A processing method and device for resisting compression interference of multiple arrays

Abstract

The application discloses a processing method and device for resisting jamming of multiple elements, and comprises the following steps: a satellite receiver receives a satellite navigation signal, and performs digital debiasing processing on the satellite navigation signal; the signal after the digital debiasing processing is subjected to down-conversion processing; the signal after the down-conversion processing is subjected to anti-jamming signal processing; the signal after the anti-jamming signal processing is subjected to up-conversion processing; and the signal after the up-conversion processing is subjected to quantization processing, so as to obtain a satellite navigation useful signal after resisting jamming. The application realizes effective suppression of the same-frequency-band jamming in the satellite navigation signal and extraction of the useful signal.

Description

Technical Field

[0001] This invention relates to the field of signal processing, and in particular to a method and apparatus for processing multi-element anti-suppression interference. Background Technology

[0002] This section is intended to provide background or context for the embodiments of this application set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] The BeiDou Navigation Satellite System is a global satellite navigation system independently developed by China. Because the signal strength of its broadcast satellite navigation signals is relatively weak, it is susceptible to interference signals from other satellites on the same frequency band. Summary of the Invention

[0004] One objective of this application is to provide a method for processing multi-element anti-suppression interference. Another objective of this application is to provide a processing apparatus for processing multi-element anti-suppression interference.

[0005] To achieve the above objectives, this application discloses a method for processing multi-element anti-suppression interference, including: The satellite receiver receives satellite navigation signals and performs digital de-biasing processing on the satellite navigation signals, and then performs down-conversion processing on the signals after digital de-biasing processing. The signal after down-conversion is subjected to anti-suppression interference signal processing, and the signal after anti-suppression interference signal processing is subjected to up-conversion processing. The signal after up-conversion is quantized to obtain a satellite navigation useful signal that is resistant to suppression interference.

[0006] Optionally, the anti-suppression interference signal processing of the down-converted signal includes: The signal is processed using the minimum array output power as the optimization criterion to obtain a signal that is resistant to suppression interference.

[0007] Optionally, the signal processing based on minimizing array output power as the optimization criterion includes: Obtain the input vector and corresponding weight coefficients of the multi-element antenna array, and set constraints on the weight coefficients to obtain the constrained weight coefficient configuration result; Based on the constrained weight coefficient configuration results, the output power of the multi-element antenna array is calculated, and the corresponding Lagrange function is constructed. The gradient of the Lagrange function is calculated, and the optimal weighting vector and minimum output power are obtained based on the solution.

[0008] Optionally, obtaining the optimal weighting vector based on the solution results includes: An initial value is set for the optimal weighting vector, and the initial value is adaptively adjusted along the direction of decreasing output power so that the weighting vector gradually converges to the optimal weighting vector.

[0009] Optionally, the adaptive adjustment of the initial value along the direction of decreasing output power includes: Construct a recursive formula for the weighted vector, and adjust the recursive process by using an adjustable step size factor to make the recursive process satisfy the constraint conditions of the weight coefficients, thus obtaining a recursive formula that satisfies the constraints.

[0010] Optionally, the recurrence relation that satisfies the constraints includes: By replacing the corresponding parameters in the adaptive adjustment algorithm formula with the estimation formula of the least mean square algorithm, the recursive formula of the power inverted array is obtained.

[0011] Optionally, after obtaining the recurrence relation that satisfies the constraints, the method further includes: The weights are iteratively updated based on the recursive formula of the power inverted array. The radiation pattern of the multi-element antenna array is adjusted by the updated weights to form nulls in the direction of interference to suppress interference and output a signal after anti-suppression interference processing.

[0012] Another aspect of this application discloses a multi-element anti-suppression interference processing device, comprising: Down-conversion processing module: The satellite receiver receives satellite navigation signals and performs digital de-biasing processing on the satellite navigation signals, and then performs down-conversion processing on the signals after digital de-biasing processing; Up-conversion processing module: performs anti-suppression interference signal processing on the signal after down-conversion processing, and performs up-conversion processing on the signal after anti-suppression interference signal processing; Quantization processing module: Quantizes the signal after up-conversion to obtain a satellite navigation useful signal that is resistant to suppression interference.

[0013] The beneficial effects of this application are as follows: The multi-element anti-suppression interference processing method disclosed in this application eliminates DC bias and low-frequency spurious interference generated during the propagation and reception of satellite navigation signals in advance through digital de-biasing processing. Secondly, it converts high-frequency satellite navigation signals into signal frequency bands suitable for anti-suppression interference processing through down-conversion processing. This solves the problems of excessive hardware computation and poor real-time performance when high-frequency signals are directly processed for array anti-interference. It reduces the hardware implementation difficulty and computational cost of anti-suppression interference signal processing. The core anti-suppression interference signal processing is completed before up-conversion processing. Interference suppression is performed on the adapted frequency band after frequency conversion. Then, up-conversion processing is used to restore the signal frequency band. Finally, quantization processing converts the analog signal into a digital useful signal that can be recognized by the satellite navigation receiver. This achieves effective suppression of co-frequency suppression interference in satellite navigation signals and extraction of useful signals. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 The flowchart illustrates a multi-element anti-suppression interference processing method according to an embodiment of this application; Figure 2 This diagram shows a structural diagram of a multi-element anti-suppression interference processing device according to an embodiment of this application. Detailed Implementation

[0015] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. The word "and / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0016] It should be understood that the terms "first," "second," etc., in the specification, claims, and drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0017] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0018] In order to solve at least one of the problems existing in the prior art, according to one aspect of this application, such as Figure 1 As shown in the figure, this embodiment discloses a method for processing multi-element anti-suppression interference, the method including: S100: The satellite receiver receives satellite navigation signals and performs digital de-biasing processing on the satellite navigation signals, and performs down-conversion processing on the signals after digital de-biasing processing.

[0019] Specifically, satellite navigation signals propagating in space are captured by a satellite navigation receiving antenna. These signals contain useful signals from the satellite navigation system, but are also mixed with suppression interference signals in the same frequency band. Because the useful satellite navigation signal itself travels a long distance and has weak signal strength, the power of the suppression interference signal is much greater than the power of the useful signal, causing the useful signal to be submerged in the interference. If directly transmitted to the satellite navigation receiver, the receiver cannot identify and process the useful signal. During propagation and reception, satellite navigation signals develop a DC bias due to hardware circuit noise and the propagation environment. This DC bias interferes with subsequent frequency conversion and anti-interference processing. Therefore, digital filtering, DC component extraction, and elimination are used to remove the DC bias and low-frequency spurious noise from the signal, resulting in a DC-bias-free digital satellite navigation signal. Secondly, the original carrier frequency of the satellite navigation signal is relatively high. Directly performing anti-interference processing on the high-frequency signal would lead to excessive hardware computation and poor real-time performance. Therefore, mixing and filtering are used to convert the high-frequency satellite navigation signal into an intermediate-frequency signal.

[0020] It should be noted that, in this application, useful signals refer to navigation messages and carrier signals broadcast by satellite navigation systems such as BeiDou, which are used by receivers to achieve positioning, navigation and timing; correspondingly, suppressed interference signals refer to interference signals that are in the same frequency band as useful signals, have significantly higher power than useful signals, and cause the receiver to be unable to capture and track them normally.

[0021] S200: Performs anti-suppression interference signal processing on the down-conversion signal and up-conversion processing on the signal after anti-suppression interference signal processing.

[0022] Specifically, the process first uses an adaptive anti-interference algorithm to suppress the interference signal in the intermediate frequency signal, extracts the useful signal, and then uses up-conversion processing to restore the useful intermediate frequency signal to a high frequency signal.

[0023] S300: Quantizes the signal after up-conversion to obtain a satellite navigation useful signal that is resistant to suppression interference.

[0024] Specifically, the intermediate frequency (IF) useful signal, after anti-suppression interference processing, is transmitted to the frequency conversion unit of the up-conversion processing module. Through mixing and filtering, the IF signal is mixed with the local oscillator signal, filtering out harmonic components and spurious signals generated during mixing, thus restoring the IF useful signal to a high-frequency signal. The quantization processing module performs high-precision sampling of the received high-frequency useful signal. The sampling rate is set according to the frequency band characteristics of the satellite navigation signal and the signal requirements of the receiver, ensuring that the sampled signal completely retains all the characteristic information of the useful signal without signal distortion. The sampled analog signal is then quantized, converting the continuous analog signal into a discrete digital signal through layering and encoding. The number of quantization bits can be selected according to the signal accuracy requirements; the higher the number of quantization bits, the higher the signal accuracy and the better the detailed characteristics of the useful signal are preserved. The quantized digital signal undergoes format verification and conversion, transforming it into the standard signal format of the satellite navigation receiver. Finally, the anti-suppression interference useful satellite navigation signal is output, which can be directly recognized and processed by the satellite navigation receiver to achieve positioning, navigation, and timing functions.

[0025] In an optional implementation, the anti-suppression interference signal processing of the down-converted signal includes: S210: The signal is processed with the minimum array output power as the optimization criterion to obtain a signal after anti-suppression interference processing.

[0026] Specifically, with the minimum array output power as the optimization criterion, the suppression of interference signals is achieved based on a multi-element antenna array. It is not necessary to know the specific parameters of the useful signal and the interference signal in advance. By dynamically adjusting the weight coefficients of the multi-element antenna array, the antenna pattern forms a null in the direction of the interference, thereby suppressing the interference signal.

[0027] In an optional implementation, processing the signal based on minimizing array output power as the optimization criterion includes: S211: Obtain the input vector and corresponding weight coefficients of the multi-element antenna array, and set constraints for the weight coefficients to obtain the constrained weight coefficient configuration result.

[0028] Specifically, the input vector and corresponding weight coefficients of the multi-element antenna array are obtained, and constraints are set to obtain the constrained weight coefficient configuration result. The multi-element antenna array is an n-element antenna array, where n is a positive integer greater than 1. 4-element, 8-element, or 16-element arrays can be selected according to anti-interference requirements. Each element synchronously receives the intermediate frequency signal after down-conversion processing. The received signals of each element are used as array input components and integrated to form the input vector of the multi-element antenna array. This input vector contains all information about the useful signal received by each element and the suppressed interference signal. A corresponding weight coefficient is configured for the input component of each element. The weight coefficients of all elements constitute a weight coefficient vector. The value of the weight coefficient determines the weight ratio of the received signal of each element in the array output. By adjusting the weight coefficients, signal control can be achieved. To avoid meaningless all-zero solutions in the weight coefficient vector and to ensure the effectiveness of array signal processing, constraints are set for the weight coefficients to obtain the constrained weight coefficient configuration result. This constraint is a linear constraint, which simplifies the subsequent weight coefficient solution process while ensuring the validity of the solution result.

[0029] In this embodiment, the constraints on the weighting coefficients can be flexibly configured according to the array configuration of the multi-element antenna array, the requirements of anti-interference scenarios, and the hardware computing power conditions. These constraints include, but are not limited to, any one or more combinations of the following: reference element unity gain linear constraint, weighting coefficient vector magnitude constraint, array beamform constraint, and array gain constraint. Those skilled in the art can make the selection according to actual needs, and this application does not limit this selection.

[0030] S212: Based on the constrained weight coefficient configuration results, calculate the output power of the multi-element antenna array and construct the corresponding Lagrange function.

[0031] Specifically, based on the constrained weight coefficient configuration, and combined with the input vector of the multi-element antenna array, the output power of the multi-element antenna array is calculated. Array output power is a core indicator for evaluating the array's signal processing performance. This invention uses minimizing array output power as the optimization criterion because: the power of suppressed interference signals is much greater than the power of useful signals; the array output power is mainly contributed by interference signals, and minimizing the array output power achieves maximum suppression of interference signals. Meanwhile, the useful signal, due to its extremely low power proportion, has a negligible impact on the array output power. Since the weight coefficients are constrained, directly minimizing the output power would be limited by these constraints. Therefore, the Lagrange multiplier method is used to integrate the constraints of the weight coefficients into the process of minimizing the array output power, constructing a corresponding Lagrange function. This transforms the constrained extreme value problem into an unconstrained extreme value problem, simplifying the subsequent solution process.

[0032] S213: Solve for the gradient of the Lagrange function, and obtain the optimal weighting vector and minimum output power based on the solution results.

[0033] Specifically, the gradient of the constructed Lagrange function is calculated. The gradient of the weight coefficient vector in the Lagrange function is calculated according to the matrix differentiation rule. The gradient result is set to zero, and the gradient equation is solved to obtain the weight coefficient vector that minimizes the array output power, i.e., the optimal weighting vector. Substituting the optimal weighting vector into the formula for calculating the array output power, the minimum output power can be obtained. At this time, the array's suppression effect on the interference signal reaches the optimal level.

[0034] In an optional implementation, obtaining the optimal weighting vector based on the solution results includes: S310: Set an initial value for the optimal weighting vector, and adaptively adjust the initial value along the direction of decreasing output power so that the weighting vector gradually converges to the optimal weighting vector.

[0035] Calculating the optimal weighted vector directly from the gradient solution requires inverting the autocorrelation matrix of the array input vector. This operation is computationally intensive and complex, making it unsuitable for real-time processing of satellite navigation signals. Therefore, an adaptive adjustment method is used to iteratively approximate the optimal weighted vector: First, an initial value is set for the optimal weighted vector. This initial value can be flexibly set based on engineering experience and hardware circuit characteristics, without needing to strictly match the theoretical value of the optimal weighted vector. Then, this initial value is adaptively adjusted along the direction of decreasing array output power. Since the gradient direction is the direction of the fastest function value growth, the negative gradient direction is chosen as the direction of decreasing output power, ensuring that each adjustment brings the weighted vector closer to the direction that minimizes the array output power, eventually converging to the optimal weighted vector. Throughout the adaptive adjustment process, the weighted vector always satisfies the aforementioned weight coefficient constraints, ensuring that the adjusted weighted vector is a valid solution.

[0036] In an optional implementation, the adaptive adjustment of the initial value along the direction of decreasing output power includes: S320: Construct a recursive formula for the weighted vector, and adjust the recursive process by using an adjustable step size factor to make the recursive process satisfy the constraint conditions of the weight coefficients, thereby obtaining a recursive formula that satisfies the constraints.

[0037] To achieve adaptive adjustment of the weighted vector, a recursive formula for the weighted vector is constructed. The update amount of the weighted vector is correlated with the negative gradient of the array output power, and the convergence of the weighted vector from the initial value to the optimal weighted vector is achieved through recursive iteration. An adjustable step size factor is introduced into the recursive formula to adjust the update step size of the weighted vector: if the step size factor is too large, it will cause oscillation in the recursive process and fail to converge to the optimal weighted vector; if the step size factor is too small, the convergence speed will be too slow and cannot meet the requirements of real-time processing. Therefore, by dynamically adjusting the value of the adjustable step size factor, the recursive process can satisfy the aforementioned weight coefficient constraints and achieve fast and stable convergence, ultimately obtaining a recursive formula that satisfies the constraints.

[0038] In an optional implementation, obtaining the recurrence relation that satisfies the constraints includes: S330: The corresponding parameters in the adaptive adjustment algorithm formula are replaced by the estimation formula of the least mean square algorithm to obtain the recursive formula of the power inverted array.

[0039] Specifically, the constrained recursive formula includes parameters related to the autocorrelation matrix of the array input vector. Real-time calculation of the autocorrelation matrix requires a large amount of historical signal data, resulting in significant computational complexity and impacting real-time processing performance. Therefore, the least mean square algorithm is used to replace the autocorrelation matrix parameters in the recursive formula. This eliminates the need to calculate the statistically significant autocorrelation matrix; parameter estimation can be achieved using only the array input and output signals at the current moment, greatly simplifying the computation process and improving real-time performance. By replacing the least mean square algorithm estimation formula, the constrained recursive formula is optimized to obtain the recursive formula for the power inverted array. This recursive formula is the core mathematical model for weight iterative updates, adapting to the anti-interference principle of the power inverted array and enabling zero-dip control of interference directions.

[0040] In an optional implementation, after obtaining the recurrence relation that satisfies the constraints, the method further includes: S340: Based on the recursive update of the power inverted array, the radiation pattern of the multi-element antenna array is adjusted by the updated weights to form nulls in the direction of interference to suppress interference, and output the signal after anti-suppression interference processing.

[0041] Specifically, the weights are iteratively updated using a recursive formula based on a power-inverted array. The input signal of the multi-element antenna array at the current moment is substituted into the recursive formula to calculate the weight coefficient vector for the next moment, completing one weight update. The updated weight coefficient vector is then applied to the multi-element antenna array to weight the received signals of each element. The input signal for the next moment is then substituted into the recursive formula for the next update, and so on, achieving real-time, dynamic iterative updates of the weight coefficients. The process of weight iterative updates is also a process of dynamic adjustment of the antenna pattern of the multi-element antenna array: as the weight coefficients continuously converge, the antenna pattern forms nulls in the direction of the interference signal. The stronger the interference signal, the deeper the null, thus effectively suppressing the interference signal. Since the useful satellite navigation signal is submerged in noise and interference, its impact on the weight iterative updates is negligible, and the antenna pattern does not form nulls in the direction of the useful signal, ensuring that the useful signal passes through the array normally. After the above processing, the output signal of the multi-element antenna array is the intermediate frequency useful signal after anti-interference processing, and the processing flow of this sub-section ends.

[0042] Another aspect of this application discloses a multi-element anti-suppression interference processing device, such as... Figure 2 As shown, it includes: Down-conversion processing module 11: The satellite receiver receives the satellite navigation signal, performs digital de-biasing processing on the satellite navigation signal, and performs down-conversion processing on the signal after digital de-biasing processing.

[0043] Up-conversion processing module 12: performs anti-suppression interference signal processing on the signal after down-conversion processing, and performs up-conversion processing on the signal after anti-suppression interference signal processing.

[0044] Quantization processing module 13: Quantizes the signal after up-conversion to obtain a satellite navigation useful signal that is resistant to suppression interference.

[0045] Since the principle by which this device solves the problem is similar to the methods described above, the implementation of this device can be found in the implementation of the methods, and will not be repeated here.

[0046] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for processing multi-element anti-suppression interference, characterized in that, include: The satellite receiver receives satellite navigation signals and performs digital de-biasing processing on the satellite navigation signals, and then performs down-conversion processing on the signals after digital de-biasing processing. The signal after down-conversion is subjected to anti-suppression interference signal processing, and the signal after anti-suppression interference signal processing is subjected to up-conversion processing. The signal after up-conversion is quantized to obtain a satellite navigation useful signal that is resistant to suppression interference.

2. The multi-element anti-suppression interference processing method according to claim 1, characterized in that, The anti-suppression interference signal processing of the down-conversion signal includes: The signal is processed using the minimum array output power as the optimization criterion to obtain a signal that is resistant to suppression interference.

3. The multi-element anti-suppression interference processing method according to claim 2, characterized in that, The signal processing based on minimizing array output power as the optimization criterion includes: Obtain the input vector and corresponding weight coefficients of the multi-element antenna array, and set constraints on the weight coefficients to obtain the constrained weight coefficient configuration result; Based on the constrained weight coefficient configuration results, the output power of the multi-element antenna array is calculated, and the corresponding Lagrange function is constructed. The gradient of the Lagrange function is calculated, and the optimal weighting vector and minimum output power are obtained based on the solution.

4. The multi-element anti-suppression interference processing method according to claim 3, characterized in that, The process of obtaining the optimal weighting vector based on the solution results includes: An initial value is set for the optimal weighting vector, and the initial value is adaptively adjusted along the direction of decreasing output power so that the weighting vector gradually converges to the optimal weighting vector.

5. The multi-element anti-suppression interference processing method according to claim 4, characterized in that, The adaptive adjustment of the initial value along the direction of decreasing output power includes: Construct a recursive formula for the weighted vector, and adjust the recursive process by using an adjustable step size factor to make the recursive process satisfy the constraint conditions of the weight coefficients, thus obtaining a recursive formula that satisfies the constraints.

6. The multi-element anti-suppression interference processing method according to claim 5, characterized in that, The recursive formulas that satisfy the constraints include: By replacing the corresponding parameters in the adaptive adjustment algorithm formula with the estimation formula of the least mean square algorithm, the recursive formula of the power inverted array is obtained.

7. The multi-element anti-suppression interference processing method according to claim 6, characterized in that, After obtaining the recurrence relation that satisfies the constraints, the following is also included: The weights are iteratively updated based on the recursive formula of the power inverted array. The radiation pattern of the multi-element antenna array is adjusted by the updated weights to form nulls in the direction of interference to suppress interference and output a signal after anti-suppression interference processing.

8. A multi-element anti-suppression interference processing device, characterized in that, include: Down-conversion processing module: The satellite receiver receives satellite navigation signals and performs digital de-biasing processing on the satellite navigation signals, and then performs down-conversion processing on the signals after digital de-biasing processing; Up-conversion processing module: performs anti-suppression interference signal processing on the signal after down-conversion processing, and performs up-conversion processing on the signal after anti-suppression interference signal processing; Quantization processing module: Quantizes the signal after up-conversion to obtain a satellite navigation useful signal that is resistant to suppression interference.

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