Efficient multi-subband anti-wideband jamming method for beidou navigation signal

Abstract

The application discloses a kind of efficient multi-subband anti-broadband suppression type interference method for Beidou navigation signal, the method includes: using 4 array antenna by radio frequency link and after AD conversion into FPGA module;FPGA module is preprocessed to each channel, down conversion and low pass filter shaping;Signal is divided into multiple subbands by SUB_DDC and SUB_FIR;The data of each subband is sent into preset matrix inversion module and is calculated, and autocorrelation matrix and cross-correlation vector are obtained;Inverse of autocorrelation matrix, cancellation coefficient based on matrix inversion is obtained by calculation to interfere cancellation;Multiple subband signals are recombined into a road by SUB_DUC and INT_FIR, and anti-interference processing is completed.Under the premise of guaranteeing anti-interference performance, logic resource overhead can be greatly reduced, and the efficiency of coefficient calculation can be significantly improved to ensure the real-time performance of anti-interference coefficient.

Description

Technical Field

[0001] This application relates to the field of BeiDou navigation technology, specifically to an efficient multi-subband anti-wideband suppression interference method for BeiDou navigation signals. Background Technology

[0002] The BeiDou Navigation Satellite System commonly employs CDMA (Code Division Multiple Access) technology, using a direct sequence spread spectrum scheme for information transmission, which provides a certain degree of anti-interference capability. However, due to the great distance between the satellite and the receiver and the low transmission power of the satellite signal, the received satellite signal power is very weak and is usually submerged in the receiver's thermal noise. When the receiver is subjected to various man-made or non-man-made radio frequency interferences, it cannot extract the satellite signal from the noise, leading to a decrease in positioning accuracy or even the inability to acquire the satellite.

[0003] Interference experienced by BeiDou satellite navigation receivers can be broadly categorized into two types: man-made interference and non-man-made interference. Man-made interference is further divided into suppression and deception interference; non-man-made interference mainly includes in-band radio frequency interference, out-of-band radio frequency interference, and environmental interference. Suppression interference transmits high-power interference signals that overlap with the satellite signal spectrum, suppressing the satellite signal at the receiver's front end, preventing the receiver from acquiring and tracking the satellite signal. It is typically classified into narrowband interference and wideband interference based on bandwidth. For narrowband interference, suppression is achieved through nulling in the frequency domain. However, this approach cannot be used for wideband suppression interference, as it would filter out useful signals as well. Therefore, appropriate anti-interference solutions are needed to address suppression interference.

[0004] Currently, most industry practices employ space-time two-dimensional anti-interference algorithms, the core of which is a space-time adaptive filtering algorithm. Adaptive beamforming algorithms require selecting a suitable signal as the desired signal based on specific circumstances, and then adjusting the system parameters according to a pre-defined optimization criterion to gradually bring the output signal closer to the desired signal. Since the number and direction of valid satellite signals received by a navigation receiver at a given time are uncertain, as are the number and form of external interference, providing a stable desired signal for the adaptive beamforming algorithm is quite difficult. Furthermore, satellite navigation signals reaching the ground are weak; if external interference occurs, a strong interference and weak signal reception environment will result. To address these issues, the power inversion adaptive algorithm is a suitable choice. This algorithm suppresses both the desired and interference signals at the array input, generating nulls in the beam pattern along the directions of both useful and interference signals. The stronger the signal, the deeper the corresponding null. If the interference is strong and the noise is weak, the null will align with the interference direction, significantly suppressing the interference. Therefore, this algorithm is well-suited for anti-interference processing in navigation receivers.

[0005] In traditional techniques, the LMS algorithm is an adaptive coefficient iterative update algorithm that consumes relatively few FPGA resources, but it is greatly affected by eigenvalues, and its convergence speed and post-convergence performance are bottlenecks. Currently, with the development of devices and the continuous optimization of matrix inversion methods, most current methods use matrix inversion to calculate the adaptive filter coefficients, which outperforms the LMS iterative algorithm.

[0006] However, matrix inversion is computationally intensive, and to ensure robust performance against interference, a large matrix size is required, which translates to significant resource overhead. Considering limited resources, some approaches use a time-for-resources tradeoff, but this approach's biggest drawback is the increased coefficient iteration time, making it unsuitable for rapidly changing external environments. Summary of the Invention

[0007] In order to solve the above-mentioned technical problems, this application proposes the following technical solution:

[0008] In a first aspect, embodiments of this application provide an efficient multi-subband anti-wideband suppression interference method for BeiDou navigation signals. The method includes: using a 4-element antenna, the signal is transmitted to an FPGA module via a radio frequency link and AD conversion; the FPGA module performs preprocessing, down-conversion, and low-pass filtering shaping on each channel; the signal is divided into multiple subbands using SUB_DDC and SUB_FIR; the data of each subband is fed into a preset matrix inversion module for calculation to obtain an autocorrelation matrix and a cross-correlation vector; the autocorrelation matrix is ​​inverted, and the cancellation coefficient is obtained based on matrix inversion for interference cancellation; the signals of multiple subbands are recombined into one channel using SUB_DUC and INT_FIR to complete the anti-interference processing.

[0009] By adopting the above implementation method, while ensuring anti-interference performance, the overhead of logic resources can be greatly reduced, and the efficiency of coefficient calculation can be significantly improved, ensuring the real-time performance of anti-interference coefficients.

[0010] In conjunction with the first aspect, in a first possible implementation of the first aspect, the inversion of the autocorrelation matrix includes: decomposing the autocorrelation matrix using an improved square root decomposition method to obtain its lower triangular matrix and diagonal matrix; then inverting the lower triangular matrix to obtain a first inverse matrix; and obtaining the inverse matrix of the autocorrelation matrix based on the first inverse matrix and the diagonal matrix.

[0011] In conjunction with the first possible implementation of the first aspect, in the second possible implementation of the first aspect, the four data streams of the 4-element antenna are divided into three auxiliary data streams and one main data stream. The three auxiliary data streams are delayed by 0-15 beats respectively, resulting in 48 auxiliary data streams. After autocorrelation, an autocorrelation matrix of size 48x48 is obtained. The autocorrelation matrix is ​​inverted using LDL decomposition, and combined with the cross-correlation vector, the corresponding adaptive filtering coefficients are calculated to obtain the interference estimation signal.

[0012] In combination with the first aspect or the first or second possible implementation of the first aspect, in the third possible implementation of the first aspect, the step of recombining the signals of multiple sub-bands into one channel through SUB_DUC and INT_FIR to complete the anti-interference processing includes: using multi-sub-band parallel processing, that is, using multiple sub-bands to divide the 20.46MHz frequency band into N parts; using a space-time filtering unit to filter each sub-band respectively; and then combining the multiple sub-band data after space-time filtering.

[0013] Combining the third possible implementation of the first aspect, in the fourth possible implementation of the first aspect, the matrix inversion module adopts a pipelined iterative mode, that is, there is no need to wait for the previous sub-band to be inverted before starting the relevant matrix calculation of the next sub-band, with the module as the pipeline unit.

[0014] In conjunction with the fourth possible implementation of the first aspect, in the fifth possible implementation of the first aspect, the results are stored in ping-pong mode within each matrix inversion module, and the matrix inversion modules complete handshake operations through busy and clr signals.

[0015] In conjunction with the fifth possible implementation of the first aspect, in the sixth possible implementation of the first aspect, when any matrix inversion module completes the operation and the RAM storing the result is not in a busy state, the operation of the next set of data can begin immediately as soon as a completion notification is received from the superior module; or, if it is in a busy state, the operation of the next set of data can begin only after the subordinate module has finished reading and cleared the busy signal. Attached Figure Description

[0016] Figure 1 A flowchart illustrating an efficient multi-subband anti-wideband suppression interference method for BeiDou navigation signals, provided as an embodiment of this application;

[0017] Figure 2 This is a schematic diagram illustrating the implementation principle of matrix inversion in an embodiment of this application. Detailed Implementation

[0018] The present solution will now be described in conjunction with the accompanying drawings and specific embodiments.

[0019] Figure 1 A flowchart illustrating an efficient multi-subband anti-wideband suppression interference method for BeiDou navigation signals, provided in an embodiment of this application, is shown below. Figure 1 The efficient multi-subband anti-wideband suppression interference method for BeiDou navigation signals in this embodiment includes:

[0020] The S101 uses a 4-element antenna, which enters the FPGA module through an RF link and AD conversion.

[0021] S102, the FPGA module performs preprocessing, downconversion, and low-pass filtering shaping on each channel.

[0022] S103 divides the signal into multiple sub-bands using SUB_DDC and SUB_FIR.

[0023] S104: The data from each sub-band is fed into the preset matrix inversion module for calculation to obtain the autocorrelation matrix and cross-correlation vector.

[0024] S105, Invert the autocorrelation matrix and use matrix inversion-based calculations to obtain cancellation coefficients for interference cancellation.

[0025] S106 uses SUB_DUC and INT_FIR to recombine the signals from multiple sub-bands into a single channel, thus completing the anti-interference processing.

[0026] Specifically, in this embodiment, the matrix inversion algorithm proceeds as follows: first, the covariance matrix A is decomposed using an improved square root decomposition method to obtain its lower triangular matrix L and diagonal matrix D. -1 Then, invert the lower triangular matrix L to obtain its inverse matrix P, and finally calculate P. H D -1 The product of P yields the inverse matrix A. -1 .

[0027] The autocorrelation matrix inversion is performed using an improved square root decomposition method. Compared to the traditional Cholesky decomposition, this method firstly eliminates the square root operation in the Decomposition module, and secondly, because the main diagonal of the decomposed L matrix is ​​all 1s, it also eliminates the need for division by the main diagonal elements compared to the traditional Cholesky decomposition. Overall, without increasing computational cost, it eliminates the square root operation and significantly reduces the amount of division, avoiding potential precision loss and making it easier to implement in hardware.

[0028] See Figure 2 In this embodiment, the four data streams from the four-element antenna are divided into three auxiliary data streams and one main data stream. The three auxiliary data streams are delayed by 0-15 beats, resulting in 48 auxiliary data streams. After autocorrelation, a [48x48] autocorrelation matrix is ​​obtained. Then, LDL decomposition is used to invert the matrix, and combined with the cross-correlation vector, the corresponding adaptive filtering coefficients are calculated to obtain the interference estimation signal. Finally, the interference estimation signal is subtracted from the main antenna signal to obtain the anti-interference IQ signal.

[0029] For each antenna element, the increased number of delay taps can form a time-domain filter, which can distinguish interference sources of different frequencies. In other words, the more taps, the higher the frequency resolution. However, the inherent characteristics of time-domain filtering also limit the filtering effect.

[0030] To improve anti-interference performance, this application adopts a multi-subband parallel processing approach. Specifically, it uses multiple subbands to divide the 20.46MHz frequency band into N parts, and uses a space-time filtering unit to filter each subband separately. The filtered data from the multiple subbands are then combined. This approach effectively improves the resolution of frequency domain filtering, better adapting to the mismatch introduced by frequency domain inconsistencies, thereby enhancing anti-interference performance. Simulation and actual test results show that this approach significantly improves anti-interference performance.

[0031] Therefore, based on the multi-subband broadband anti-interference scheme, the matrix inversion module adopts a pipelined iterative mode. This means that subbands do not need to wait for the previous subband's inversion to complete before starting the correlation matrix calculation for the next subband; instead, modules act as pipeline units. Each module stores results in a ping-pong pattern, and modules communicate with each other via busy and clr signals. When a module completes its calculation and the RAM storing the result is not in a busy state, it can immediately begin calculating the next set of data upon receiving a completion notification from the higher-level module. If it is in a busy state, it waits for the lower-level module to finish reading and clear the busy signal before starting the next set of data. This pipelined operation of the matrix inversion system significantly improves the efficiency of coefficient calculation.

[0032] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0033] Of course, the above description is not limited to the examples above. Technical features not described in this application can be implemented by or using existing technology, and will not be repeated here. The above embodiments and drawings are only used to illustrate the technical solutions of this application and are not intended to limit this application. If any substitution is required, this application has only been described in detail with reference to preferred embodiments. Those skilled in the art should understand that any changes, modifications, additions or substitutions made by those skilled in the art within the scope of this application do not depart from the spirit of this application and should also fall within the scope of protection of the claims of this application.

Claims

1. A highly efficient multi-subband anti-wideband suppression interference method for BeiDou navigation signals, characterized in that, The method includes: A four-element antenna is used, which enters the FPGA module through an RF link and AD conversion; The FPGA module performs preprocessing, downconversion, and low-pass filtering shaping on each channel; The signal is divided into multiple sub-bands using SUB_DDC and SUB_FIR; The data from each sub-band are fed into a preset matrix inversion module for calculation to obtain the autocorrelation matrix and cross-correlation vector; The autocorrelation matrix is ​​inverted, and the cancellation coefficients are obtained by matrix inversion calculation to cancel the interference. The inversion of the autocorrelation matrix includes: The autocorrelation matrix is ​​decomposed into its lower triangular matrix and diagonal matrix using an improved square root decomposition method. Then, invert the lower triangular matrix to obtain the first inverse matrix; The inverse matrix of the autocorrelation matrix is ​​obtained from the first inverse matrix and the diagonal matrix; The four data streams from the 4-element antenna are divided into three auxiliary data streams and one main data stream. The three auxiliary data streams are delayed by 0-15 beats respectively, resulting in 48 auxiliary data streams. After autocorrelation, a 48x48 autocorrelation matrix is ​​obtained. The autocorrelation matrix is ​​inverted using LDL decomposition and combined with the cross-correlation vector to calculate the corresponding adaptive filter coefficients and obtain the interference estimation signal. By using SUB_DUC and INT_FIR, signals from multiple sub-bands are recombined into a single channel to complete the anti-interference processing.

2. The efficient multi-subband anti-wideband suppression interference method for BeiDou navigation signals according to claim 1, characterized in that, The process of recombining signals from multiple sub-bands into a single signal using SUB_DUC and INT_FIR to achieve anti-interference processing includes: Multi-subband parallel processing is adopted, that is, multiple subbands are used to divide the 20.46MHz frequency band into N parts; Each sub-band is filtered separately using a space-time filtering unit. After filtering, the data from multiple sub-bands that have passed the space-time filter are combined.

3. The efficient multi-subband anti-wideband suppression interference method for BeiDou navigation signals according to claim 2, characterized in that, The matrix inversion module adopts a pipelined iterative mode, meaning that there is no need to wait for the previous sub-band to be inverted before starting the relevant matrix calculation of the next sub-band, with the module as the pipeline unit.

4. The efficient multi-subband anti-wideband suppression interference method for BeiDou navigation signals according to claim 3, characterized in that, Each matrix inversion module stores the results in a ping-pong mode, and the matrix inversion modules communicate with each other via the busy and clr signals.

5. The efficient multi-subband anti-wideband suppression interference method for BeiDou navigation signals according to claim 4, characterized in that, When any matrix inversion module completes its operation and the RAM storing the result is not in a busy state, it can immediately begin the operation on the next set of data as soon as it receives a completion notification from the upper-level module. or; If the system is in a busy state, it will wait for the next module to finish reading and clear the busy signal before starting the calculation of the next set of data.

Images