Beidou anti-interference deception time service method and system based on multi-layer detection

Abstract

The invention relates to the technical field of satellite time service, and discloses a Beidou anti-interference deception time service method and system based on multilayer detection, and the method comprises the steps: receiving a multi-band signal of a satellite, and recording a noise parameter; performing time frequency domain feature extraction on the multi-band signal, and performing weighted fusion on the extracted time frequency features to obtain a comprehensive feature vector; and establishing a conjoint analysis model, detecting an abnormal signal according to the comprehensive feature vector, performing time calculation on the detected real signal, and outputting time information. According to the method, efficient discrimination and accurate time service output of abnormal signals are ensured, the time service accuracy and stability of a Beidou system in a complex interference scene are remarkably improved, and a reliable time reference is provided for key industries.

Description

Technical Field

[0001] The present invention relates to the field of satellite timing technology, and in particular to a Beidou anti-interference and deception timing method and system based on multi-layer detection. Background Art

[0002] BeiDou Navigation Satellite System (BDS), as a global satellite navigation system independently developed by my country, not only provides high-precision positioning services, but also plays an important role in the field of timing. Timing technology provides a unified and accurate time reference for ground equipment through the propagation characteristics of satellite signals, and is widely used in power dispatching, communication networks, financial transactions and other fields. However, with the widespread popularization of satellite navigation technology, the problem of its signals being susceptible to interference and deception attacks has become more prominent. In complex electromagnetic environments, especially in highly dynamic or high-noise scenarios, interference signals (such as broadband interference, narrowband interference) and forged signals (such as deception attacks) can significantly affect the credibility of satellite signals, resulting in a decrease in timing accuracy, and even causing serious consequences such as time synchronization failure.

[0003] In order to meet the above challenges, existing technologies have introduced a variety of anti-interference and anti-spoofing timing methods. For example, the interference suppression method based on single-frequency signals achieves preliminary judgment by monitoring the signal strength and noise ratio; the multi-band fusion technology attempts to use the complementarity between multi-frequency signals to enhance the anti-interference ability; and the anomaly detection method based on machine learning identifies abnormal signal characteristics through training models. These methods have improved the anti-interference and anti-spoofing capabilities to a certain extent, but their effectiveness and adaptability in complex environments are still limited. For example, single-frequency signal technology cannot completely eliminate multipath interference, the existing multi-band methods lack deep fusion of time and frequency domain features, and the machine learning method faces the problems of single features and poor real-time performance. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that the existing anti-interference timing technology has the problems of single signal feature analysis dimension and insufficient adaptability to environmental changes.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: a Beidou anti-interference and deception timing method based on multi-layer detection, comprising: receiving multi-band signals from satellites and recording noise parameters; extracting time-frequency domain features of the multi-band signals, weightedly fusing the extracted time-frequency features, and obtaining a comprehensive feature vector; establishing a joint analysis model, detecting abnormal signals according to the comprehensive feature vector, performing time resolution on the real signals that pass the detection, and outputting time information.

[0007] As a preferred solution of the Beidou anti-interference and deception timing method based on multi-layer detection described in the present invention, wherein: the multi-band signal includes a B1 band signal, a B2 band signal and a B3 band signal.

[0008] As a preferred solution of the Beidou anti-interference deception timing method based on multi-layer detection described in the present invention, wherein: the noise parameter includes recording the noise parameter during the period of no signal transmission, calculating the noise power, and expressing it as: , in, Indicates frequency band Noise power on Indicates the frequency band index; Indicates the sampling time; Indicates time Time, frequency band The noise signal strength.

[0009] As a preferred solution of the Beidou anti-interference and deception timing method based on multi-layer detection described in the present invention, the time-frequency domain feature extraction includes filtering, denoising and normalizing the multi-band signals, extracting the time domain characteristics of the signals in the time domain, including the phase shift rate, power fluctuation and time delay characteristics; using fast Fourier transform, mapping the signal from the time domain to the frequency domain, and extracting the frequency domain characteristics.

[0010] As a preferred solution of the Beidou anti-interference deception timing method based on multi-layer detection described in the present invention, wherein: the weighted fusion includes calculating the signal power of each frequency band, expressed as: , in, Indicates frequency band The signal power of Indicates frequency band The received original signal; the signal-to-noise ratio is calculated using the noise power and signal power , expressed as: , Using the characteristic of signal-to-noise ratio reflecting signal quality, the signal-to-noise ratio is normalized to calculate the weight, which intuitively reflects the contribution ratio of each frequency band signal to the comprehensive feature vector, expressed as: , in, Indicates frequency band The weighting coefficient of Represents the sum of the signal-to-noise ratios of all frequency bands.

[0011] As a preferred solution of the Beidou anti-interference deception timing method based on multi-layer detection described in the present invention, wherein: the weighted fusion also includes, according to the weighted coefficient The time domain features and frequency domain features are weighted and expressed as: , , in, Indicates frequency band The time domain weighted features; Indicates frequency band The time domain feature vector of Indicates frequency band The frequency domain weighted features of Indicates frequency band The frequency domain feature vector of ; the weighted fusion of time domain and frequency domain features to obtain the comprehensive feature vector, expressed as: , in, represents the comprehensive feature vector; Represents the fusion weight of time domain features; Represents the fusion weight of frequency domain features; and Dynamically adjust based on signal-to-noise ratio.

[0012] As a preferred solution of the Beidou anti-interference deception timing method based on multi-layer detection described in the present invention, the joint analysis model includes: based on the logistic regression model, using the gradient descent method to optimize the logistic regression loss function, establishing a joint analysis model, and performing comprehensive feature vector Perform anomaly detection and obtain anomaly probability .

[0013] A Beidou anti-interference and deception timing system based on multi-layer detection adopting any of the methods described in the present invention, wherein: an acquisition module receives multi-band signals from satellites, records noise parameters and calculates noise power during a period of no signal transmission, and uploads the noise power to an analysis module; the analysis module extracts time and frequency characteristics of the multi-band signals and performs weighted fusion, calculates the signal-to-noise ratio according to the noise parameters, performs dynamic adjustment, and obtains a comprehensive feature vector; and a timing module establishes a joint analysis model based on a logistic regression model, detects abnormal signals according to the comprehensive feature vector, performs time resolution on real signals that pass the detection, and outputs time information.

[0014] A computer device comprises: a memory and a processor; the memory stores a computer program, comprising: the steps of implementing any one of the methods of the present invention when the processor executes the computer program.

[0015] A computer-readable storage medium stores a computer program, comprising: when the computer program is executed by a processor, the steps of implementing any one of the methods of the present invention are implemented.

[0016] Beneficial effects of the present invention: The method of the present invention ensures the accuracy and integrity of the basic data of anti-interference processing by receiving multi-band signals and recording noise parameters, which helps to dynamically adapt to interference changes in complex environments; generates a comprehensive feature vector by extracting features in the time and frequency domain and weighted fusion, thereby realizing in-depth analysis and optimized expression of the multi-dimensional characteristics of the signal and enhancing the robustness of signal discrimination; finally, uses a joint analysis model to perform anomaly detection and timing solution on the comprehensive feature vector, ensuring efficient discrimination of abnormal signals and precise timing output, significantly improving the timing accuracy and stability of the Beidou system in complex interference scenarios, and providing a reliable time reference for key industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 An overall flow chart of a Beidou anti-interference and deception timing method based on multi-layer detection provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0020] Example 1, reference Figure 1 , is an embodiment of the present invention, and provides a Beidou anti-interference and deception timing method based on multi-layer detection, comprising: S1: Receive multi-band signals from satellites and record noise parameters.

[0021] Furthermore, multi-band signals refer to navigation signals sent by navigation satellites (such as the Beidou system) through radio waves of different frequencies. Multi-band signals usually operate on different frequency bands, and each frequency band has a specific purpose, such as positioning, timing or enhancing navigation accuracy.

[0022] The signals of the Beidou navigation system are mainly distributed in the B1 band, B2 band and B3 band. Specifically, the frequency range of the B1 band is 1559.052MHz-1563.144MHz (the center frequency is 1575.42MHz). The general timing function is implemented based on the B1 band signal. At the same time, the B1 signal is suitable for a wide range of daily use; the B2 signal performs better in high-dynamic scenarios (such as aviation and drones); the B3 signal provides additional enhancements and is suitable for high precision and high security requirements.

[0023] Different from general timing technology, considering the different effects of signals in different frequency bands when propagating in the atmosphere, the method of the present invention not only uses the B1 frequency band for timing, but also weights and combines multi-frequency band signals to calculate the comprehensive signal characteristics, and performs timing solution to obtain accurate time after eliminating interference. The combined use can effectively reduce the multipath effect and ionospheric interference, and at the same time can provide more signal redundancy and selection, and improve the signal's anti-interference ability.

[0024] Furthermore, the noise parameter includes recording the noise parameter during the period of no signal transmission. The period of no signal transmission is determined based on the signal power. Specifically, during the signal reception process, the power value of the received signal is calculated in real time, which is expressed as: , in, Indicates the power value of the received signal; Indicates the sampling window time length; Indicates the received signal; and noise reference power For comparison, if near And within the preset threshold range, it is determined as a period of no signal transmission.

[0025] The noise power is calculated based on the recorded noise parameters and is expressed as: , in, Indicates frequency band Noise power on Indicates the frequency band index; Indicates the sampling time; Indicates time Time, frequency band The noise signal strength.

[0026] It should be noted that noise parameters may not only be collected during the period of no signal transmission, but collecting noise parameters during the period of no signal transmission is the most direct and accurate method, because the collected signal is completely composed of environmental noise and does not contain the main signal or interference signal. In a dynamic environment or when it is impossible to determine the period of no signal, noise parameters can also be estimated during the signal transmission period by signal separation or statistical methods.

[0027] S2: Extract time-frequency domain features of multi-band signals, perform weighted fusion on the extracted time-frequency features, and obtain a comprehensive feature vector.

[0028] Furthermore, the time-frequency domain feature extraction includes filtering, denoising and normalizing the multi-band signal, and extracting the time domain features of the signal in the time domain, including phase shift rate, power fluctuation and time delay characteristics. Specifically, the phase shift rate reflects the speed of phase change, and the instantaneous phase of the received Beidou signal is extracted using the phase shift rate formula: , in, represents the instantaneous phase; Represents the instantaneous angular frequency.

[0029] The change of signal power reflects the stability of signal strength. Calculate the instantaneous power of the signal , perform sliding average on the instantaneous power to obtain power fluctuation: , in, Indicates power fluctuation. The time delay characteristic is obtained by measuring the pseudorange of the signal and comparing the pseudorange with the true signal pseudorange.

[0030] Furthermore, the signal is mapped from the time domain to the frequency domain using a fast Fourier transform to extract frequency domain features. In this embodiment, the frequency domain features include the main frequency position, bandwidth symmetry, and power spectrum density. The signal is fast Fourier transformed to obtain a spectrum: , The main frequency position is expressed as: , in, represents the spectrum; Represents a signal in the time domain; represents frequency variable; represents the kernel function of Fourier transform; Indicates the main frequency position; Indicates the use The parameter that reaches its maximum value.

[0031] The bandwidth symmetry and power spectral density are calculated as: , , in, Indicates bandwidth symmetry; Represents the highest frequency in the signal spectrum; Represents the lowest frequency in the signal spectrum; represents the power spectral density.

[0032] Furthermore, the extracted time-frequency features are weighted and fused to calculate the signal power of each frequency band, which is expressed as: , in, Indicates frequency band The signal power of Indicates frequency band The original signal received.

[0033] Calculate signal-to-noise ratio using noise power and signal power , expressed as: , Using the characteristic of signal-to-noise ratio reflecting signal quality, the signal-to-noise ratio is normalized to calculate the weight to ensure The value range of is between 0 and 1. The normalized weight can intuitively reflect the contribution ratio of each frequency band signal to the comprehensive feature vector, which is expressed as: , in, Indicates frequency band The weighting coefficient of Represents the sum of the signal-to-noise ratios of all frequency bands.

[0034] It should be noted that It is the ratio of signal strength to noise strength, indicating the clarity and anti-interference ability of the signal. The higher it is, the less interference the signal in this frequency band is subject to, the better the quality is, and the more reliable its characteristics are.

[0035] Noise and interference are unevenly distributed in different scenarios. Some frequency bands may be more interfered in certain scenarios (such as urban environments or high-dynamic scenarios). Dynamic Adjustment , can adapt to the current environment, give priority to the use of higher quality signal frequency bands, and dynamically weighted to avoid excessive impact of interfered frequency bands on the comprehensive feature vector, thereby improving the reliability of the system in complex environments.

[0036] According to the weighting factor The time domain features and frequency domain features are weighted and expressed as: , , in, Indicates frequency band The time domain weighted features; Indicates frequency band The time domain feature vector of Indicates frequency band The frequency domain weighted features of Indicates frequency band The frequency domain feature vector of .

[0037] Time domain characteristics Directly reflects the dynamic behavior of the signal when it is received, suitable for capturing instantaneous interference and abnormal changes, and plays an important role in rapid interference detection and signal authenticity analysis; frequency domain characteristics It reflects the overall frequency distribution state of the signal and is suitable for detecting counterfeit signals and interference signals in the frequency domain. The frequency domain characteristics are of high value for the analysis of long-term interference and spectrum anomalies.

[0038] By weighted fusion of time domain and frequency domain features, the complementary advantages of the two features can be fully utilized to improve the accuracy of abnormal signal detection, which can be expressed as: , in, represents the comprehensive feature vector; Represents the fusion weight of time domain features; Represents the fusion weight of frequency domain features.

[0039] and Adjust according to the signal-to-noise ratio, or set static according to specific scenes and For example, in high-speed dynamic scenes, the weight of time domain features is increased. ; Increase the weight of frequency domain features in a stable environment , in this embodiment, and The calculation method is expressed as: , , in, Indicates the rate of change of time domain characteristics. The greater the rate of change, the stronger the dynamic characteristics of the signal; It indicates the rate of change of frequency domain characteristics. The smaller the rate of change, the more stable the signal spectrum characteristics are.

[0040] It should be noted that Dynamic adjustment based on signal-to-noise ratio reflects the importance of signal quality to features. and By adjusting the contribution ratio of time domain and frequency domain features, this hierarchical design fully utilizes the advantages of multi-band signals, while enhancing the adaptability and accuracy of the comprehensive feature vector for anomaly detection and timing output.

[0041] S3: Establish a joint analysis model, detect abnormal signals based on the comprehensive feature vector, perform time calculation on the real signal that passes the detection, and output time information.

[0042] Furthermore, based on the logistic regression model, a joint analysis model is established to detect anomalies on the received signals, which is expressed as: , in, Indicates the probability that the signal is abnormal; Represents the model weight vector, which is learned through training data; Represents a mathematical constant; represents the transposed matrix; Represents the bias term.

[0043] To train the joint analysis model, collect annotated data sets containing normal signals and abnormal signals (such as interference signals or deception signals), normalize the input features, and use the gradient descent method to optimize the logistic regression loss function: , in, represents the total number of samples; represents the loss function; Indicates The labels of samples.

[0044] Set anomaly thresholds based on historical data ,Will and Make a comparison to determine the degree of interference of the signal. It can also be set to different ranges or multiple layers of thresholds, such as is 0.4, is 0.7, when It is a normal signal when Indicates that the signal is slightly disturbed, and the signal is recorded and marked as a potential anomaly, but the timing calculation is still attempted; when Indicates that the signal is severely interfered with, timing calculation is suspended or rejected, and an alarm is optionally triggered.

[0045] Furthermore, the signal judged as normal by the joint analysis model is time-solved and high-precision time information is output. The pseudorange of the navigation signal is calculated and corrected according to the clock error information broadcast by the satellite. The pseudorange is further corrected considering the ionosphere and troposphere delays. Based on the pseudorange and satellite position, the time offset between the receiver and the satellite is calculated using the Doppler effect to perform time solution.

[0046] The time solution results of all frequency band signals are integrated, and the final high-precision time is output using the weighted average method: , in, Indicates the time of final output; Indicates frequency band The calculated time.

[0047] This embodiment also provides a Beidou anti-interference and deception timing system based on multi-layer detection, including: an acquisition module, which receives multi-band signals from satellites, records noise parameters and calculates noise power during periods of no signal transmission, and uploads the noise to an analysis module; an analysis module, which extracts time and frequency characteristics of multi-band signals and performs weighted fusion, calculates the signal-to-noise ratio based on noise parameters, performs dynamic adjustment, and obtains a comprehensive feature vector; a timing module, which establishes a joint analysis model based on a logistic regression model, detects abnormal signals based on comprehensive feature vectors, performs time resolution on real signals that pass detection, and outputs time information.

[0048] Example 2: The following is an embodiment of the present invention, which provides a Beidou anti-interference and deception timing method based on multi-layer detection. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0049] In order to verify the innovation and advantages of the method of the present invention, a comprehensive analysis experiment was conducted on the B1, B2, and B3 frequency band signals of the Beidou navigation system. A high-sensitivity receiver was used to simultaneously receive the B1, B2, and B3 frequency band signals, and the signal power was used to monitor the period of no signal transmission, and the noise parameters were recorded during this period. The noise power recorded in the experiment was -115dBm, which corresponds to the background noise level in an urban environment. The experimental data is shown in Table 1.

[0050] Table 1 Experimental data table , From the table data, we can see that the signal-to-noise ratio (SNR) directly reflects the quality of the signals in each frequency band. The B2 frequency band has the highest SNR (25 dB), so its weight www is the highest (0.5), which indicates that in high-dynamic scenarios, the B2 signal contributes the most. Compared with B1 (20 dB) and B3 (23 dB), the B2 signal has significant advantages in anti-interference and enhanced navigation accuracy.

[0051] The time domain features and frequency domain features of each frequency band are significantly different. The comprehensive time domain features and frequency domain features of B2 are 0.8 and 0.9 respectively, which are higher than those of other frequency bands. After weighted fusion, the comprehensive feature vector values ​​are 0.68 (time domain) and 0.75 (frequency domain), which are close to the theoretical optimal values, indicating that the method of the present invention can significantly improve the accuracy of feature fusion.

[0052] After comprehensive processing of multi-band signals, the final time solution accuracy reached 15 ns, which is significantly better than using only single-band signals (theoretical value 20 ns), proving the effectiveness of multi-band weighted fusion in resisting interference and improving time accuracy in complex environments.

[0053] The method of the present invention effectively reduces the multipath effect and ionospheric interference and improves the anti-interference capability by dynamically weighting and fusing the characteristics of multi-band signals. Compared with the prior art, the present invention can adjust the weight of each frequency band signal in real time, give priority to high-quality signals, and ensure the stability of timing accuracy in complex environments.

[0054] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program code.

[0055] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0056] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0057] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0058] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A Beidou anti-interference and deception timing method based on multi-layer detection, characterized in that: include: Receive multi-band signals from satellites and record noise parameters; Extract time-frequency domain features from multi-band signals, perform weighted fusion on the extracted time-frequency features, and obtain a comprehensive feature vector; Establish a joint analysis model, detect abnormal signals based on comprehensive feature vectors, perform time calculation on real signals that pass the detection, and output time information; The joint analysis model is based on the logistic regression model to establish a joint analysis model to perform anomaly detection on the received signal, which is expressed as: ， in, Indicates the probability that the signal is abnormal; Represents the model weight vector, which is learned through training data; Represents a mathematical constant; represents the transposed matrix; represents the bias term; Train the joint analysis model, collect labeled data sets containing normal and abnormal signals, normalize the input features, and use the gradient descent method to optimize the logistic regression loss function: ， in, represents the total number of samples; represents the loss function; Indicates The labels of samples.

2. The Beidou anti-interference deception timing method based on multi-layer detection as claimed in claim 1 is characterized in that: The multi-band signal includes a B1 frequency band signal, a B2 frequency band signal and a B3 frequency band signal.

3. The Beidou anti-interference deception timing method based on multi-layer detection as claimed in claim 2 is characterized in that: The noise parameters include recording noise parameters and calculating noise power during a period of no signal transmission, which is expressed as: ， in, Indicates frequency band Noise power on Indicates the frequency band index; Indicates sampling time; Indicates time Time, frequency band The noise signal strength.

4. The Beidou anti-interference and deception timing method based on multi-layer detection as claimed in claim 3 is characterized in that: The time-frequency domain feature extraction includes filtering, denoising and normalizing the multi-band signal, and extracting the time-domain features of the signal in the time domain, including phase shift rate, power fluctuation and time delay characteristics; Fast Fourier transform is used to map the signal from time domain to frequency domain and extract frequency domain features.

5. The Beidou anti-interference and deception timing method based on multi-layer detection as claimed in claim 4 is characterized in that: The weighted fusion includes calculating the signal power of each frequency band, which is expressed as: ， in, Indicates frequency band The signal power of Indicates frequency band The original signal received; Calculate signal-to-noise ratio using noise power and signal power , expressed as: ， Using the characteristic of signal-to-noise ratio reflecting signal quality, the signal-to-noise ratio is normalized to calculate the weight, which intuitively reflects the contribution ratio of each frequency band signal to the comprehensive feature vector, expressed as: ， in, Indicates frequency band The weighting coefficient of Represents the sum of the signal-to-noise ratios of all frequency bands.

6. The Beidou anti-interference and deception timing method based on multi-layer detection as claimed in claim 5 is characterized in that: The weighted fusion also includes, according to the weighted coefficient The time domain features and frequency domain features are weighted and expressed as: ， ， in, Indicates frequency band The time domain weighted features; Indicates frequency band The time domain feature vector of Indicates frequency band The frequency domain weighted features of Indicates frequency band The frequency domain feature vector of The weighted fusion of time domain and frequency domain features is used to obtain a comprehensive feature vector, which is expressed as: ， in, represents the comprehensive feature vector; Represents the fusion weight of time domain features; Represents the fusion weight of frequency domain features; and Dynamically adjust based on signal-to-noise ratio.

7. The Beidou anti-interference and deception timing method based on multi-layer detection as claimed in claim 6 is characterized in that: The joint analysis model includes: based on the logistic regression model, using the gradient descent method to optimize the logistic regression loss function, establishing a joint analysis model, and calculating the comprehensive feature vector Perform anomaly detection and obtain anomaly probability .

8. A Beidou anti-interference and deception timing system based on multi-layer detection using any of the methods of claims 1 to 7, characterized in that: include, The acquisition module receives the multi-band signals from the satellite, records the noise parameters and calculates the noise power during the period without signal transmission, and uploads it to the analysis module; The analysis module extracts the time-frequency characteristics of multi-band signals and performs weighted fusion, calculates the signal-to-noise ratio according to the noise parameters, performs dynamic adjustment, and obtains the comprehensive feature vector; The timing module establishes a joint analysis model based on the logistic regression model, detects abnormal signals according to the comprehensive feature vector, performs time calculation on the real signals that pass the detection, and outputs the time information.

9. A computer device comprising: A memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the Beidou anti-interference and deception timing method based on multi-layer detection as described in any one of claims 1-7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the Beidou anti-interference and deception timing method based on multi-layer detection are implemented as described in any one of claims 1-7.

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