A Method for Controlling Anti-Jamming Signals of a High-Gain Satellite Navigation Antenna

Abstract

The present invention relates to the technical field of satellite navigation anti-interference, and particularly to an anti-interference signal control method for a high-gain satellite navigation antenna. The method includes the following steps: obtaining the original signal data of the satellite navigation signal and performing digital sampling on the original signal data to generate local sine signal data; performing digital down-conversion processing on the local sine signal data to obtain baseband signal data, where the digital down-conversion processing includes local oscillator frequency setting and multiplying the sampling signal by the local oscillator signal; designing the parameters of a low-pass filter according to the baseband signal data and using the low-pass filter to perform high-frequency filtering on the baseband signal data to obtain DC component data; performing spectrum analysis on the DC component data and performing interference signal feature recognition to obtain interference feature data. The implementation method of the present invention is simple, does not increase the hardware cost when implemented digitally, is easy to implement in engineering, and has stronger platform adaptability.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite navigation anti-interference, and particularly to an anti-interference signal control method for a high-gain satellite navigation antenna. Background Art

[0002] A high-gain satellite navigation antenna is a high-gain antenna specifically designed to receive or transmit satellite navigation signals. Its main objective is to enhance the signal reception ability, especially in complex environments, improve the anti-interference performance, and ensure the high precision and stability of navigation signals. Gain refers to the ability of an antenna to receive or transmit signals, usually expressed in dBi. The design of a high-gain antenna can concentrate more signal energy in a specific direction, thereby achieving more efficient signal transmission between satellites and ground devices. This is very important for satellite navigation systems (such as GPS, Beidou, etc.), especially in weak signal environments, where high-gain antennas can effectively increase the signal strength. High-gain antennas improve the gain through directional radiation (rather than omnidirectional radiation) to ensure that the receiving antenna can receive clear and stable signals from the target satellite. When controlling the anti-interference signals of high-gain satellite navigation antennas, the following problems often exist: Traditional anti-interference methods may require complex hardware devices, increasing the system cost and complexity; Navigation applications usually require real-time processing, and traditional anti-interference methods may introduce significant delays. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide an anti-interference signal control method for a high-gain satellite navigation antenna to solve at least one of the above technical problems.

[0004] To achieve the above objective, an anti-interference signal control method for a high-gain satellite navigation antenna includes the following steps:

[0005] Step S1: Obtain the original signal data of the satellite navigation signal, perform digital sampling on the original signal data to generate local sine signal data; perform digital down-conversion processing on the local sine signal data to obtain baseband signal data, where the digital down-conversion processing includes local oscillator frequency setting and multiplication of the sampling signal and the local oscillator signal;

[0006] Step S2: Design the low-pass filter parameters according to the baseband signal data, and use the low-pass filter to perform high-frequency filtering on the baseband signal data to obtain DC component data; perform spectrum analysis on the DC component data and perform interference signal feature recognition to obtain interference feature data;

[0007] Step S3: Determine the anti-interference algorithm based on the interference feature data, and use the anti-interference algorithm to perform interference suppression processing on the DC component data, so as to obtain the interference suppression signal data; Compare the signal-to-noise ratio based on the DC component data and the interference suppression signal data, and evaluate the anti-interference effect, so as to obtain the anti-interference effect evaluation data;

[0008] Step S4: Optimize and adjust the anti-interference algorithm parameters according to the anti-interference effect evaluation data, and construct an adaptive anti-interference model; Use the adaptive anti-interference model to perform anti-interference processing on the interference suppression signal data to obtain the optimized signal data;

[0009] Step S5: Perform interpolation processing on the optimized signal data and remove the high-frequency components to obtain the high-sampling rate signal data; Use the local sine signal data to perform digital up-conversion processing on the high-sampling rate signal data to obtain the anti-interference output signal.

[0010] Through high-precision digital sampling and down-conversion processing, the present invention converts the original high-frequency satellite navigation signal into a baseband signal, making subsequent signal processing more effective. By setting the local oscillator frequency, the system can accurately lock onto the target frequency band and avoid interference from other frequencies. After transforming the high-frequency signal into a baseband signal, the burden on the subsequent processor is reduced, enabling anti-interference algorithms and other filtering operations to be carried out at a lower frequency, thereby reducing the occupation of system resources. The design and use of a low-pass filter can effectively remove high-frequency noise and unnecessary signal components, retaining the useful low-frequency signal part (DC component), thus providing a cleaner signal for subsequent interference analysis. By performing spectral analysis on the DC component, different types of interference signals (such as narrowband interference, broadband interference, pulse interference, etc.) can be accurately identified, providing a basis for the selection of subsequent anti-interference algorithms. According to the interference characteristic data, the system can select appropriate anti-interference algorithms (such as adaptive notch filtering, time-domain cancellation method, frequency-domain suppression method, etc.), thereby achieving optimized processing for different interference types; by suppressing the interference signal, the system can significantly improve the signal-to-noise ratio, ensuring the reliability and accuracy of the navigation signal; real-time evaluation of the anti-interference effect can ensure that the system maintains optimal performance in a changing interference environment and provides feedback for subsequent algorithm optimization. By evaluating the anti-interference effect, the system can automatically adjust the parameters of the anti-interference algorithm to ensure that the anti-interference processing is adaptively optimized with changes in the environment. This dynamic optimization improves the stability and anti-interference effect of the system; the construction of an adaptive anti-interference model enables the system to flexibly respond in various complex interference environments, reducing the need for manual intervention and achieving intelligent anti-interference processing. Through interpolation processing, the sampling rate of the signal can be increased, improving the signal detail recovery and quality, thereby enhancing the signal effect after anti-interference processing. The removal of high-frequency components further eliminates potential residual interference signals, ensuring the purity of the output signal. Through digital up-conversion processing, the signal is finally restored to an appropriate frequency range, generating an output signal with anti-interference capabilities, thereby ensuring that the navigation system can operate stably in an interference environment. This method provides an effective satellite navigation anti-interference solution through progressive signal processing and interference suppression. From the acquisition of the original signal, filtering, interference identification to adaptive optimization, each step enhances the anti-interference effect, ensuring the reliability and accuracy of the navigation signal in a complex interference environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments read with reference to the accompanying drawings:

[0012] Figure 1 It is a schematic flow chart of the steps of the anti-interference signal control method for a high-gain satellite navigation antenna of the present invention;

[0013] Figure 2 is Figure 1 a detailed step - by - step schematic diagram of step S1 in

[0014] Figure 3 is Figure 1 a detailed step - by - step schematic diagram of step S2 in Specific Embodiments

[0015] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0016] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0017] It should be understood that although terms such as "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed associated items.

[0018] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides an anti - interference signal control method for a large - gain satellite navigation antenna, and the method includes the following steps:

[0019] Step S1: Obtain the original signal data of the satellite navigation signal, perform digital sampling on the original signal data to generate local sine signal data; perform digital down - conversion processing on the local sine signal data to obtain baseband signal data, where the digital down - conversion processing includes local oscillator frequency setting and multiplication of the sampling signal and the local oscillator signal.

[0020] Step S2: Design the low-pass filter parameters based on the baseband signal data, and perform high-frequency filtering on the baseband signal data using the low-pass filter to obtain the DC component data; perform spectrum analysis on the DC component data and identify the interference signal characteristics to obtain the interference characteristic data;

[0021] Step S3: Determine the anti-interference algorithm based on the interference characteristic data, and perform interference suppression processing on the DC component data using the anti-interference algorithm to obtain the interference suppression signal data; compare the signal-to-noise ratio based on the DC component data and the interference suppression signal data, and evaluate the anti-interference effect to obtain the anti-interference effect evaluation data;

[0022] Step S4: Optimize and adjust the anti-interference algorithm parameters based on the anti-interference effect evaluation data, and construct an adaptive anti-interference model; perform anti-interference processing on the interference suppression signal data using the adaptive anti-interference model to obtain the optimized signal data;

[0023] Step S5: Perform interpolation processing on the optimized signal data and remove the high-frequency components to obtain the high-sampling-rate signal data; perform digital up-conversion processing on the high-sampling-rate signal data using the local sine signal data to obtain the anti-interference output signal.

[0024] In the embodiment of the present invention, refer to Figure 1 As described, it is a schematic diagram of the step flow of a method for controlling anti-interference signals of a large-gain satellite navigation antenna according to the present invention. In this example, the method for controlling anti-interference signals of the large-gain satellite navigation antenna includes the following steps:

[0025] Step S1: Obtain the original signal data of the satellite navigation signal, and perform digital sampling on the original signal data to generate local sine signal data; perform digital down-conversion processing on the local sine signal data to obtain the baseband signal data, where the digital down-conversion processing includes local oscillator frequency setting and multiplication of the sampling signal and the local oscillator signal;

[0026] In the embodiment of the present invention, the original RF data of the satellite navigation signal is first obtained through a high-sensitivity antenna. The RF signal is amplified by a preamplifier and then sent to a high-speed analog-to-digital converter for digital sampling. The sampling frequency is set to be higher than twice the highest frequency of the original signal. For example, for an L1-band satellite signal of 1.57542 GHz, the sampling frequency is set to 3.2 GHz. The sampled signal generates a local sine signal through a digital signal generator, with a frequency equal to the set local oscillator frequency (such as 1.57542 GHz), and then performs digital down-conversion operation on the local sine signal. The local sine signal and the sampling signal are subjected to in-phase and quadrature component multiplication calculations, and finally the baseband signal data is obtained. The baseband signal frequency range is reduced to a lower frequency band for subsequent processing.

[0027] Step S2: Design the low-pass filter parameters according to the baseband signal data, and use the low-pass filter to perform high-frequency filtering on the baseband signal data to obtain the DC component data; perform spectrum analysis on the DC component data and identify the interference signal characteristics to obtain the interference feature data;

[0028] In the embodiment of the present invention, first, the spectrum characteristics of the baseband signal are analyzed by fast Fourier transform to determine the signal bandwidth and the center frequency. Based on this, a low-pass filter is designed. The cut-off frequency of the filter is set to the navigation signal bandwidth (for example, 10 MHz), the transition bandwidth is set to 1 MHz, and the stopband attenuation is 40 dB. Then, the designed low-pass filter is used to filter the baseband signal to remove the high-frequency components and retain the DC component to obtain the DC component data. Next, short-time Fourier transform analysis is performed on the DC component data to obtain the time-frequency characteristics of the signal and identify the interference signal characteristics therein. For example, broadband interference is detected in a certain frequency band, and its frequency range is from 1.5754 GHz to 1.5764 GHz, and the interference intensity reaches -60 dBm.

[0029] Step S3: Determine the anti-interference algorithm according to the interference feature data, and use the anti-interference algorithm to perform interference suppression processing on the DC component data to obtain the interference suppression signal data; compare the signal-to-noise ratio based on the DC component data and the interference suppression signal data, and evaluate the anti-interference effect to obtain the anti-interference effect evaluation data;

[0030] In the embodiment of the present invention, the interference type is first identified as broadband interference, and the frequency domain suppression method is used for interference suppression. The specific operation is as follows: Apply an adaptive notch filter to the DC component data, set the center frequency of the notch filter to 1.5759 GHz, the bandwidth to 1 MHz, and automatically adjust the parameters of the filter to adapt to the frequency drift, so as to effectively suppress the interference signal and obtain the interference suppression signal data. Then, calculate the signal-to-noise ratio of the DC component data and the interference suppression signal data, and it is found that the signal-to-noise ratio has increased by about 10 dB after suppression. Finally, evaluate the anti-interference effect, and through comprehensive analysis of the signal-to-noise ratio improvement and the correlation peak of the navigation signal, obtain the anti-interference effect evaluation data, indicating that the quality of the navigation signal has been greatly improved after anti-interference processing.

[0031] Step S4: Optimize and adjust the anti-interference algorithm parameters according to the anti-interference effect evaluation data, and construct an adaptive anti-interference model; use the adaptive anti-interference model to perform anti-interference processing on the interference suppression signal data to obtain the optimized signal data;

[0032] In the embodiments of the present invention, according to the anti-interference effect evaluation data, the anti-interference algorithm parameters are optimized, such as adjusting the bandwidth of the notch filter and the automatic tracking rate of the center frequency, so as to improve the effect of suppressing broadband interference. Using these optimized parameters, an adaptive anti-interference model is constructed, and the adaptive model can dynamically adjust the anti-interference algorithm parameters according to different interference types and signal characteristics. During operation, the system automatically calls the adaptive anti-interference model to perform secondary processing on the interference suppression signal data, further improving the anti-interference effect, and finally obtaining the optimized signal data. At this time, the signal-to-noise ratio is further increased to 15 dB, and the bit error rate of the navigation signal is reduced to an acceptable range.

[0033] Step S5: Perform interpolation processing on the optimized signal data and remove high-frequency components to obtain high-sampling-rate signal data; perform digital up-conversion processing on the high-sampling-rate signal data using local sine signal data to obtain the anti-interference output signal.

[0034] In the embodiments of the present invention, first, multi-phase interpolation processing is performed on the optimized signal data, and the interpolation multiple is set to 4 times to improve the sampling rate of the signal. Then, the interpolated signal is reconstructed using a fractional delay filter to remove the remaining high-frequency interference components and obtain high-sampling-rate signal data. Next, the generated local sine signal is accurately synchronized with the high-sampling-rate signal data using phase-locked loop technology to ensure that their phases are consistent. Finally, the high-sampling-rate signal is digitally up-converted through quadrature modulation, and the output frequency is the same as that of the original navigation signal, and finally the anti-interference output signal is obtained. Tests show that the signal-to-noise ratio of the anti-interference output signal is increased by 15 dB, fully meeting the requirements of the actual satellite navigation system.

[0035] Through high-precision digital sampling and down-conversion processing, the present invention converts the original high-frequency satellite navigation signal into a baseband signal, making subsequent signal processing more effective. By setting the local oscillator frequency, the system can accurately lock onto the target frequency band and avoid interference from other frequencies. After transforming the high-frequency signal into a baseband signal, the burden on the subsequent processor is reduced, enabling anti-interference algorithms and other filtering operations to be carried out at a lower frequency, thereby reducing the occupation of system resources. The design and use of a low-pass filter can effectively remove high-frequency noise and unnecessary signal components, retaining the useful low-frequency signal part (DC component), thus providing a cleaner signal for subsequent interference analysis. By performing spectral analysis on the DC component, different types of interference signals (such as narrowband interference, broadband interference, pulse interference, etc.) can be accurately identified, providing a basis for the selection of subsequent anti-interference algorithms. According to the interference characteristic data, the system can select appropriate anti-interference algorithms (such as adaptive notch filtering, time-domain cancellation method, frequency-domain suppression method, etc.), thereby achieving optimized processing for different interference types; by suppressing the interference signal, the system can significantly improve the signal-to-noise ratio, ensuring the reliability and accuracy of the navigation signal; real-time evaluation of the anti-interference effect can ensure that the system maintains optimal performance in a changing interference environment and provides feedback for subsequent algorithm optimization. Through the evaluation of the anti-interference effect, the system can automatically adjust the parameters of the anti-interference algorithm to ensure that the anti-interference processing is adaptively optimized as the environment changes. This dynamic optimization improves the stability and anti-interference effect of the system; the construction of an adaptive anti-interference model enables the system to flexibly respond to various complex interference environments, reducing the need for manual intervention and achieving intelligent anti-interference processing. Through interpolation processing, the sampling rate of the signal can be increased, improving the signal detail recovery and quality, thereby enhancing the signal effect after anti-interference processing. The removal of high-frequency components further eliminates potential residual interference signals, ensuring the purity of the output signal. Through digital up-conversion processing, the signal is finally restored to an appropriate frequency range to generate an output signal with anti-interference ability, thus ensuring that the navigation system can continuously and stably operate in an interference environment. This method provides an effective satellite navigation anti-interference solution through progressive signal processing and interference suppression. From the acquisition of the original signal, filtering, interference identification to adaptive optimization, each step enhances the anti-interference effect, ensuring the reliability and accuracy of the navigation signal in a complex interference environment.

[0036] Preferably, step S1 includes the following steps:

[0037] Step S11: Obtain the original satellite navigation radio frequency signal; amplify the original satellite navigation radio frequency signal using a preamplifier with high linearity to obtain the original signal data;

[0038] Step S12: Perform system clock synchronization on the original signal data and add an accurate timestamp to obtain signal time-synchronized data;

[0039] Step S13: Perform analog-to-digital conversion processing on the signal time-synchronized data based on a high-speed analog-to-digital converter and perform digital sampling to obtain digital sampled signal data, where the sampling frequency of the digital sampling is greater than twice the highest frequency in the original signal data;

[0040] Step S14: Extract key frequency components from the digital sampled signal data through a digital signal generator to generate local sine signal data synchronized with the original signal data;

[0041] Step S15: Perform digital down-conversion processing on the local sine signal data to obtain baseband signal data, where the digital down-conversion processing includes local oscillator frequency setting and multiplication of the sampled signal and the local oscillator signal.

[0042] As an embodiment of the present invention, referring to Figure 2 shown, for Figure 1 the detailed step flow diagram of step S1 in

[0043] Step S11: Obtain the original satellite navigation radio frequency signal; amplify the original satellite navigation radio frequency signal using a preamplifier with high linearity to obtain the original signal data;

[0044] In the embodiment of the present invention, the original satellite navigation radio frequency signal is first received by a high-gain satellite antenna, and the signal frequency is 1.57542 GHz (L1 band). To ensure the stability of the signal quality in the subsequent processing, a preamplifier with high linearity is used to amplify the radio frequency signal. The gain of the preamplifier is set to 35 dB to ensure that the signal remains within the linear range after amplification and does not distort. The amplified signal is the original signal data, and the signal power is increased to a level suitable for subsequent processing, usually between -90 dBm and -50 dBm.

[0045] Step S12: Perform system clock synchronization on the original signal data and add an accurate timestamp to obtain signal time-synchronized data;

[0046] In the embodiment of the present invention, for the original signal data, system clock synchronization is first performed to ensure that the time reference of the signal remains consistent in subsequent steps. A high-precision temperature-compensated crystal oscillator (TCXO) is used as the reference clock source, and its accuracy is within ±0.5 ppm. Then, an accurate timestamp is added to the signal data. The accuracy of the timestamp reaches the nanosecond level (ns). Time synchronization is performed through the GPS timing system to ensure that the timestamp is synchronized with the Coordinated Universal Time (UTC), generating signal time synchronization data. This step ensures the timing consistency and accuracy in subsequent signal processing, which is particularly crucial in a multi-antenna system.

[0047] Step S13: Perform analog-to-digital conversion processing on the signal time synchronization data based on a high-speed analog-to-digital converter and perform digital sampling to obtain digital sampled signal data, where the sampling frequency of the digital sampling is greater than twice the highest frequency in the original signal data;

[0048] In the embodiment of the present invention, the signal time synchronization data is input into a high-speed analog-to-digital converter (ADC) for analog-to-digital conversion processing. To ensure that the signal is not distorted, the sampling frequency of the analog-to-digital conversion is set to more than twice the highest frequency of the original signal. For example, for a navigation signal of 1.57542 GHz, the sampling frequency is set to 3.2 GHz. The resolution of the analog-to-digital converter is set to 12 bits to ensure sufficient quantization accuracy. The converted signal is digital sampled signal data. The bandwidth of this digital sampled signal data is 16 MHz, which can contain all frequency components of the satellite navigation signal, ensuring the accuracy of subsequent processing.

[0049] Step S14: Extract the key frequency components from the digital sampled signal data through a digital signal generator to generate local sine signal data synchronized with the original signal data;

[0050] In the embodiment of the present invention, a high-performance digital signal generator is used to extract the key frequency components from the digital sampled signal data. The extracted key frequency components are synchronized with the center frequency of the original signal, which is 1.57542 GHz. The output frequency set by the digital signal generator is the same as the local oscillator frequency, and at the same time, it is ensured that the generated local sine signal is in phase with the original signal. The frequency accuracy of this local sine signal is controlled within 0.1 ppm, and the distortion of the output signal is less than 0.01%. Finally, local sine signal data synchronized with the original signal data is generated.

[0051] Step S15: Perform digital down-conversion processing on the local sine signal data to obtain baseband signal data, where the digital down-conversion processing includes setting the local oscillator frequency and multiplying the sampled signal by the local oscillator signal.

[0052] In the embodiment of the present invention, digital down-conversion processing is performed on local sine signal data. First, the local oscillator frequency is set to be the same as the center frequency of the received satellite navigation signal, which is 1.57542 GHz. A local oscillator signal is generated using a digital frequency synthesizer and is divided into an in-phase component (I channel) and a quadrature component (Q channel). The local sine signal data is multiplied by the in-phase and quadrature component signals respectively to generate an in-phase component signal and a quadrature component signal. Then, digital low-pass filtering processing is performed on the in-phase and quadrature component signals respectively, and the cut-off frequency of the filter is set to 8 MHz to remove high-frequency noise and interference, and finally baseband signal data is obtained. The frequency range of the baseband signal is reduced to 0 - 8 MHz, which is convenient for subsequent processing and demodulation.

[0053] The satellite navigation signals of the present invention are usually very weak, especially when far from the satellite or limited by the antenna gain. Using a preamplifier with high linearity can effectively amplify these weak signals to achieve the signal-to-noise ratio required for subsequent processing. The high-linearity preamplifier can amplify the signal without introducing significant nonlinear distortion. This is crucial for maintaining the original characteristics and accuracy of the signal, especially in a complex interference environment, avoiding additional interference or signal distortion introduced by the amplifier's nonlinearity. The high-linearity preamplifier is usually accompanied by a low noise figure, which can minimize the noise introduced by itself while amplifying the signal; this helps to improve the overall system signal-to-noise ratio (SNR) and provides a clearer signal basis for subsequent signal processing and anti-interference. Through the adjustable gain characteristic of the preamplifier, the system can adapt to satellite navigation signals of different intensities, ensuring effective signal capture and processing in various operating environments. System clock synchronization ensures that all signal processing modules operate on the same time basis, avoiding signal processing errors caused by clock deviation, which is particularly important for real-time anti-interference processing and data analysis. Precise timestamps allow each signal sample to be accurately corresponding to its capture time, facilitating subsequent signal analysis, fault troubleshooting, and performance evaluation, which is particularly crucial in a dynamic interference environment and can help identify and locate interference sources. In a system that requires multi-antenna or multi-receiver collaborative processing, clock synchronization and timestamps ensure that the data of each receiving unit can be accurately aligned, enabling more efficient signal fusion and anti-interference processing. Many anti-interference algorithms rely on the time-domain characteristics of the signal. Clock synchronization and timestamps ensure that these algorithms can be applied within an accurate time frame, enhancing their interference suppression effect. The sampling frequency is greater than twice the highest signal frequency (i.e., the Nyquist frequency), ensuring distortion-free sampling of the signal and avoiding the occurrence of aliasing, which is crucial for the accuracy of subsequent digital signal processing and anti-interference algorithms. A higher sampling frequency provides more sampling points, helping to more precisely capture the details and transient characteristics of the signal, improving the accuracy and effect of signal processing. Based on the analog-to-digital conversion processing ability of a high-speed analog-to-digital converter (ADC), the system can real-time process high-speed changing satellite navigation signals and interference signals, ensuring the real-time and effectiveness of anti-interference processing. A higher sampling rate allows the system to more finely identify and analyze the spectral and time-domain characteristics of interference signals, enhancing the accuracy of interference identification and suppression. The digital signal generator can accurately extract and generate a local sine signal synchronized with the key frequency components of the original signal, ensuring frequency matching during the down-conversion process and avoiding signal aliasing or frequency offset. By extracting the key frequency components, the data generation of the local sine signal can focus on the target signal, reducing the processing of unnecessary frequency components and improving the efficiency of subsequent signal processing and anti-interference algorithms.Generate a local sine signal synchronized with the original signal data to ensure accurate alignment of the signal phase and frequency during downconversion and baseband signal generation, improving the accuracy and effectiveness of the anti-interference algorithm. The digital signal generator can flexibly configure the frequency components of the local sine signal according to different satellite navigation frequency bands to adapt to the complex environment of multi-frequency bands and multi-signal sources, enhancing the adaptability and diversity of the system. Through digital downconversion, the high-frequency radio frequency signal is converted into a baseband signal, enabling subsequent digital signal processing (such as filtering, spectrum analysis, anti-interference algorithm) to be carried out within a lower frequency range, improving the processing efficiency and accuracy. The local oscillator frequency can be dynamically adjusted according to the specific signal environment and interference situation to achieve flexible downconversion of signals in different frequency bands, enhancing the adaptability and anti-interference ability of the system. The baseband signal has a lower bandwidth and complexity compared to the high-frequency radio frequency signal, simplifying the subsequent digital signal processing steps and reducing the computational burden and resource consumption of the system. During downconversion, the setting of the local oscillator frequency and the multiplication operation of the sampling signal and the local oscillator signal help suppress the signal components in non-target frequency bands, reduce the influence of interference signals, and improve the quality and purity of the baseband signal. During the digital downconversion process, the synchronous generation and multiplication operation of the local sine signal ensure phase alignment, providing favorable conditions for subsequent coherent demodulation and signal recovery, and enhancing the accuracy and stability of signal recovery.

[0054] Preferably, step S15 includes the following steps:

[0055] Step S151: Set the local oscillator frequency according to the center frequency of the original signal data, and generate a digital local oscillator signal with the set local oscillator frequency by using a digital frequency synthesizer, where the digital local oscillator signal includes in-phase and quadrature components;

[0056] In the embodiment of the present invention, according to the original signal data received from the satellite navigation system, its center frequency is 1.57542 GHz (L1 band). In order to convert the high-frequency signal into an easily processed baseband signal, digital downconversion processing needs to be achieved through local oscillator frequency setting. A local oscillator signal is generated by using a digital frequency synthesizer, and the local oscillator frequency is set to be the same as the center frequency of the original signal, that is, 1.57542 GHz. The generated local oscillator signal is divided into an in-phase component (I component) and a quadrature component (Q component). The digital frequency synthesizer adopts phase synchronization technology to ensure the phase accuracy of the in-phase and quadrature component signals. The phase difference between the in-phase signal and the local oscillator signal is 0 degrees, and the phase difference of the quadrature signal is 90 degrees. The frequency accuracy of the frequency synthesizer is set to 0.01 ppm to ensure the stability of the downconversion process.

[0057] Step S152: Multiply the local sine signal data by the in-phase and quadrature components of the digital local oscillator signal respectively to obtain an in-phase component signal and a quadrature component signal;

[0058] In the embodiments of the present invention, the local sine signal data is multiplied by the in-phase component and the quadrature component of the digital local oscillator signal respectively. In the specific implementation process, two sets of multipliers are used to process the in-phase component and the quadrature component respectively. After sampling the local sine signal data, the value of each sampling point is multiplied by the in-phase signal and the quadrature signal respectively to obtain the in-phase component signal (I-channel signal) and the quadrature component signal (Q-channel signal). This step decomposes the original high-frequency signal into two low-frequency components, corresponding to the amplitude and phase information of the signal respectively, which is convenient for subsequent filtering and signal demodulation.

[0059] Step S153: Perform digital low-pass filtering on the in-phase component signal and the quadrature component signal to obtain the in-phase channel and quadrature channel signals;

[0060] In the embodiments of the present invention, digital low-pass filtering is performed on the in-phase component signal and the quadrature component signal. To ensure that the filter can effectively remove high-frequency interference, the digital low-pass filter is designed based on the FIR (finite impulse response) filter structure, and the cut-off frequency is set to 8 MHz, slightly higher than the signal bandwidth to avoid excessive signal loss. The order of the FIR filter is set to 64, which can effectively smooth the signal and reduce high-frequency noise. During the filtering process, the I-channel and Q-channel signals are processed independently, and finally the filtered in-phase channel and quadrature channel signals are output. These signals are low-frequency baseband signals, and the signal frequency is shifted to near 0 Hz, which is convenient for subsequent signal processing.

[0061] Step S154: Calculate the amplitude and phase information of the signal according to the in-phase channel and quadrature channel signals to form baseband signal data in complex form.

[0062] In the embodiments of the present invention, the amplitude and phase information of the signal is calculated using the method of complex number calculation according to the in-phase channel (I-channel) and quadrature channel (Q-channel) signals. In the specific operation process, the I-channel signal is used as the real part, and the Q-channel signal is used as the imaginary part to synthesize a baseband signal in complex form. The amplitude of the signal is obtained by calculating the square root of the sum of the squares of the I-channel and Q-channel signals. The formula is: amplitude = √(I 2 + Q2). The phase information is obtained by calculating the arctangent value (atan2), and the phase = atan2(Q, I). The baseband signal data in complex form obtained by this operation contains complete amplitude and phase information, which can be used for subsequent demodulation or interference analysis processing.

[0063] The present invention sets the local oscillator frequency according to the center frequency of the original signal, which can ensure the precise matching of the local oscillator signal with the target frequency band. This avoids frequency offset or error and ensures the accuracy after the signal is down-converted. Using a digital frequency synthesizer can flexibly generate local oscillator signals of different frequencies, adapting to the frequency band requirements of various satellite navigation systems (such as GPS, Beidou, GLONASS, etc.), and improving the compatibility of the system. The local oscillator signal is divided into in-phase (I) and quadrature (Q) components. This design enables the system to completely capture the phase information of the signal, not just the amplitude of the signal. Phase information is crucial for navigation positioning and anti-interference, and can improve the anti-interference ability and signal recovery ability of the system. Multiplying the local sine signal data with the in-phase component (I) and the quadrature component (Q) of the digital local oscillator signal respectively can separate the in-phase and quadrature information in the high-frequency signal. In this way, the amplitude and phase of the signal can be analyzed and processed separately in subsequent processing, improving the signal demodulation accuracy. By multiplying the local sine signal with the in-phase and quadrature components respectively, it can ensure the complete retention of the amplitude and phase information of the signal. This is crucial for coherent demodulation and can accurately recover the original navigation signal. The I / Q component separation enables the system to more accurately identify and analyze the characteristics of interference signals, so as to better apply anti-interference algorithms and significantly improve the stability of the system in an interference environment. Digital low-pass filtering is performed on the in-phase (I) and quadrature (Q) components respectively, which can effectively remove high-frequency noise and interference signals and retain the low-frequency components of the baseband signal. This can not only improve the clarity of the signal, but also reduce the interference error in subsequent processing. The application of the low-pass filter can smooth the signal, reduce transient spikes and unnecessary high-frequency components, and ensure the smoothness and stability of the in-phase path and quadrature path signals. This can improve the signal decoding and processing performance of the entire navigation system. The digital low-pass filter can dynamically adjust the filtering parameters according to the noise and interference characteristics in actual applications, further enhancing the adaptability and signal processing ability of the system in a complex interference environment. By calculating the amplitude and phase information of the in-phase path and quadrature path signals to generate complex-form baseband signal data, the instantaneous amplitude and phase of the signal can be accurately represented. This can ensure the integrity of the signal and provide more dimensional information for subsequent demodulation, signal analysis, and anti-interference processing. The complex-form baseband signal contains not only amplitude information but also phase information, which is crucial for coherent demodulation. The demodulation algorithm based on the complex baseband signal can more efficiently recover the original navigation data, reduce the bit error rate, and improve the navigation accuracy of the system. By simultaneously processing the amplitude and phase information of the signal, the system can more accurately identify and suppress phase interference or amplitude interference, enhancing the overall anti-interference ability. The complex baseband signal can be used for various subsequent processing, including spectrum analysis, modulation and demodulation, positioning calculation, etc. This data representation form has strong versatility and flexibility and is suitable for different application scenarios and signal processing algorithms.

[0064] Preferably, step S2 includes the following steps:

[0065] Step S21: Conduct a preliminary analysis on the baseband signal data to determine the bandwidth and main frequency components of the signal, thereby obtaining signal spectrum feature data;

[0066] Step S22: Design the parameters of the pass filter based on the signal spectrum feature data according to the cut-off frequency, transition band width, and stop band attenuation, thereby obtaining filter design parameters;

[0067] Step S23: Perform high-frequency filtering on the baseband signal data based on the filter design parameters, thereby obtaining DC component data;

[0068] Step S24: Conduct short-time Fourier transform analysis on the DC component data, thereby obtaining time-frequency feature data;

[0069] Step S25: Identify the interference signal features based on the time-frequency feature data, thereby obtaining interference feature data, where the interference signal identification includes narrowband interference identification, broadband interference identification, pulse interference identification, and swept-frequency interference identification.

[0070] As an embodiment of the present invention, referring to Figure 3 as shown, for Figure 1 the detailed step flow diagram of step S2 in

[0071] Step S21: Conduct a preliminary analysis on the baseband signal data to determine the bandwidth and main frequency components of the signal, thereby obtaining signal spectrum feature data;

[0072] In the embodiment of the present invention, the spectrum analysis of the baseband signal data is first performed, and the fast Fourier transform (FFT) is used to determine the bandwidth and main frequency components of the signal. The specific operation is to segment the baseband signal data and perform FFT operations on each segment of the signal to obtain the spectrum diagram of the signal. Through the spectrum diagram, the energy distribution of the signal in the frequency domain can be observed, and the bandwidth and main frequency components of the signal can be determined. For example, during the analysis process, it is found that the spectrum of the signal is concentrated in the range of 0 to 5 MHz, the bandwidth is about 5 MHz, and the main frequency component appears at about 2 MHz. At this time, the spectrum feature data of the signal can be recorded as the bandwidth, center frequency, and morphological features of the spectrum, which is convenient for subsequent filter design and interference feature extraction.

[0073] Step S22: Design the parameters of the pass filter based on the signal spectrum feature data according to the cut-off frequency, transition band width, and stop band attenuation, thereby obtaining filter design parameters;

[0074] In an embodiment of the present invention, a low-pass filter suitable for the baseband signal is designed according to the signal spectrum characteristic data. When designing the filter, the bandwidth, center frequency of the signal, and the high-frequency components to be filtered out are relied on. Here, based on a signal bandwidth of 5 MHz, the cut-off frequency of the filter is set to 5 MHz, the transition bandwidth is set to 0.5 MHz, and the stopband attenuation requirement is 60 dB. Specifically, the window function method is used to design the FIR filter, and the Hanning window is selected as the window function to ensure a good frequency-domain response. The order of the filter is set to 128 according to the design requirements to ensure sufficient frequency selectivity. The output of this step is the design parameters of the filter, including key parameters such as filter coefficients, order, and transition band, which are used for subsequent filtering processing.

[0075] Step S23: Perform high-frequency filtering on the baseband signal data based on the filter design parameters to obtain DC component data;

[0076] In an embodiment of the present invention, the filter parameters are used to perform high-frequency filtering on the baseband signal data. During specific operation, the FIR filter is used to perform convolution calculation on the baseband signal data point by point, filtering out the high-frequency components in the baseband signal and retaining the low-frequency DC component. The filtered signal mainly contains the DC component, with a frequency near 0 Hz, and the high-frequency noise and interference components of the signal are effectively suppressed. The output of this step is the filtered DC component data, which is mainly used for subsequent interference analysis and feature extraction.

[0077] Step S24: Perform short-time Fourier transform analysis on the DC component data to obtain time-frequency characteristic data;

[0078] In an embodiment of the present invention, short-time Fourier transform (STFT) analysis is performed on the DC component data to obtain the time-frequency characteristic data of the signal. During specific operation, the DC component signal is divided into multiple short time periods (usually dozens of milliseconds), and Fourier transform is performed on each period to obtain the spectrum change of the signal at each moment. The window length of the short-time Fourier transform is set to 64 sampling points, and the step size is set to 32 points to ensure the balance of time-frequency resolution. Through STFT analysis, a time-frequency diagram of the signal can be obtained, where the horizontal axis is time, the vertical axis is frequency, and the color represents the energy intensity of the signal. This time-frequency characteristic data contains the spectrum change information of the signal at different time points, providing a key basis for subsequent interference feature recognition.

[0079] Step S25: Identify the interference signal characteristics according to the time-frequency characteristic data to obtain interference characteristic data, where the interference signal identification includes narrowband interference identification, broadband interference identification, pulse interference identification, and swept-frequency interference identification.

[0080] In an embodiment of the present invention, interference signal feature recognition is performed based on time-frequency feature data. First, an automatic detection is performed on the time-frequency diagram, and abnormal frequency components in the signal are identified by setting a threshold. For narrowband interference, the identified ones are high-intensity signal peaks appearing in a specific frequency band; for wideband interference, the identified one is an abnormal increase in signal energy within a large range of frequency bands; the recognition of impulse interference is mainly judged by the sudden change of the signal in the time domain; while sweep interference is characterized by the continuous change of frequency over time. During the recognition process, a method based on statistical features is used to classify the interference types in the time-frequency diagram. Through the automatic recognition of these interference types, interference feature data is obtained, specifically including information such as the type of interference, frequency range, and time persistence.

[0081] Through the preliminary analysis of the spectrum of the baseband signal, the bandwidth and the main frequency components of the signal are quickly obtained, laying a foundation for subsequent filtering and interference identification. This enables the system to better focus on the frequency bands of interest and exclude irrelevant components. Understanding the bandwidth and the main frequency components of the signal helps to provide guiding information for filter design. By obtaining the spectrum feature data in advance, a basis can be provided for the subsequent design of filter parameters, optimizing the bandwidth selection of the filter and avoiding over-filtering or under-filtering. By preliminarily analyzing the frequency components, unnecessary calculations in the subsequent processing can be reduced, focusing on the data processing of important frequency bands, thereby improving the computational efficiency of the system. By utilizing the spectrum features of the signal, appropriate filter parameters can be adaptively designed to ensure the precise coverage of the signal bandwidth by the filter. The selection of appropriate cut-off frequencies, transition bandwidths, and stop-band attenuations can effectively filter out unwanted high-frequency components while retaining the key parts of the signal. Through reasonable filter parameter design, over-filtering of the signal during the filtering process can be effectively prevented, maintaining the fidelity of the signal and improving the quality of the signal after processing. Based on the stop-band attenuation and transition bandwidth design, interference signals in adjacent frequency bands can be effectively suppressed, avoiding signal interference caused by spectrum overlap, thereby enhancing the anti-interference effect of the system. Through high-frequency filtering, high-frequency noise and interference components in the baseband signal can be effectively removed, retaining the low-frequency DC component. This lays a clean base signal for subsequent interference identification and signal processing, improving the accuracy of signal processing. The removal of high-frequency components can reduce unnecessary signal redundancy, thereby reducing the amount of data for processing the signal and improving the processing efficiency. At the same time, the extraction of the DC component retains the core features of the signal, facilitating subsequent analysis. The filtered DC component contains key information of the signal, such as amplitude, phase, etc., providing a reliable data basis for interference identification and being conducive to improving the accuracy of anti-interference processing. The short-time Fourier transform can simultaneously analyze the characteristic changes of the signal in both time and frequency, providing dynamic time-frequency characteristic data. This method can reveal the instantaneous changes and frequency components in the signal, helping to identify complex interference patterns, especially pulse and swept-frequency interference. Through the short-time Fourier transform, real-time interference analysis can be achieved, quickly capturing the characteristics of interference signals. In a dynamic environment, this analysis method can detect the changes in the signal in a timely manner, providing rapid feedback for anti-interference processing. The time-frequency characteristic data can effectively identify various complex interference patterns, including periodic interference, pulse interference, etc., helping to accurately analyze different types of interference components in the signal and improving the anti-interference ability of the system. This step can identify various types of interference signals through the time-frequency characteristic data, including narrowband interference (interference targeting specific frequency bands), broadband interference (interference covering a relatively wide frequency range), pulse interference (interference with a sudden characteristic in time), and swept-frequency interference (interference with continuously changing frequencies). This diverse interference identification ability ensures that the system can cope with various complex interference environments.By performing interference recognition through time-frequency feature data, the spectrum and time characteristics of interference signals can be comprehensively analyzed, thereby more accurately identifying the characteristics of different types of interference and reducing the likelihood of misjudgment and missed judgment. The identified interference feature data provides an accurate parameter basis for subsequent anti-interference algorithms. Based on these features, the system can design more effective anti-interference algorithms and improve the effect of anti-interference processing.

[0082] Preferably, step S25 includes the following steps:

[0083] Step S251: Perform power spectral density estimation on the time-frequency feature data and improve the spectral resolution based on a window function to obtain high-resolution spectral data;

[0084] In the embodiment of the present invention, power spectral density (PSD) estimation is performed on the time-frequency feature data, and the Welch method is used to improve the accuracy of spectral estimation. First, the time-frequency data is divided into multiple overlapping time periods, and a window function is applied to each period of data for weighted processing. The window function is usually selected as the Hanning window to reduce the spectral leakage effect and improve the spectral resolution. Then, the fast Fourier transform (FFT) is performed on the data of each time period, and the results are averaged to reduce the influence of random noise, obtaining high-resolution spectral data. For example, using an FFT calculation of 512 points, with 50% overlap between each segment, the final output is the average power density on each frequency band. This process can effectively improve the spectral resolution and capture the subtle features of interference signals.

[0085] Step S252: Obtain background noise data, estimate the environmental noise level based on the background noise data, and calculate the mean and standard deviation of the spectrum to generate background noise baseline data;

[0086] In the embodiment of the present invention, background noise data in the environment is obtained, usually by sampling multiple time periods without interference signals. When estimating the environmental noise level, statistical analysis is performed on the background noise data to calculate the mean and standard deviation of its spectrum. In specific operations, the spectral data of the background noise is averaged in time and frequency to obtain the mean spectral line, and at the same time, the standard deviation of each frequency point is calculated to generate background noise baseline data. This baseline data is used to describe the normal fluctuation range of background noise and can be used as a reference standard for subsequent interference recognition. For example, the average power density of background noise in the 1 MHz frequency band is -80 dBm, and the standard deviation is 5 dB, and these values will be used to identify abnormal spectral fluctuations.

[0087] Step S253: Perform interference signal recognition on the high-resolution spectral data according to the background noise baseline data to obtain comprehensive interference recognition data, where interference signal recognition includes narrowband interference recognition, broadband interference recognition, pulse interference recognition, and swept-frequency interference recognition;

[0088] In an embodiment of the present invention, interference signals are identified from high-resolution spectrum data based on background noise baseline data. First, by setting a threshold, spectral components exceeding the background noise baseline are identified. The threshold is typically set as the mean of the background noise plus three times the standard deviation to ensure that only spectral components with significant differences are identified. For narrowband interference, a significant increase in power within a certain narrow frequency band in the spectrum is detected by the system; broadband interference is manifested as a significant power level higher than the baseline within a larger frequency band; impulse interference is identified by short-time power peaks in the time domain; and sweep interference is identified by the characteristic of continuous movement of the spectrum over time. The identification result of the interference signal is output as comprehensive interference identification data, which records the type of interference and its affected frequency band.

[0089] Step S254: Extract characteristic parameters of the interference signal from the comprehensive interference identification data to obtain interference characteristic data, where the characteristic parameters include center frequency, bandwidth, duration, and power level.

[0090] In an embodiment of the present invention, characteristic parameters of the interference signal are extracted based on the comprehensive interference identification data. In specific operations, first, the center frequency of each type of interference is estimated. For example, for narrowband interference, the center frequency is the frequency point where the maximum power density appears. Secondly, the bandwidth of the interference signal is calculated by detecting the effective frequency band width of the interference signal in the frequency domain. For impulse interference, its duration also needs to be calculated, which is determined according to the signal power mutation interval in the time domain. Finally, the power level of the interference signal is recorded, usually in the form of power spectral density (dBm / Hz). The output of this step is interference characteristic data including the center frequency, bandwidth, duration, and power level of the interference signal, which is used for subsequent anti-interference processing.

[0091] The present invention estimates the power spectral density and combines window functions to improve the spectral resolution, making the resolution of signals in the frequency domain higher. This is of great significance for accurately distinguishing signals with adjacent frequencies, especially for the separation and identification of narrowband interference and broadband interference. The high-resolution spectrum can more clearly reveal weak interference signals. Even if the power of the interference signal is low, it can be more clearly distinguished in the high-resolution spectrum, thereby improving the sensitivity of the system to weak interference. The high-resolution spectral data can better separate signals with similar frequencies, reduce aliasing, enhance the adaptability of the system to complex interference environments, and contribute to subsequent interference signal identification. By collecting background noise data, the system can construct an accurate background noise baseline to reflect the noise level in the current environment. This enables subsequent interference identification to better eliminate the influence of environmental noise and improve the accuracy of interference detection. By calculating the spectral mean and standard deviation, the system can identify signal variations beyond the normal noise level, thereby effectively reducing false alarms and ensuring the accuracy of interference identification. The environmental noise level changes with the scene and time. The noise estimation based on the background noise baseline can enable the system to adapt to these changes and ensure high interference detection performance in different noise environments. By combining the background noise baseline data and high-resolution spectral data, the system can accurately identify narrowband, broadband, pulsed, and swept-frequency interference. This broad interference identification ability makes the system applicable to complex electromagnetic interference environments and ensures the stability of navigation signals. Through the background noise baseline, the system can distinguish true interference signals from background noise, thereby reducing misjudgment and missed judgment and significantly improving the reliability of interference identification. The generated comprehensive interference identification data provides comprehensive interference characteristic information for subsequent anti-interference processing, enabling anti-interference algorithms to be optimized targeted and enhancing the effectiveness of interference suppression. Extracting characteristic parameters such as the center frequency, bandwidth, duration, and power level of interference signals can describe the interference in detail, facilitating subsequent interference classification and suppression. These characteristic information provides a comprehensive description of interference for the system, enabling anti-interference algorithms to perform targeted processing according to different interference types and characteristics. By extracting interference characteristic data, the system can formulate more precise anti-interference strategies according to the characteristics of different interferences (such as frequency range, intensity, etc.). For example, the processing strategies for pulsed interference and narrowband interference are different, and the extraction of these characteristics ensures the flexibility and effectiveness of anti-interference algorithms. The system can dynamically adjust the anti-interference strategy according to the interference characteristic data extracted in real time, enabling the system to maintain high anti-interference performance in a constantly changing interference environment and enhancing the stability and reliability of navigation signals.

[0092] Preferably, step S253 includes the following steps:

[0093] Step S2531: Perform narrowband interference identification on the high-resolution spectrum data based on a preset peak rule to obtain narrowband interference identification data, where the preset peak rule is specifically that the power density at this frequency point exceeds the noise level in the background noise baseline data plus 5 times the standard deviation;

[0094] In the embodiment of the present invention, narrowband interference identification is performed on the high-resolution spectrum data, and a preset peak rule is used to detect narrowband interference. The specific method is to traverse each frequency point in the spectrum data and determine whether its power density exceeds the noise level in the background noise baseline plus 5 times the standard deviation. If the power density of a certain frequency point meets this condition, it is regarded as narrowband interference. For example, if the average noise power at a certain frequency in the background noise baseline is -90 dBm and the standard deviation is 2 dB, when the power density of the frequency point exceeds -80 dBm, the system marks this point as narrowband interference. Through this method, interference signals existing in the local frequency range can be effectively identified, and narrowband interference identification data can be generated to record the frequency position and intensity of the interference occurrence.

[0095] Step S2532: Calculate the local mean of the high-resolution spectrum data, compare it with the global mean, and perform band identification of the global mean according to a preset mean threshold to obtain wideband interference identification data;

[0096] In the embodiment of the present invention, wideband interference identification is performed on the high-resolution spectrum data. First, the global trend of the signal is identified through local mean calculation. The specific operation is to divide the spectrum into multiple frequency bands and calculate the local power mean within each frequency band. Then, the local mean of each frequency band is compared with the average power of the global spectrum. If the local mean of a certain frequency band is significantly higher than the global mean and exceeds the preset mean threshold, it is determined as wideband interference. For example, the set threshold is the global mean plus 3 dB. If the local mean of a certain frequency band exceeds this threshold, it is identified as wideband interference. This method can effectively capture signal interference in the wide frequency range, generate wideband interference identification data, and indicate the frequency band range and average power level of the interference.

[0097] Step S2533: Extract short-time high-amplitude signals from the high-resolution spectrum data to obtain impulse interference identification data;

[0098] In the embodiments of the present invention, pulse interference recognition is performed on high-resolution spectrum data, with the focus on extracting high-amplitude signals that appear within a short time. In specific operations, first, the spectrum data within each time window on the time-frequency diagram is scanned to detect abnormally high-amplitude signals that appear within a short time. These high-amplitude signals are characterized by short duration and high power density, usually manifested as instantaneous peaks on the spectrum. For example, if the power density of a certain frequency suddenly jumps to -50 dBm within a 10-ms window while the power density of the surrounding frequency bands remains below -80 dBm, it is recognized as pulse interference. The pulse interference recognition data extracted through this process records the time point and frequency range at which the interference occurs.

[0099] Step S2534: Perform swept-frequency interference recognition on the high-resolution spectrum data based on the frequency-time change pattern, so as to obtain swept-frequency interference recognition data;

[0100] In the embodiments of the present invention, swept-frequency interference recognition is performed on high-resolution spectrum data, and interference detection is carried out using the characteristics of the frequency-time change pattern. Swept-frequency interference usually shows that the frequency changes linearly or non-linearly with time, and the power level remains stable or changes regularly during the frequency sweep. In specific operations, first, by comparing the spectrum data within different time windows, the continuous change of the frequency components is observed to detect whether there is a pattern of frequency movement with time. If a certain frequency component moves continuously on the time axis, for example, the frequency sweeps from 2 GHz to 2.2 GHz within 1 second and the power level remains high, it is determined as swept-frequency interference. This process generates swept-frequency interference recognition data, recording the start frequency, end frequency, time span, and power change of the frequency sweep.

[0101] Step S2535: Combine the broadband interference recognition data, pulse interference recognition data, swept-frequency interference recognition data, and narrowband interference recognition data into comprehensive interference recognition data.

[0102] In the embodiments of the present invention, the broadband interference recognition data, pulse interference recognition data, swept-frequency interference recognition data, and narrowband interference recognition data are combined to form comprehensive interference recognition data. First, the recognition results of various types of interference are classified and sorted to ensure that different types of interference do not overlap in frequency and time. Then, according to the characteristics of each type of interference, the characteristic parameters of each interference are summarized into the comprehensive interference recognition data. For example, the results of narrowband interference and swept-frequency interference existing simultaneously in a certain frequency band at a certain moment are combined, and the corresponding interference frequency band and type are marked. Finally, comprehensive interference recognition data containing multiple interference types and characteristics is generated. This data provides comprehensive interference information for subsequent anti-interference processing.

[0103] The present invention identifies signals in the spectrum that exceed the background noise baseline plus 5 times the standard deviation as interference signals by presetting a peak rule. The setting of this rule helps to effectively capture narrowband interference, especially those signals with concentrated frequency points and significantly higher power than the background noise. By setting the multiple standard of the peak rule, the false alarm phenomenon of identifying normal signals or background noise as interference is effectively reduced, while avoiding missed reports and improving the recognition accuracy of the system. By comparing the mean value of the local frequency band with the global mean value, the system can effectively identify broadband interference, especially those interference signals that persist within a relatively wide frequency range. By comparing the global mean value and the local mean value of the spectrum data, it effectively adapts to different environmental noise levels and ensures that the system can accurately identify broadband interference in a complex environment. Impulse interference is often a short-term but high-intensity signal. By extracting high-amplitude short-term signals, the system can quickly identify such interference and avoid its interference effect on normal navigation signals. Impulse interference has the characteristics of instantaneous appearance and sharp increase in power. Extracting high-amplitude short-term signals can ensure the system's rapid response to this type of interference and improve the interference detection efficiency. Swept-frequency interference affects the signal quality through the pattern of frequency changing with time. The system effectively identifies this complex interference pattern by analyzing the frequency-time variation law, ensuring that it can be captured and processed in a timely manner. Swept-frequency interference changes continuously in frequency, so traditional static spectrum analysis methods are difficult to handle. Through the joint analysis of time and frequency, the system can monitor and identify swept-frequency interference in real time, improving the anti-interference ability of the system in a dynamic environment. By combining the identification results of narrowband, broadband, impulse, and swept-frequency interference into comprehensive data, the system can comprehensively grasp all interference signals in the current environment and provide global interference information for subsequent anti-interference processing. The combination of comprehensive interference identification data enables the anti-interference system to obtain all interference information at once when dealing with multiple types of interference, reducing the time and complexity required for separately processing different interference types and improving the efficiency of anti-interference processing. By combining multiple interference data, the system can better extract the characteristics of each interference (such as frequency range, intensity, etc.), providing a more comprehensive basis for anti-interference algorithms and further improving the effect of suppressing interference.

[0104] Preferably, step S3 includes the following steps:

[0105] Step S31: Select an anti-interference algorithm based on the interference type according to the interference characteristic data, so as to obtain algorithm selection result data, where the anti-interference algorithms include adaptive notch filtering, time-domain cancellation method, frequency-domain suppression method, and space-time adaptive processing;

[0106] In the embodiments of the present invention, a suitable anti-interference algorithm is selected according to interference characteristic data. First, the identified interference characteristics are analyzed to determine their types (such as narrowband interference, broadband interference, pulse interference, or swept-frequency interference). For example, for narrowband interference, an adaptive notch filter can be selected; for broadband interference, time-domain cancellation or frequency-domain suppression methods may be more suitable. The specific process of algorithm selection includes presetting matching algorithms for different interference types, comparing the current interference characteristic data with these preset conditions, and finally selecting the optimal anti-interference algorithm to generate algorithm selection result data, indicating the specific algorithm to be applied and the corresponding parameter settings.

[0107] Step S32: Perform interference suppression processing on the DC component data based on the algorithm selection result data to obtain preliminary interference suppression signal data;

[0108] In the embodiments of the present invention, interference suppression processing is performed on the DC component data according to the algorithm selection result data. In specific implementation, for example, an adaptive notch filter is used. First, the center frequency of the filter is set to the frequency of the narrowband interference, and the gain of the filter is dynamically adjusted using an adaptive algorithm to ensure effective suppression of the interference component at this frequency. Suppose in a certain case, the frequency of the narrowband interference is 1.5 GHz, and the set bandwidth of the filter is 100 kHz. Then, by updating the filter parameters in real time, preliminary interference suppression signal data is obtained, removing the influence of the target interference frequency.

[0109] Step S33: Perform signal quality evaluation on the preliminary interference suppression signal data based on signal-to-noise ratio calculation, code correlation peak analysis, and navigation accuracy estimation to obtain signal quality evaluation data;

[0110] In the embodiments of the present invention, signal quality evaluation is performed on the preliminary interference suppression signal data. The specific methods include calculating the signal-to-noise ratio (SNR), analyzing the code correlation peak, and estimating the navigation accuracy. The calculation of the signal-to-noise ratio is achieved by calculating the ratio of the effective power of the signal to the background noise power. Suppose the effective power of the signal is -70 dBm and the background noise power is -90 dBm, then the SNR is 20 dB. In addition, the code correlation algorithm is used to analyze the correlation peak of the signal, and a peak threshold is set. If the correlation peak is higher than the set value, the signal quality is better. Finally, through the estimation of the navigation accuracy, the effectiveness of the signal in the actual navigation scenario is evaluated, and finally signal quality evaluation data is generated.

[0111] Step S34: Fine-tune the anti-interference algorithm parameters according to the signal quality evaluation data and perform interference suppression processing again to obtain interference suppression signal data;

[0112] In the embodiments of the present invention, the anti-interference algorithm parameters are fine-tuned according to the signal quality evaluation data. This process involves reviewing the signal quality evaluation results. For example, if the signal-to-noise ratio is low or the code-related peak does not meet the standard, it may be necessary to adjust the algorithm parameters, such as increasing the bandwidth of the adaptive notch filter or modifying the sample length in the time-domain cancellation method. After setting the new parameters, the interference suppression process is carried out again to obtain new interference suppression signal data, ensuring that the signal can effectively improve the anti-interference ability in practical applications.

[0113] Step S35: Perform signal-to-noise ratio comparison and analysis on the interference suppression signal data and the DC component data, and calculate the interference suppression gain, so as to obtain the anti-interference effect evaluation data.

[0114] In the embodiments of the present invention, signal-to-noise ratio comparison and analysis are performed on the interference suppression signal data and the DC component data, and the interference suppression gain is calculated. In specific implementation, the signal-to-noise ratio is calculated for the interference suppression signal data and the DC component data respectively. If the effective power of the interference suppression signal is -65 dBm and the background noise power is still -90 dBm, the new signal-to-noise ratio is 25 dB. By comparing the initial signal-to-noise ratio and the signal-to-noise ratio after processing, the interference suppression gain is calculated. For example, if the initial signal-to-noise ratio is 20 dB, the gain is 5 dB. This data forms the anti-interference effect evaluation data, demonstrating the effectiveness of the processed signal in anti-interference.

[0115] Through analyzing interference characteristic data, the system can select the most suitable anti-interference algorithm according to different interference types (such as notch filtering for narrowband interference and frequency-domain suppression for broadband interference, etc.), ensuring that there is an effective processing method for each type of interference and improving the anti-interference efficiency. The system can combine different algorithms according to the complex interference environment to deal with multi-type interference, forming a more comprehensive and flexible interference suppression scheme. Through appropriate algorithms, the system can effectively reduce or eliminate different types of interference signals, significantly improve the signal quality of the DC component data, and ensure that the navigation signal is not affected by interference. Based on the results of the previous algorithm selection, the system can dynamically apply different suppression techniques to achieve a rapid response to changes in the interference situation, enabling the system to still work stably in a changing interference environment. Through various indicators such as signal-to-noise ratio calculation, code correlation peak analysis, and navigation accuracy estimation, the system can comprehensively evaluate the anti-interference effect from different perspectives to ensure that the signal quality after suppression is effectively improved. Through signal quality evaluation, the system can obtain an intuitive feedback on the anti-interference effect, providing a basis for subsequent parameter optimization and making the interference suppression process more accurate. According to the signal quality evaluation data, the parameters of the anti-interference algorithm are fine-tuned to ensure that the algorithm can achieve the best effect in the actual environment and avoid over-suppression or under-suppression. By continuously adjusting the algorithm parameters according to the real-time evaluation results, the system can adaptively adjust in different interference environments, achieve a dynamic improvement in anti-interference ability, and ensure long-term stability. Through signal-to-noise ratio comparison analysis and interference suppression gain calculation, the system can intuitively quantify the improvement effect brought by the anti-interference processing, providing an accurate basis for subsequent optimization. Through anti-interference effect evaluation, the system can confirm the gain of the suppression effect and further optimize the anti-interference processing flow to ensure the continuous improvement and optimal execution of the anti-interference scheme under different interference conditions.

[0116] Preferably, step S31 includes the following steps:

[0117] Step S311: Classify the interference type of the interference characteristic data, including narrowband interference, broadband interference, pulse interference, and swept-frequency interference, so as to obtain interference type classification data;

[0118] In the embodiments of the present invention, for the interference feature data, interference type classification is first required. It is necessary to analyze according to indicators such as spectrum information, time characteristics, and power level in the interference feature data. In specific implementation, machine learning algorithms, such as support vector machine (SVM) or random forest, can be used to train and classify the extracted interference features. For this purpose, some benchmark thresholds are set: for example, if the spectrum width is less than 1 MHz, it is classified as narrowband interference; if the spectrum width is greater than 20 MHz, it is classified as broadband interference; if a high-amplitude pulse appears in a short time for the signal, it is marked as pulse interference; and if the signal frequency changes smoothly and unstably over time, it is marked as swept-frequency interference. Finally, after these processes, the obtained interference type classification data will clearly indicate different interference types for subsequent steps to use.

[0119] Step S312: Establish an anti-interference algorithm decision tree based on the interference type classification data, so as to obtain algorithm decision tree data;

[0120] In the embodiments of the present invention, an anti-interference algorithm decision tree is established according to the interference type classification data. First, it is necessary to determine the anti-interference algorithm corresponding to each interference type. The construction of the decision tree can be based on expert experience or existing literature to form a set of structured decision rules. For example, if the interference type is narrowband interference, the first branch of the decision tree selects an adaptive notch filter; if it is broadband interference, the frequency domain suppression method is selected. The specific steps for constructing the decision tree include mapping each interference type to the corresponding algorithm to form a tree structure, and attaching decision conditions to each node. Finally, the generated algorithm decision tree data will provide a basis for subsequent anti-interference algorithm matching.

[0121] Step S313: Perform optimal anti-interference algorithm matching for various types of interference based on the algorithm decision tree data, so as to obtain algorithm matching result data;

[0122] In the embodiments of the present invention, optimal anti-interference algorithm matching is performed for various types of interference based on the algorithm decision tree data. The specific steps include traversing the decision tree, checking each interference type, and performing matching according to the corresponding anti-interference algorithm decision. Using the actually identified interference type, input it into the decision tree, and according to the structure of the tree and the corresponding rules, automatically match the optimal anti-interference algorithm. If the currently identified interference is narrowband interference, the system will directly output an adaptive notch filter as the matching result. Through this process, the generated algorithm matching result data will clearly list the optimal anti-interference algorithm corresponding to each interference, laying a foundation for subsequent optimization decisions.

[0123] Step S314: Perform conflict detection and priority sorting on the algorithm matching result data, so as to obtain optimized algorithm selection result data.

[0124] In the embodiments of the present invention, conflict detection and priority sorting are performed on the algorithm matching result data. First, it is necessary to identify whether multiple anti-interference algorithms are applicable to the same type of interference. By comparing each matching result and checking the performance parameters of the algorithms (such as anti-interference effect, processing delay, etc.), their priorities are sorted. For example, if narrowband interference can be processed by both an adaptive notch filter and a frequency-domain suppression method at the same time, the adaptive notch filter with the best performance is preferably selected. To this end, a scoring mechanism can be set to score each algorithm, and finally the algorithm with the highest score is selected as the optimized algorithm selection result data, which will ensure that the anti-interference solution can efficiently and effectively cope with various types of interference in practical applications.

[0125] By subdividing the interference feature data into narrowband interference, broadband interference, pulse interference, and frequency-sweeping interference, the system can more accurately identify each type of interference, and then select a more suitable anti-interference algorithm, avoiding unnecessary resource waste. This fine classification method ensures the accurate identification of the characteristics of different types of interference, enabling the system to respond faster to specific interference sources and improving the overall anti-interference effect. By establishing an anti-interference algorithm decision tree, the system can automatically select the most suitable anti-interference path according to different types of interference, simplifying the manual intervention process and improving the efficiency and accuracy of algorithm decision-making. The decision tree provides multiple levels of anti-interference solutions, enabling the system to cope with multiple types of interference in a complex environment and ensuring that each type of interference has a suitable processing method. Under the guidance of the decision tree, the system can select the optimal anti-interference algorithm for each type of interference, ensuring that the application of the anti-interference algorithm is not only effective but also efficient, and avoiding the decline of the anti-interference effect caused by inappropriate algorithms. According to the optimal matching, the system only calls the necessary anti-interference algorithms, saving computing resources, reducing processing delay, and improving real-time performance. Different types of interference may require the simultaneous application of multiple algorithms. Conflict detection helps to identify and avoid interference between the simultaneously applied algorithms, thus ensuring the compatibility between the algorithms and avoiding the situation where the processing effects cancel each other out. By sorting the algorithm priorities, the system can preferentially execute the most critical anti-interference algorithms according to the severity and type of interference, ensuring that the most influential problems on the navigation performance are solved first in a complex interference environment.

[0126] Preferably, step S4 includes the following steps:

[0127] Step S41: Establish an anti-interference performance index system based on the signal-to-noise ratio improvement, interference suppression degree, and signal distortion degree according to the anti-interference effect evaluation data, so as to obtain performance index data;

[0128] In the embodiment of the present invention, an anti-interference performance index system based on signal-to-noise ratio improvement, interference suppression degree, and signal distortion degree is established according to anti-interference effect evaluation data. First, specific performance indicators need to be defined. The signal-to-noise ratio improvement can be obtained by calculating the difference in the signal-to-noise ratio between the interference suppression signal data and the original signal data; the interference suppression degree can be evaluated by calculating the ratio of the original signal to the interference signal; the signal distortion degree can be measured by the root mean square error (RMSE) to measure the deviation between the signal and the target signal. Then, based on these indicators, an anti-interference performance evaluation system is established. The specific parameters can be set as the signal-to-noise ratio improvement should be greater than 10 dB, the interference suppression degree should be less than -20 dB, and the signal distortion degree should be less than 3%. Finally, the performance index data established using these indicators will provide a benchmark for subsequent parameter optimization.

[0129] Step S42: Perform a sensitivity analysis on the anti-interference algorithm parameters according to the performance index data to obtain parameter sensitivity data;

[0130] In the embodiment of the present invention, a sensitivity analysis is performed on the anti-interference algorithm parameters according to the performance index data. The goal of the sensitivity analysis is to identify the degree of influence of each parameter on the anti-interference performance. The "local sensitivity analysis method" can be used. By changing one parameter and keeping other parameters unchanged, observe the change of the performance index. For example, gradually change the cut-off frequency of the adaptive notch filter from 0.1 MHz to 1 MHz, and record the signal-to-noise ratio improvement after each change. By calculating the sensitivity of each parameter, parameter sensitivity data is obtained, and the parameters with large sensitivity are identified to guide the subsequent optimization direction.

[0131] Step S43: Use the parameter sensitivity data to construct an adaptive optimization objective function and perform parameter optimization to obtain an optimized parameter set;

[0132] In the embodiment of the present invention, the parameter sensitivity data is used to construct an adaptive optimization objective function. In the specific implementation process, the signal-to-noise ratio improvement, interference suppression degree, and signal distortion degree are selected as the optimization objectives, and weights are set based on the previous sensitivity analysis results. The optimization objective function can be formalized as: F(x) = w 1 *SNR + *ISD - *Dist, where w 1 , w 2 , w 3 are weights, SNR is the signal-to-noise ratio improvement, ISD is the interference suppression degree, and Dist is the signal distortion degree. Use an optimization algorithm (such as particle swarm optimization or genetic algorithm) to perform parameter optimization. The finally generated optimized parameter set will help improve the anti-interference performance.

[0133] Step S44: Determine the optimized parameter space according to the parameter sensitivity data, and set the parameter value range and constraint conditions to obtain parameter optimization configuration data;

[0134] In an embodiment of the present invention, an optimized parameter space is determined based on parameter sensitivity data, and a parameter value range and constraint conditions are set. First, the range of optimizable parameters is defined. For example, the cut-off frequency range of an adaptive notch filter is from 0.1 MHz to 1 MHz, and the gain range is from 1 to 10 dB. Then, constraint conditions are set for each parameter. For example, in a specific case, the gain is not allowed to exceed 8 dB to avoid over-suppressing the signal. These pieces of information are integrated into parameter optimization configuration data, forming clear optimization objectives and constraint conditions for the subsequent optimization process.

[0135] Step S45: Initialize the population of the evolutionary algorithm based on the parameter optimization configuration data, thereby obtaining initial population data, where initializing the population of the evolutionary algorithm includes encoding scheme design and initial population generation;

[0136] In an embodiment of the present invention, the population of the evolutionary algorithm is initialized based on the parameter optimization configuration data, and an appropriate encoding scheme is selected. For example, binary encoding or real-number encoding is used to represent different parameter combinations. In the initialization stage, a certain number of individuals (such as 100) are generated, and each individual represents a parameter combination. The generation of these initial individuals can be randomized to ensure extensive exploration of the parameter space, ensure coverage of different optimization strategies, form initial population data, and lay a foundation for subsequent fitness evaluation.

[0137] Step S46: Perform fitness evaluation on the initial population data, calculate the performance metrics of each individual through simulation tests, thereby obtaining population fitness data;

[0138] In an embodiment of the present invention, fitness evaluation is performed on the initial population data, and the performance metrics of each individual are calculated through simulation tests. For this purpose, the parameter combination of each individual can be input into the anti-interference algorithm for simulation, the signal-to-noise ratio improvement, interference suppression degree, and signal distortion degree of each individual are recorded, and the fitness score is calculated based on these metrics. The formula for fitness evaluation can be Fitness = α·SNR + β·ISD - γ·Dist, where α, β, and γ are weight coefficients. Finally, the obtained population fitness data will guide subsequent selection, crossover, and mutation operations.

[0139] Step S47: Perform selection, crossover, and mutation operations based on the population fitness data to generate a new generation of population, and repeat the fitness evaluation process, thereby obtaining evolutionary population data;

[0140] In the embodiments of the present invention, selection, crossover, and mutation operations are performed based on population fitness data. First, the roulette wheel selection or tournament selection method is used to select individuals with higher fitness from the current population as parents. Then, new individuals are generated through single-point crossover or multi-point crossover to form a new population. At the same time, a mutation operation is applied, for example, randomly changing a certain parameter value in an individual with a certain probability to enhance the diversity of the population. The fitness evaluation process is repeated to obtain new population fitness data, so as to continuously iterate and evolve to improve the overall performance.

[0141] Step S48: Judge the evolved population data according to the preset termination condition. If it is satisfied, output the optimal individual as the optimized parameter set; otherwise, return to step S47 to continue evolution, where the preset termination condition is specifically that the signal-to-noise ratio improvement is greater than or equal to 15 dB and the signal distortion degree is less than or equal to 5%.

[0142] In the embodiments of the present invention, the evolved population data is judged according to the preset termination condition, and the preset termination condition is that the signal-to-noise ratio improvement is greater than or equal to 15 dB and the signal distortion degree is less than or equal to 5%. At the end of each generation, check whether the current best individual meets these conditions. If the conditions are met, the system will output this individual as the final optimized parameter set; if not, return to step S47 to continue iteration to ensure that the evolutionary algorithm can continuously improve and achieve a better anti-interference effect.

[0143] Step S49: Construct an adaptive anti-interference model based on the optimized parameter set to obtain adaptive model data, where the adaptive anti-interference model includes a dynamic parameter adjustment mechanism and an algorithm switching strategy; use the adaptive anti-interference model to perform secondary processing on the interference suppression signal data to obtain optimized signal data.

[0144] In the embodiments of the present invention, an adaptive anti-interference model is constructed based on the optimized parameter set. First, determine the structure of the model, including a dynamic parameter adjustment mechanism and an algorithm switching strategy. The dynamic parameter adjustment mechanism allows the parameters of the filter to be automatically adjusted according to the real-time signal condition to adapt to the changing interference environment; while the algorithm switching strategy switches between different anti-interference algorithms according to the currently detected interference type. After construction, use this adaptive anti-interference model to perform secondary processing on the interference suppression signal data, and finally obtain optimized signal data to ensure efficient anti-interference performance under various interference conditions.

[0145] By constructing a performance index system based on signal-to-noise ratio, interference suppression ratio, and signal distortion degree, the present invention can effectively quantify the performance of anti-interference algorithms, ensuring a clear standard when evaluating the anti-interference effect. From the improvement of signal-to-noise ratio, interference suppression ratio to signal distortion degree, it ensures the comprehensiveness of anti-interference performance evaluation, not only considering the effect of suppressing interference but also taking into account signal quality. Sensitivity analysis helps identify key parameters in anti-interference algorithms. Minor adjustments to these parameters may have a significant impact on system performance, thus providing a basis for subsequent optimization. By identifying parameters with less impact on performance, it is possible to avoid wasting computing resources on unimportant parameters and focus on optimizing the most valuable parameters. The optimization objective function constructed using sensitivity data can be dynamically adjusted according to the interference characteristics of the actual environment, making the parameter optimization process more adaptable and ensuring that the optimization direction is highly consistent with actual requirements. Through the optimization objective function, the anti-interference algorithm can improve the signal-to-noise ratio while minimizing signal distortion to the greatest extent, ensuring the anti-interference effect while maintaining signal quality. Setting the parameter space and constraint conditions can prevent the algorithm from searching in invalid parameter regions, improving the optimization efficiency and reducing unnecessary computational overhead. By setting the parameter value range and constraint conditions, it is possible to ensure that there will be no over-adjustment during the optimization process, which may cause system instability, thus guaranteeing the robustness of the algorithm. The initialized population of the evolutionary algorithm covers a wide range of parameter combinations, ensuring that the optimization process explores from multiple directions and increasing the possibility of finding the global optimal solution. Through reasonable coding design, the evolutionary algorithm can represent parameter combinations more efficiently, further improving the accuracy and efficiency of algorithm optimization. Fitness evaluation ensures that the optimization effect of each parameter combination can be strictly tested through simulation, thereby screening out individuals with better performance and improving the quality of algorithm optimization; through fitness evaluation, it is possible to avoid performance degradation caused by some parameter combinations due to unexpected situations, ensuring the effectiveness and accuracy of population evolution. The selection, crossover, and mutation operations in the evolutionary algorithm combine the advantages of global search and local optimization, ensuring both the exploration of new parameter combinations and the optimization of existing better solutions, and avoiding being trapped in local optimal solutions; through iterative evolution, the system can gradually approach the optimal solution, ensuring that the anti-interference algorithm has good adaptability and robustness in different interference environments. By setting specific signal-to-noise ratio improvement and signal distortion degree as termination conditions, it ensures that the optimization process has a clear goal and can prevent over-optimization from affecting signal quality; setting reasonable termination conditions can effectively reduce the computing time, avoid unnecessary repeated optimization, and improve the execution efficiency of the algorithm.The adaptive anti-interference model has a dynamic parameter adjustment mechanism, which can automatically adjust the anti-interference algorithm parameters according to real-time environmental changes, ensuring that the system is always in the best anti-interference state and adapting to complex interference environments. Through the algorithm switching strategy, the system can flexibly switch anti-interference algorithms according to different interference types and environmental requirements, improving the flexibility and accuracy of anti-interference processing. The secondary processing uses an optimized parameter set to further enhance the interference suppression effect while maximizing the preservation of signal quality. The finally output optimized signal data can maintain high stability and signal accuracy in complex interference environments.

[0146] Preferably, step S5 includes the following steps:

[0147] Step S51: Perform multi-phase interpolation processing on the optimized signal data and use a fractional delay filter to reconstruct the signal, thereby obtaining high-precision interpolation data;

[0148] In the embodiment of the present invention, multi-phase interpolation processing is performed on the optimized signal data, and the cubic spline interpolation algorithm is used to increase the sampling rate by adding interpolation points to the signal, specifically set to increase the original signal sampling rate to 5 times. First, analyze the original sampling rate of the optimized signal data to determine the positions and frequency ranges that need to be interpolated. Then, use a fractional delay filter to reconstruct the interpolated signal. The design of the fractional delay filter can adopt the window function method to ensure the smoothness and continuity of the interpolated signal. Finally, through multi-phase interpolation and reconstruction, the obtained high-precision interpolation data should have a high frequency resolution to meet the requirements of subsequent processing.

[0149] Step S52: Perform singular spectrum analysis on the high-precision interpolation data, identify and remove residual high-frequency interference components, thereby obtaining high-sampling-rate signal data;

[0150] In the embodiment of the present invention, singular spectrum analysis (SSA) is performed on the high-precision interpolation data. First, use the singular value decomposition (SVD) technique to perform time-frequency decomposition on the interpolated signal. The specific operation is to select an appropriate window size, segment the signal, and calculate the singular value matrix of each segment. Subsequently, identify the singular value characteristics related to high-frequency interference and remove the residual high-frequency interference components through a threshold method. This process removes the components of the noise signal and retains the main components of the signal. The obtained high-sampling-rate signal data will have better signal quality and lower interference effects.

[0151] Step S53: Use phase-locked loop technology to accurately synchronize the local sine signal data with the high-sampling-rate signal data and perform digital up-conversion processing based on quadrature modulation, thereby obtaining an anti-interference output signal.

[0152] In the embodiments of the present invention, the phase-locked loop (PLL) technology is used to precisely synchronize the local sine signal data with the high-sampling-rate signal data. First, a phase detector is designed to compare the phase difference between the local signal and the high-sampling-rate signal, and phase tracking is achieved by adjusting the loop filter. The specific parameters are set as follows: a band-pass filter is used to limit the signal bandwidth to 1 MHz to 2 MHz to ensure the effectiveness of signal synchronization. After synchronization is completed, the quadrature modulation technology is used to perform digital up-conversion processing on the synchronized signal. In this step, the local sine signal is multiplied by the high-sampling-rate signal after phase adjustment to generate an anti-interference output signal. The final output signal has high anti-interference performance and better signal quality.

[0153] The multi-phase interpolation processing of the present invention can add more sampling points to the signal, increase the sampling rate of the signal, thereby improving the ability to capture signal changes and improving the time resolution of the signal. Using a fractional delay filter can effectively reduce the aliasing effect, making the interpolated signal smoother, avoiding the influence of high-frequency components on the signal quality, and improving the accuracy of subsequent signal processing. High-precision interpolation data can more truly reflect the original characteristics of the signal, providing high-quality basic data for subsequent analysis and processing. Singular spectrum analysis is a powerful signal processing tool that can effectively identify high-frequency interference components in the signal. By removing these components, the clarity and usability of the signal are improved. By denoising the signal, the useful components of the signal can be retained to the greatest extent, ensuring that the high-sampling-rate signal data still has high authenticity and accuracy; the clarity of the high-sampling-rate signal data is improved after removing interference, providing a better basis for subsequent signal analysis, feature extraction or other processing, and enhancing the overall performance of the system. The phase-locked loop (PLL) technology can achieve precise synchronization of the local signal and the high-sampling-rate signal, ensuring the stability of the phase relationship between the two, thereby enhancing the integrity and consistency of the signal. The digital up-conversion processing based on quadrature modulation can effectively improve the spectrum utilization rate of the signal, reduce interference to the channel, and enable the signal to be transmitted in a wider frequency spectrum range. The final anti-interference output signal has good anti-interference ability in both the frequency domain and the time domain, is more adaptable, can effectively cope with various interference situations, and improves the reliability and stability of the system.

[0154] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be included in the present invention.

[0155] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A high-gain satellite navigation antenna anti-interference signal control method, characterized in that: The following steps are involved: Step S1: acquiring original signal data of a satellite navigation signal, and digitally sampling the original signal data to generate local sinusoidal signal data; performing digital down-conversion processing on the local sinusoidal signal data to obtain baseband signal data, wherein the digital down-conversion processing includes setting a local oscillator frequency and multiplying the sampling signal by the local oscillator signal; Step S2: designing low-pass filter parameters according to the baseband signal data, and performing high-frequency filtering on the baseband signal data using the low-pass filter, thereby obtaining DC component data; Perform spectrum analysis on DC component data and identify interference signals to obtain interference feature data; Step S3: Determine the anti-interference algorithm according to the interference feature data, and use the anti-interference algorithm to perform interference suppression processing on the DC component data, so as to obtain interference suppression signal data; perform signal-to-noise ratio comparison according to the DC component data and the interference suppression signal data, and perform anti-interference effect evaluation, so as to obtain anti-interference effect evaluation data. Step S3 includes the following steps: Step S31: selecting an anti-interference algorithm based on the interference type according to the interference feature data, thereby obtaining algorithm selection result data, wherein the anti-interference algorithm includes adaptive notch filtering, time domain elimination method, frequency domain suppression method and space-time adaptive processing; Step S32: performing interference suppression processing on the DC component data based on the algorithm selection result data, thereby obtaining preliminary interference suppression signal data; Step S33: performing signal quality evaluation based on signal-to-noise ratio calculation, code correlation peak analysis and navigation accuracy estimation on the preliminary interference suppression signal data, thereby obtaining signal quality evaluation data; Step S34: fine-tuning the anti-interference algorithm parameters according to the signal quality evaluation data, and re-performing the interference suppression process, thereby obtaining interference suppression signal data; Step S35: performing signal-to-noise ratio comparison analysis on the interference suppression signal data and the DC component data, and calculating the interference suppression gain, thereby obtaining anti-interference effect evaluation data; Step S4: Optimize and adjust the anti-interference algorithm parameters according to the anti-interference effect evaluation data, and build an adaptive anti-interference model; use the adaptive anti-interference model to perform anti-interference processing on the interference suppression signal data to obtain optimized signal data; Step S5: interpolate the optimized signal data and remove the high-frequency components to obtain high-sampling rate signal data; perform digital up-conversion processing on the high-sampling rate signal data using the local sinusoidal signal data to obtain an anti-interference output signal.

2. The high-gain satellite navigation antenna anti-interference signal control method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining an original satellite navigation radio frequency signal; amplifying the original satellite navigation radio frequency signal using a high linearity preamplifier to obtain original signal data; Step S12: performing system clock synchronization on the original signal data and adding an accurate timestamp to obtain signal time synchronization data; Step S13: performing analog-to-digital conversion processing on the signal time synchronization data based on a high-speed analog-to-digital converter, and performing digital sampling to obtain digital sampled signal data, wherein the sampling frequency of the digital sampling is greater than twice the highest frequency in the original signal data; Step S14: extracting key frequency components from the digital sampled signal data by a digital signal generator, thereby generating local sinusoidal signal data synchronized with the original signal data; Step S15: Perform digital down-conversion processing on the local sinusoidal signal data to obtain baseband signal data, wherein the digital down-conversion processing includes setting the local oscillator frequency and multiplying the sampling signal by the local oscillator signal.

3. The high-gain satellite navigation antenna anti-interference signal control method according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: setting the local oscillator frequency according to the center frequency of the original signal data, and using a digital frequency synthesizer to generate a digital local oscillator signal with the set local oscillator frequency, wherein the digital local oscillator signal includes an in-phase component and an orthogonal component; Step S152: multiplying the local sinusoidal signal data with the in-phase and quadrature components of the digital local oscillator signal respectively, thereby obtaining an in-phase component signal and a quadrature component signal; Step S153: performing digital low-pass filtering on the in-phase component signal and the quadrature component signal to obtain in-phase path signals and quadrature path signals; Step S154: Calculate the amplitude and phase information of the signal according to the in-phase path and the quadrature path signals to form baseband signal data in complex form.

4. The high-gain satellite navigation antenna anti-interference signal control method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing a preliminary analysis on the baseband signal data to determine the bandwidth and main frequency components of the signal, thereby obtaining signal spectrum characteristic data; Step S22: Designing low-pass filter parameters based on cutoff frequency, transition band width and stop-band attenuation according to the signal spectrum characteristic data, thereby obtaining filter design parameters; Step S23: performing high-frequency filtering on the baseband signal data based on the filter design parameters, thereby obtaining DC component data; Step S24: performing short-time Fourier transform analysis on the DC component data to obtain time-frequency characteristic data; Step S25: performing interference signal identification according to the time-frequency characteristic data to obtain interference characteristic data, wherein interference signal identification includes narrowband interference identification, broadband interference identification, pulse interference identification and swept frequency interference identification.

5. The high-gain satellite navigation antenna anti-interference signal control method according to claim 4, characterized in that: Step S25 includes the following steps: Step S251: performing power spectrum density estimation on the time-frequency feature data, and improving the spectrum resolution based on a window function, thereby obtaining high-resolution spectrum data; Step S252: obtaining background noise data, estimating the ambient noise level according to the background noise data, and calculating the mean and standard deviation of the spectrum, thereby generating background noise baseline data; Step S253: performing interference signal identification on the high-resolution spectrum data according to the background noise baseline data, thereby obtaining comprehensive interference identification data, wherein the interference signal identification includes narrowband interference identification, broadband interference identification, pulse interference identification and swept frequency interference identification; Step S254: extract characteristic parameters of the interference signal from the comprehensive interference identification data, thereby obtaining interference characteristic data, wherein the characteristic parameters include center frequency, bandwidth, duration and power level.

6. The high-gain satellite navigation antenna anti-interference signal control method according to claim 5, characterized in that: Step S253 includes the following steps: Step S2531: performing narrowband interference identification based on a preset peak rule on the high-resolution spectrum data, thereby obtaining narrowband interference identification data; specifically: traversing each frequency point in the high-resolution spectrum data, determining whether its power density exceeds the noise level in the background noise baseline plus 5 times the standard deviation; if the power density of a certain frequency point meets this condition, it is regarded as narrowband interference; identifying the interference signal existing in the local frequency range, and generating narrowband interference identification data; Step S2532: Calculate the local mean of the high-resolution spectrum data, compare it with the global mean, and identify the frequency band of the global mean according to a preset mean threshold, so as to obtain broadband interference identification data; Step S2533: extracting short-time high-amplitude signals from the high-resolution spectrum data, thereby obtaining pulse interference identification data; Step S2534: performing sweeping frequency interference identification based on the frequency-time variation mode on the high-resolution spectrum data, thereby obtaining sweeping frequency interference identification data; Step S2535: Combine the broadband interference identification data, the pulse interference identification data, the swept frequency interference identification data and the narrowband interference identification data into comprehensive interference identification data.

7. The high-gain satellite navigation antenna anti-interference signal control method according to claim 1, characterized in that: Step S31 includes the following steps: Step S311: classifying the interference type of the interference feature data, including narrowband interference, broadband interference, pulse interference and swept frequency interference, so as to obtain interference type classification data; Step S312: establishing an anti-interference algorithm decision tree according to the interference type classification data, thereby obtaining algorithm decision tree data; Step S313: performing optimal anti-interference algorithm matching for various types of interference based on the algorithm decision tree data, thereby obtaining algorithm matching result data; Step S314: Conflict detection and priority sorting are performed on the algorithm matching result data to obtain optimized algorithm selection result data.

8. The high-gain satellite navigation antenna anti-interference signal control method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: establishing an anti-interference performance index system based on signal-to-noise ratio improvement, interference suppression and signal distortion according to the anti-interference effect evaluation data, thereby obtaining performance index data; Step S42: performing sensitivity analysis on anti-interference algorithm parameters according to the performance indicator data, thereby obtaining parameter sensitivity data; Step S43: constructing an adaptive optimization objective function using parameter sensitivity data, and performing parameter optimization to obtain an optimized parameter set; Step S44: determining the optimization parameter space according to the parameter sensitivity data, and setting the parameter value range and constraint conditions, thereby obtaining parameter optimization configuration data; Step S45: Initializing the evolutionary algorithm population based on the parameter optimization configuration data to obtain initial population data, wherein initializing the evolutionary algorithm population includes coding scheme design and initial population generation; Step S46: Perform fitness evaluation on the initial population data, calculate the performance index of each individual through simulation test, and thus obtain population fitness data; Step S47: performing selection, crossover and mutation operations based on the population fitness data to generate a new generation of populations, and repeating the fitness evaluation process to obtain evolved population data; Step S48: judging the evolution population data according to the preset termination condition, if it is satisfied, outputting the optimal individual as the optimization parameter set, otherwise returning to step S47 to continue the evolution, wherein the termination condition is specifically set as the signal-to-noise ratio improvement greater than or equal to 15dB and the signal distortion less than or equal to 5%; Step S49: construct an adaptive anti-interference model based on the optimized parameter set to obtain adaptive model data, wherein the adaptive anti-interference model includes a dynamic parameter adjustment mechanism and an algorithm switching strategy; perform secondary processing on the interference suppression signal data using the adaptive anti-interference model to obtain optimized signal data.

9. The high-gain satellite navigation antenna anti-interference signal control method according to claim 1, characterized in that: Step S5 includes the following steps: Step S51: performing multi-phase interpolation processing on the optimized signal data, and reconstructing the signal using a fractional delay filter, thereby obtaining high-precision interpolation data; Step S52: performing singular spectrum analysis on the high-precision interpolation data to identify and remove residual high-frequency interference components, thereby obtaining high sampling rate signal data; Step S53: using the phase-locked loop technology to accurately synchronize the local sinusoidal signal data with the high sampling rate signal data, and performing digital up-conversion processing based on orthogonal modulation, so as to obtain an anti-interference output signal.

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