimation results based on different signal segment indices p The complex amplitude of the signal is corrected to obtain the complex amplitude estimate for the j-th iteration. And output it to the convergence decision module.

[0120] when At that time, the complex amplitude estimate is

[0121]

[0122] when At that time, the complex amplitude estimate is

[0123]

[0124] (4.5.2) Correlation difference under different signal segment indices p based on the phase deviation estimation module output Estimates of phase deviation The update is performed and output to the convergence decision module. The update process is as follows:

[0125]

[0126] Where β μ For constant coefficients, ← indicates that the value on the left is updated to the value on the right.

[0127] (4.5.3) Correlation difference under different signal segment indices p based on the frequency deviation estimation module output Estimates of frequency out-of-range values The update is performed and output to the convergence decision module. The update process is as follows:

[0128]

[0129] Where β η Constant coefficient.

[0130] (4.6) The convergence decision module determines whether the multi-parameter joint estimation iteration has converged and performs logic control. If the difference between the parameter estimates in two consecutive iterations is less than the convergence decision threshold, the iteration is considered to have converged, and the magnitude estimate is adjusted accordingly. As the final complex amplitude estimate Phase deviation estimate As the final estimate of phase deviation Frequency out-of-range estimate As the final estimate of frequency deviation Output to the interference reconstruction and elimination module; otherwise, it is considered that the iteration has not converged, the iteration counter j is incremented by 1, the estimation results of each parameter are fed back to the iterative multi-parameter joint estimation module, and the process proceeds to step (4.1) for the next iteration.

[0131] (5) After the single-channel signal iterative multi-parameter joint estimation converges, the interference reconstruction and cancellation module reconstructs the interference signal based on the estimated values ​​of the complex amplitude vector, phase deviation, and frequency deviation of the forwarded interference signal, and removes it from the received signal. The received signal after interference removal is then output to the local two-dimensional correlation and peak detection module. The calculation formula is as follows:

[0132]

[0133] (6) The local two-dimensional correlation and peak detection module performs local two-dimensional correlation and peak detection within the range near the global correlation peak obtained in step (2), that is, within two chip ranges to the left and right of the global correlation peak. The formula for calculating the energy accumulation value within the search unit is as follows:

[0134]

[0135] The definitions of each variable are given in formulas (7) and (8).

[0136] After the energy accumulation value is calculated, it is expressed in h. n,l,k The peak value is used as the detection decision quantity for detection and decision-making, and is temporarily regarded as the target signal. The pseudo-code phase coarse estimate of the signal is recorded. Rough estimate of Doppler frequency

[0137] (7) The dual-channel signal parameter estimation initialization module sets the initial values ​​of the parameters to be estimated during the dual-channel signal iterative multi-parameter joint estimation process. It sets the complex amplitude, phase deviation, and frequency deviation of the forwarding interference signal to the output of the single-channel signal iterative multi-parameter joint estimation. It sets the initial values ​​of the target signal's phase deviation and frequency deviation to 0, and sets the initial value of the target signal's complex amplitude to...

[0138]

[0139] (8) The dual-channel signal iterative multi-parameter joint estimation module regards the pseudo-code phase and Doppler frequency corresponding to the global two-dimensional correlation peak as the coarse estimate of the pseudo-code phase and Doppler frequency of the forwarding interference signal; regards the pseudo-code phase and Doppler frequency corresponding to the local two-dimensional correlation peak as the coarse estimate of the pseudo-code phase and Doppler frequency of the target signal, and performs iterative estimation of phase deviation, frequency deviation and signal complex amplitude for both the forwarding interference signal and the target signal, and outputs the pseudo-code phase and complex amplitude estimates of the two signals to the signal detection and decision module.

[0140] The dual-channel signal iterative multi-parameter joint estimation can be regarded as the iterative multi-parameter joint estimation of two independent single-channel signals, so the calculation process is the same as step (4). In the iterative multi-parameter joint estimation process of the target signal, the "+,I" in each of the terms in equations (11) to (35) is replaced with "+,D" and "-,I" is replaced with "-,D", so that the estimated values ​​of the complex amplitude, phase deviation and frequency deviation of the target signal can be obtained.

[0141] Once the iterative multi-parameter joint estimation of both signals converges, the pseudo-code phase and complex amplitude estimates of the two signals are output to the signal detection and decision module.

[0142] (9) After obtaining the pseudo-code phase and complex amplitude estimates of the two signals, the signal detection and decision module uses the 2-norm of the complex amplitude estimate and the pseudo-code phase estimate as detection decision quantities to make a detection decision and outputs the signal acquisition result. The signal detection and decision method is as follows: Figure 3 As shown.

[0143] If the 2-norm of the complex amplitude estimate of one signal is higher than the set power detection threshold 1, and if only one signal's power estimate is higher than the power detection threshold 1, then that signal is determined to be a forwarding interference signal, and the result of target signal acquisition failure is output. If the 2-norm of the complex amplitude estimates of both signals is higher than the detection threshold, but the pseudo-code phase estimate of the signal with the larger 2-norm of the complex amplitude estimate is smaller than that of the other signal, then the signal with the larger 2-norm of the complex amplitude estimate is considered to be forwarding interference, and the result of target signal acquisition failure is output. If the 2-norm of the complex amplitude estimates of both signals is higher than the power detection threshold 1, and the pseudo-code phase estimate of the signal with the larger 2-norm of the complex amplitude estimate is greater than that of the other smaller signal, then the signal with the smaller 2-norm of the complex amplitude estimate is considered the target signal, and the result of target signal acquisition success is output.

[0144] If the 2-norm of the complex amplitude estimates of both signals is less than the power detection threshold 1, and the power estimates of both signals are higher than the power detection threshold 2, where the power detection threshold 2 is less than the power detection threshold 1, then forwarding interference is considered to exist. The signal with the smaller pseudocode phase estimate is considered the target signal, and the result of successful target signal acquisition is output. If the 2-norm of the complex amplitude estimate of only one signal is higher than the power detection threshold 2, then the target signal is considered to be detected, and the result of successful target signal acquisition is output. If the 2-norm of the complex amplitude estimates of both signals is lower than the power detection threshold 2, then there is no interference, and the result of failed target signal acquisition is output.

[0145] In summary, the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An anti-replay interference acquisition system based on iterative least mean square error filtering, characterized in that, The global two-dimensional correlation and peak detection module, the single-path signal parameter estimation initialization module, the single-path signal iterative multi-parameter joint estimation module, the interference reconstruction and elimination module, the local two-dimensional correlation and peak detection module, the double-path signal parameter estimation initialization module, the double-path signal iterative multi-parameter joint estimation module, and the signal detection decision module are included. First, in the receiving channel, based on the phase search interval Δ T =T s and frequency search interval △ F The uncertainties in phase and frequency are divided into a two-dimensional search grid, with each grid denoted as a two-dimensional search unit, where T... s The sampling interval; The global two-dimensional correlation and peak detection module is configured to perform zero-padding matching filtering and non-coherent accumulation on the received signal in each phase-frequency two-dimensional search unit to obtain correlation values, and perform peak detection on the correlation values to obtain local correlation peaks, and record the pseudo-code phase value and the Doppler frequency value of the search unit where the local correlation peak is located as the pseudo-code phase coarse estimation value and the Doppler frequency coarse estimation value of the repeater jamming signal. The single-path signal parameter estimation initialization module is configured to set initial values of parameters to be estimated in the single-path signal iterative multi-parameter joint estimation process; set the complex amplitude, the phase offset and the frequency offset of the target signal to 0, set the phase offset and the frequency offset initial values of the repeater jamming signal to 0, and set the complex amplitude initial value of the repeater jamming signal according to the pseudo-code phase coarse estimation value and the Doppler frequency coarse estimation value of the repeater jamming signal. The single-path signal iterative multi-parameter joint estimation module is configured to perform iterative estimation of the phase offset, the frequency offset and the complex amplitude of the repeater jamming signal through the RMMSE filtering model after offset compensation. The interference reconstruction and elimination module is configured to reconstruct the interference signal according to the complex amplitude vector, the phase offset and the frequency offset estimation values of the repeater jamming signal estimated by the single-path signal iterative multi-parameter joint estimation module, eliminate the reconstructed interference signal from the received signal, and output the received signal after elimination of the interference signal to the local two-dimensional correlation and peak detection module. The local two-dimensional correlation and peak detection module is configured to perform local two-dimensional correlation in a range of two code chips on the left and right of the global correlation peak, perform peak detection, obtain a local two-dimensional correlation peak value, perform detection decision taking the detected local two-dimensional correlation peak value as a detection decision quantity, and regard the pseudo-code phase and the Doppler frequency corresponding to the local two-dimensional correlation peak value as the pseudo-code phase coarse estimation value and the Doppler frequency coarse estimation value of the target signal. The double-path signal parameter estimation initialization module is configured to set the complex amplitude, the phase offset and the frequency offset of the repeater jamming signal to the output of the single-path signal iterative multi-parameter joint estimation module; set the phase offset and the frequency offset initial values of the target signal to 0, and set the complex amplitude initial value of the target signal according to the pseudo-code phase coarse estimation value and the Doppler frequency coarse estimation value of the target signal. The double-path signal iterative multi-parameter joint estimation module is configured to perform iterative estimation of the phase offset, the frequency offset and the complex amplitude of the repeater jamming signal and the target signal simultaneously through the RMMSE filtering model after offset compensation, and output the pseudo-code phase and the complex amplitude estimation values of the two signals to the signal detection decision module. The signal detection decision module takes the 2-norm of the complex amplitude estimation value and the pseudo-code phase estimation value as a detection decision quantity to make a detection decision, and outputs a signal capture result.

2. The anti-replay interference acquisition system based on iterative least mean square error filtering of claim 1, wherein, The single-path signal iterative multi-parameter joint estimation module performs iterative estimation on the phase off-grid quantity, the frequency off-grid quantity and the complex amplitude of the signal, and comprises a complex amplitude estimation submodule, a signal cancellation submodule for off-grid quantity estimation, a phase off-grid quantity estimation submodule, a frequency off-grid quantity estimation submodule, a parameter estimation value correction and updating submodule and a convergence decision submodule; The complex amplitude estimation submodule is configured to perform complex amplitude estimation on the retransmission interference signal based on off-grid quantity compensated RMMSE filtering, and send the complex amplitude estimation value of the retransmission interference signal to the parameter estimation value correction and updating submodule. The signal cancellation submodule is configured to reconstruct the target signal, cancel the reconstructed target signal from the received signal, and output the operation result to the phase off-grid quantity estimation module and the frequency off-grid quantity estimation module. The phase off-grid quantity estimation submodule is configured to estimate the phase off-grid quantity of the retransmission interference signal, and send the phase off-grid quantity estimation value of the retransmission interference signal to the parameter estimation value correction and updating submodule. The frequency off-grid quantity estimation submodule is configured to estimate the frequency off-grid quantity of the retransmission interference signal, and send the frequency off-grid quantity estimation value of the retransmission interference signal to the parameter estimation value correction and updating submodule. The parameter estimation value correction and updating submodule is configured to update the complex amplitude estimation value, the phase off-grid quantity estimation value and the frequency off-grid quantity estimation value of the retransmission interference signal and output them to the convergence decision submodule. The convergence decision submodule judges whether the iteration of the multi-parameter joint estimation converges and performs logical control; if the difference between the parameter estimation values of the previous and next iterations is less than a convergence decision threshold, it is considered that the iteration converges, the amplitude estimation value obtained in the last iteration is taken as the final complex amplitude estimation value, the phase off-grid quantity estimation value obtained in the last iteration is taken as the final phase off-grid quantity estimation value, and the frequency off-grid quantity estimation value obtained in the last iteration is taken as the final frequency off-grid quantity estimation value, and the final complex amplitude estimation value, the final phase off-grid quantity estimation value and the final frequency off-grid quantity estimation value are output to the interference reconstruction and cancellation module. If the difference between the parameter estimation values of the previous and next iterations is not less than the convergence decision threshold, it is considered that the iteration does not converge, the iteration counter j is incremented by 1, and the parameter estimation results are fed back to the single-path signal iterative multi-parameter joint estimation module for the next iteration.

3. An anti-replay interference acquisition system based on iterative least mean square error filtering as described in claim 2, wherein, The signal detection decision module specifically comprises: If the 2-norm of the complex amplitude estimation value of one signal is higher than the set power detection threshold 1, if only the power estimation value of one signal is higher than the power detection threshold 1, it is determined that the signal is a retransmission interference signal, and the result of target signal capture failure is output; if the 2-norm of the complex amplitude estimation value of two signals is higher than the detection threshold, but the code phase estimation value of the signal with higher 2-norm of the complex amplitude estimation value of the two signals is smaller than the other signal, it is considered that the signal with higher 2-norm of the complex amplitude estimation value of the two signals is a retransmission interference, and the result of target signal capture failure is output; if the 2-norm of the complex amplitude estimation value of two signals is higher than the power detection threshold 1, and the code phase estimation value of the signal with higher 2-norm of the complex amplitude estimation value of the two signals is greater than the other small signal, the signal with smaller 2-norm of the complex amplitude estimation value of the two signals is regarded as the target signal, and the result of target signal capture success is output; If the 2-norm of the complex amplitude estimation value of two signals is smaller than the power detection threshold 1, if the power estimation value of two signals is higher than the power detection threshold 2, wherein the power detection threshold 2 is smaller than the power detection threshold 1, it is considered that there is retransmission interference, and the signal with smaller code phase estimation value of the two signals is regarded as the target signal, and the result of target signal capture success is output; if only the 2-norm of the complex amplitude estimation value of one signal is higher than the power detection threshold 2, it is considered that the detected is the target signal, and the result of target signal capture success is output; if the 2-norm of the complex amplitude estimation value of two signals is lower than the power detection threshold 2, it is considered that there is no interference, and the result of target signal capture failure is output.

Images