Satellite navigation spoofing interference suppression method, device, equipment, storage medium and computer program product

Abstract

A satellite navigation spoofing interference suppression method, device, equipment, storage medium and computer program product are disclosed. The satellite navigation spoofing interference suppression method comprises: acquiring satellite signals received by a receiving device, the satellite signals comprising satellite navigation signals transmitted by at least one satellite; inputting the satellite signals into a pre-trained fuzzy KNN algorithm model to identify spoofing signals in the satellite signals; and driving the receiving device to stop receiving the spoofing signals. The satellite signals received by the receiving device are acquired, and then the fuzzy KNN algorithm model is used for classification to determine the spoofing signals in the satellite signals. Spoofing signals in the satellite signals can be identified during the satellite signal receiving stage, and then the receiving device is driven to stop receiving the spoofing signals. Spoofing signals are removed from the satellite signal source, effectively improving the accuracy and anti-interference capability of satellite navigation.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation technology, and in particular to a method, apparatus, device, storage medium, and computer program product for suppressing satellite navigation deception interference. Background Technology

[0002] Satellite navigation, as an important technology in the field of navigation, can provide global users with real-time, all-weather, continuous, and high-precision three-dimensional position information.

[0003] However, with the gradual development of various satellite navigation jamming and spoofing technologies, jamming and spoofing signals pose severe challenges to the accuracy, continuity, and integrity of satellite navigation systems. In particular, spoofing jamming seriously threatens the effective application of satellite navigation system positioning performance. Furthermore, current satellite receivers do not distinguish between despread and tracking code phase values; the measured values ​​are directly used for corresponding positioning calculations. This provides opportunities for satellite spoofing jammers to achieve their deceptive objectives. Consequently, most satellite spoofing signal detection methods employ multi-antenna identification during the radio frequency signal processing stage, which requires sophisticated hardware design and is difficult to implement. Summary of the Invention

[0004] This invention provides a method, apparatus, device, storage medium, and computer program product for suppressing satellite navigation spoofing interference, so as to effectively identify and suppress spoofing interference signals in satellite navigation.

[0005] According to one aspect of the present invention, a method for suppressing satellite navigation spoofing interference is provided, comprising:

[0006] Acquire satellite signals received by the receiving device, the satellite signals including satellite navigation signals transmitted by at least one satellite;

[0007] The satellite signal is input into a pre-trained fuzzy KNN algorithm model to identify spoofing signals in the satellite signal;

[0008] The receiving device is then driven to stop receiving the spoofing signal.

[0009] Optionally, acquiring the satellite signal received by the receiving device includes:

[0010] Acquire the carrier signal received by the receiving device;

[0011] The carrier signal is demodulated to obtain a satellite navigation signal transmitted by at least one satellite, which is used as a satellite signal.

[0012] Optionally, demodulating the carrier signal to obtain a satellite navigation signal transmitted by at least one satellite, as a satellite signal, includes:

[0013] The carrier signal is demodulated to obtain a satellite navigation signal transmitted by at least one satellite;

[0014] The acquisition threshold correlation peak count, signal power, correlation peak half width at half maximum (FWHM), Doppler offset, and Doppler rate of change of at least one of the satellite navigation signals received by the receiving device are obtained.

[0015] The satellite navigation signal, the number of correlation peaks at the acquisition threshold, the signal power, the full width at half maximum (FWHM) of the correlation peaks, the Doppler offset, and the Doppler rate of change are used as the satellite signal.

[0016] Optionally, the step of using the satellite navigation signal, the number of acquisition threshold correlation peaks, the signal power, the full width at half maximum (FWHM) of the correlation peaks, the Doppler offset, and the Doppler rate of change as the satellite signal includes:

[0017] The satellite navigation signal, the number of acquisition threshold correlation peaks, the signal power, the full width at half maximum (FWHM) of the correlation peaks, the Doppler offset, and the Doppler rate of change are concatenated into a multidimensional vector;

[0018] The multidimensional vector is used as a satellite signal.

[0019] Optionally, inputting the satellite signal into a pre-trained fuzzy KNN algorithm model to identify spoofing signals in the satellite signal includes:

[0020] The satellite signals are input into a pre-trained fuzzy KNN algorithm model to classify each satellite navigation signal in the satellite signals, thereby obtaining spoofing signals and navigation signals.

[0021] According to another aspect of the present invention, a satellite navigation spoofing interference suppression device is provided, comprising:

[0022] The acquisition module is used to acquire satellite signals received by the receiving device, the satellite signals including satellite navigation signals transmitted by at least one satellite;

[0023] The classification module is used to perform the task of inputting the satellite signal into a pre-trained fuzzy KNN algorithm model to identify spoofing signals in the satellite signal;

[0024] The driver module is used to drive the receiving device to stop receiving the spoofing signal.

[0025] Optionally, the acquisition module includes:

[0026] The acquisition unit is used to acquire the carrier signal received by the receiving device;

[0027] The demodulation unit is used to demodulate the carrier signal to obtain a satellite navigation signal transmitted by at least one satellite, as a satellite signal.

[0028] According to another aspect of the present invention, a satellite navigation spoofing interference suppression device is provided, the satellite navigation spoofing interference suppression device comprising:

[0029] At least one processor; and

[0030] A memory communicatively connected to the at least one processor; wherein,

[0031] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the satellite navigation spoofing interference suppression method according to any embodiment of the present invention.

[0032] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the satellite navigation spoofing interference suppression method according to any embodiment of the present invention.

[0033] According to another aspect of the present invention, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the satellite navigation spoofing interference suppression method according to any embodiment of the present invention.

[0034] The technical solution of this invention acquires satellite signals received by the receiving device, and then uses a fuzzy KNN algorithm model to classify and identify spoofing signals in the satellite signals. This enables the identification of spoofing signals in the satellite signals during the satellite signal reception stage, and then drives the receiving device to stop receiving the spoofing signals, thereby eliminating spoofing signals from the source of the satellite signals and effectively improving the accuracy and anti-interference capability of satellite navigation.

[0035] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used 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 those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart of a satellite navigation spoofing interference suppression method provided in Embodiment 1 of the present invention;

[0038] Figure 2 This is a schematic diagram of a satellite navigation spoofing interference suppression device according to Embodiment 2 of the present invention;

[0039] Figure 3 This is a schematic diagram of a satellite navigation deception interference suppression device provided in Embodiment 3 of the present invention. Detailed Implementation

[0040] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0041] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0042] Example 1

[0043] Figure 1 This is a flowchart illustrating a satellite navigation spoofing interference suppression method provided in Embodiment 1 of the present invention. This embodiment is applicable to the suppression of spoofing interference signals in satellite navigation signals. The method can be executed by a satellite navigation spoofing interference suppression device, which can be implemented in hardware and / or software. This device can be configured in computer equipment, such as a server, workstation, personal computer, etc. Figure 1 As shown, the method includes:

[0044] S110. Acquire the satellite signal received by the receiving device.

[0045] Among these satellite signals, at least one satellite navigation signal is included, meaning that the receiving device can receive multiple satellite navigation signals simultaneously.

[0046] Generally, satellite navigation refers to the technology of using navigation satellites to provide navigation and positioning for users on the ground, at sea, in the air, and in space. Common examples include GPS navigation and BeiDou navigation. A satellite navigation system consists of three parts: navigation satellites, ground stations, and user positioning equipment.

[0047] Navigation satellites: The space segment of a satellite navigation system, consisting of multiple navigation satellites forming a space navigation network that transmits satellite navigation signals to the ground.

[0048] Ground stations: These stations track, measure, and predict satellite orbits and control and manage the operation of onboard equipment. They typically include tracking stations, telemetry stations, a computing center, an injection station, and a time unification system. Tracking stations track and measure the satellite's position coordinates. Telemetry stations receive telemetry data from the satellite for ground monitoring and analysis of the onboard equipment's operation. The computing center uses this information to calculate the satellite's orbit, predict orbital parameters for the next period, determine the navigation information to be transmitted to the satellite, and then sends this information to the satellite via the injection station.

[0049] User positioning equipment typically consists of a receiver, timer, data preprocessor, computer, and display. It receives weak signals from satellites, demodulates and decodes satellite orbital parameters and timing information, and simultaneously measures navigation parameters (distance, distance difference, and rate of change of distance, etc.). The computer then calculates the user's position coordinates (two-dimensional or three-dimensional) and velocity vector components. User positioning equipment comes in various types, including shipborne, airborne, vehicle-mounted, and single-person carried devices.

[0050] In this step, the received satellite signals are obtained from the receiving device. These satellite signals may include satellite navigation signals transmitted by multiple satellites in the same or different frequency bands.

[0051] In addition, when acquiring satellite signals received by the receiving equipment, parameters such as the number of acquisition threshold correlation peaks, power, half width at half maximum (FWHM) of the correlation peaks, Doppler offset, and Doppler rate of change of the captured satellite signals can also be acquired simultaneously.

[0052] S120. Input the satellite signal into a pre-trained fuzzy KNN algorithm model to identify spoofing signals in the satellite signal.

[0053] The fuzzy KNN algorithm is an improvement on the standard KNN algorithm (k-Nearest Neighbor, KNN), offering significantly better performance in terms of accuracy. The difference between this method and traditional KNN is that it fuzzifies the distances between an unknown sample and its k nearest neighbors, then assigns a membership degree to each class. This method pre-computes class membership using the training set, and then calculates the KNN for each sample in the test set. Its basic classification principle is that when classifying a new sample, it only needs to find the k most similar samples from the training dataset, and then determine the class of the unknown sample based on the classes of these k most similar samples. Therefore, the KNN method is intuitive, requires no prior statistical knowledge, and is unsupervised, making it an important non-parametric classification algorithm. Furthermore, the KNN method does not rely on class boundaries to determine the class of a sample; instead, it primarily relies on a finite number of neighboring samples for the determination.

[0054] In this embodiment of the invention, the satellite signals obtained in the aforementioned steps are input into a pre-trained fuzzy KNN algorithm model to classify satellite navigation signals and identify spoofing signals. Since spoofing signals are transmitted by signal relay devices, which receive different satellite signals, deceive them, and then transmit them together, different satellite signals and different signal relay devices result in different spoofing signals, meaning different classifications of spoofing signals. Therefore, accurately defining the boundary conditions for spoofing signals is difficult. Thus, based on the parameters of known spoofing signals (training samples), and according to the sample membership degree (which can be understood as the strong correlation of spoofing signals; the higher the membership degree, the stronger the correlation of spoofing signals belonging to that category),...

[0055] In the specific implementation, the training samples are known deception signal samples with the same vector dimension as the test samples. After sampling, the sampling device classifies the test samples one by one. That is, it imports the vector of the test sample Xi, obtains the K training samples with the smallest Euclidean distance to the test sample Xi, and calculates the membership degree of the corresponding category for the K training samples. Finally, it assigns weights according to the distance and calculates the membership degree of the test sample Xi in the corresponding category. The membership degree of the test sample Xi is compared with a preset threshold range. If the membership degree of the test sample Xi is within the preset threshold range, then the test sample Xi is a deception signal of that category.

[0056] S130, drive the receiving device to stop receiving spoofing signals.

[0057] In the aforementioned steps, the various satellite navigation signals in the satellite signals are classified using the fuzzy KNN algorithm model. Once the spoofing signals in the satellite signals are identified, the corresponding drive signal can be sent to the receiving device in this step to drive the receiving device to stop receiving the spoofing signal, thereby eliminating the spoofing signal from the source of the satellite signal.

[0058] By acquiring satellite signals received by the receiving device and then using the fuzzy KNN algorithm model to classify and identify spoofing signals in the satellite signals, spoofing signals in the satellite signals can be identified during the satellite signal reception stage. Then, the receiving device can be driven to stop receiving the spoofing signal, thereby eliminating the spoofing signal from the source of the satellite signal and effectively improving the accuracy and anti-interference capability of satellite navigation.

[0059] In this embodiment of the invention, S110 may include:

[0060] S111. Obtain the carrier signal received by the receiving device.

[0061] A carrier signal is a radio wave of a specific frequency that can broadcast modulated pseudo-code and data code (satellite signals) in the form of a sine wave.

[0062] S112. Demodulate the carrier signal to obtain a satellite navigation signal transmitted by at least one satellite, which is used as a satellite signal.

[0063] In this embodiment of the invention, the receiving device can simultaneously receive satellite navigation signals transmitted by multiple satellites, and all the satellite navigation signals received by the receiving device are collectively referred to as satellite signals. Therefore, in this step, it is necessary to demodulate the satellite signals to obtain the satellite navigation signals transmitted by each satellite.

[0064] Furthermore, S112 may include:

[0065] S1121. Demodulate the carrier signal to obtain a satellite navigation signal transmitted by at least one satellite.

[0066] S1122. Acquire the number of acquisition threshold correlation peaks, signal power, correlation peak half-width, Doppler offset, and Doppler rate of change of at least one satellite navigation signal received by the receiving device.

[0067] S1123. The satellite navigation signal, the number of correlation peaks at the acquisition threshold, the signal power, the half-width at half-maximum of the correlation peak, the Doppler offset, and the Doppler rate of change are taken as the satellite signal.

[0068] Furthermore, the satellite navigation signal, the number of correlation peaks at the acquisition threshold, the signal power, the half-width at half-maximum (FWHM) of the correlation peaks, the Doppler offset, and the Doppler rate of change are concatenated into a multi-dimensional vector; this multi-dimensional vector is then used as the satellite signal.

[0069] Among them, the detection of the number of correlation peaks at the capture threshold is a common spoofing interference detection method. Its detection principle is as follows: when the number of correlation peaks is 0, there is no navigation signal; when the number of correlation peaks is 1 or 2, there may be a spoofing interference signal. Power detection is used when the number of correlation peaks is 2. By comparing the power of the signal with a threshold, if it exceeds the threshold, it is a spoofing signal; otherwise, it is a navigation signal. The correlation peak half-width at half-maximum (FWHM) detection is used when the number of correlation peaks is 1. Due to signal delay, two correlation peaks may merge into one correlation peak. Therefore, by comparing the half-width at half-maximum (FWHM) of the signal with a preset threshold, if it exceeds the threshold, it is a spoofing signal. Doppler offset detection is used after FWHM detection. Similarly, it is compared with a threshold. If it exceeds the threshold, it is a spoofing signal. Doppler rate of change detection is used after Doppler offset detection. Similarly, it is compared with a threshold. If it is less than the threshold, it is a spoofing signal. Accurate detection of deceptive signals requires multiple steps, and the steps are highly correlated. Therefore, the five corresponding parameters can be obtained simultaneously as the vector dimensions of the test sample and imported into the fuzzy KNN algorithm to determine whether the test sample belongs to a deceptive signal.

[0070] Finally, the satellite signals are input into a pre-trained fuzzy KNN algorithm model to classify each satellite navigation signal in the satellite signals, thereby obtaining the deception signal and the navigation signal.

[0071] Assume the sample to be classified is Xi (i = 1, 2, 3, ..., n), and the category is c (c = 1, 2, 3, ..., n) (in this embodiment of the invention, the category can be set as a deception signal, that is, the sample signal that satisfies the parameter conditions is the deception signal). The basic idea of ​​the KNN algorithm is as follows: First, calculate the dissimilarity between the sample to be classified Xi and each training sample according to the magnitude of the Euclidean distance value. Then, select the k data samples with the smallest dissimilarity to the sample to be classified as the k nearest neighbors of Xi. Finally, determine the category of Xi based on the k nearest neighbors of Xi.

[0072] In the specific implementation, the training samples are known deception signal samples with the same vector dimension as the test samples. After sampling, the sampling device classifies the test samples one by one. That is, it imports the vector of the test sample Xi, obtains the K training samples with the smallest Euclidean distance to the test sample Xi, and calculates the membership degree of the corresponding category for the K training samples. Finally, it assigns weights according to the distance and calculates the membership degree of the test sample Xi in the corresponding category. The membership degree of the test sample Xi is compared with a preset threshold range. If the membership degree of the test sample Xi is within the preset threshold range, then the test sample Xi is a deception signal of that category.

[0073] Optionally, if a test sample Xi is detected as a deception signal, the test sample is added to the training sample. The training sample is periodically filtered to remove training samples with low membership in each category, ensuring fast computation and improving the accuracy of deception signal detection.

[0074] Example 2

[0075] Figure 2 This is a schematic diagram of a satellite navigation spoofing interference suppression device provided in Embodiment 2 of the present invention. Figure 2 As shown, the device includes an acquisition module 21, a classification module 22, and a driving module 23. Wherein:

[0076] The acquisition module 21 is used to acquire satellite signals received by the receiving device, including satellite navigation signals transmitted by at least one satellite;

[0077] Classification module 22 is used to perform the task of inputting satellite signals into a pre-trained fuzzy KNN algorithm model to identify spoofing signals in the satellite signals;

[0078] The driver module 23 is used to drive the receiving device to stop receiving spoofing signals.

[0079] Module 21 includes:

[0080] The acquisition unit is used to acquire the carrier signal received by the receiving device;

[0081] The demodulation unit is used to demodulate the carrier signal to obtain the satellite navigation signal transmitted by at least one satellite, as the satellite signal.

[0082] The demodulation unit includes:

[0083] The demodulation subunit is used to demodulate the carrier signal to obtain the satellite navigation signal transmitted by at least one satellite;

[0084] The parameter acquisition subunit is used to acquire the number of acquisition threshold correlation peaks, signal power, correlation peak half width at half maximum, Doppler offset, and Doppler rate of change of at least one satellite navigation signal received by the receiving device.

[0085] The generation subunit is used to perform the operation of taking satellite navigation signals, the number of correlation peaks at the acquisition threshold, signal power, correlation peak half-width, Doppler offset, and Doppler rate of change as satellite signals.

[0086] In the generation sub-unit, the satellite navigation signal, the number of acquisition threshold correlation peaks, signal power, correlation peak half-width at half-maximum, Doppler offset, and Doppler rate of change are used as the satellite signal, including:

[0087] The satellite navigation signal, the number of correlation peaks at the acquisition threshold, the signal power, the full width at half maximum (FWHM) of the correlation peaks, the Doppler offset, and the Doppler rate of change are concatenated into a multidimensional vector.

[0088] Multidimensional vectors are used as satellite signals.

[0089] Classification module 22 includes:

[0090] The classification unit is used to perform the classification of each satellite navigation signal in the satellite signal by inputting the satellite signal into a pre-trained fuzzy KNN algorithm model, thereby obtaining the deception signal and the navigation signal.

[0091] The satellite navigation deception interference suppression device provided in the embodiments of the present invention can execute the satellite navigation deception interference suppression method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0092] Example 3

[0093] Figure 3 A schematic diagram of a satellite navigation spoofing interference suppression device 10, which can be used to implement embodiments of the present invention, is shown. The satellite navigation spoofing interference suppression device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The satellite navigation spoofing interference suppression device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0094] like Figure 3 As shown, the satellite navigation spoofing interference suppression device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the satellite navigation spoofing interference suppression device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0095] Multiple components in the satellite navigation spoofing interference suppression device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless transceiver, etc. The communication unit 19 allows the satellite navigation spoofing interference suppression device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0096] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as satellite navigation spoofing interference suppression methods.

[0097] In some embodiments, the satellite navigation spoofing interference suppression method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed onto the satellite navigation spoofing interference suppression device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the satellite navigation spoofing interference suppression method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the satellite navigation spoofing interference suppression method by any other suitable means (e.g., by means of firmware).

[0098] In some embodiments, the satellite navigation spoofing interference suppression method can be implemented as a computer program, a computer program product, the computer program product including a computer program that, when executed by a processor, implements the various methods and processes described above, such as the satellite navigation spoofing interference suppression method.

[0099] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0100] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0101] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0102] To provide user interaction, the systems and techniques described herein can be implemented on a satellite navigation spoofing and interference suppression device, which includes: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the satellite navigation spoofing and interference suppression device. Other types of devices can also be used to provide user interaction; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0103] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0104] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0105] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0106] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for suppressing satellite navigation spoofing interference, characterized in that, include: Acquire satellite signals received by the receiving device, the satellite signals including satellite navigation signals transmitted by at least one satellite; The satellite signal is input into a pre-trained fuzzy KNN algorithm model to identify spoofing signals in the satellite signal; The step of inputting the satellite signal into a pre-trained fuzzy KNN algorithm model to identify spoofing signals in the satellite signal includes: The satellite signals are input into a pre-trained fuzzy KNN algorithm model to classify each satellite navigation signal in the satellite signals, thereby obtaining spoofing signals and navigation signals; The step of inputting the satellite signals into a pre-trained fuzzy KNN algorithm model to classify each satellite navigation signal in the satellite signals to obtain spoofing signals and navigation signals includes: The training samples are known deception signal samples with the same vector dimension as the test samples. After sampling, the sampling device classifies the test samples one by one. That is, it imports the vector of the test sample, obtains the K training samples with the smallest Euclidean distance to the test sample, and calculates the membership degree of the corresponding category for the K training samples. Finally, it assigns weights according to the distance and calculates the membership degree of the test sample in the corresponding category. The membership degree of the test sample is compared with a preset threshold range. If the membership degree of the test sample is within the preset threshold range, then the test sample is a deception signal of that category. If test sample Xi is detected as a deceptive signal, the test sample is added to the training sample. The training sample is periodically filtered to remove training samples with low membership in each category. The receiving device is then driven to stop receiving the spoofing signal.

2. The satellite navigation spoofing interference suppression method according to claim 1, characterized in that, The acquisition of satellite signals received by the receiving device includes: Acquire the carrier signal received by the receiving device; The carrier signal is demodulated to obtain a satellite navigation signal transmitted by at least one satellite, which is used as a satellite signal.

3. The satellite navigation spoofing interference suppression method according to claim 2, characterized in that, The demodulation of the carrier signal to obtain a satellite navigation signal transmitted by at least one satellite, as a satellite signal, includes: The carrier signal is demodulated to obtain a satellite navigation signal transmitted by at least one satellite; The acquisition threshold correlation peak count, signal power, correlation peak half width at half maximum (FWHM), Doppler offset, and Doppler rate of change of at least one of the satellite navigation signals received by the receiving device are obtained. The satellite navigation signal, the number of correlation peaks at the acquisition threshold, the signal power, the full width at half maximum (FWHM) of the correlation peaks, the Doppler offset, and the Doppler rate of change are used as the satellite signal.

4. The satellite navigation spoofing interference suppression method according to claim 3, characterized in that, The method of using the satellite navigation signal, the number of correlation peaks at the acquisition threshold, the signal power, the full width at half maximum (FWHM) of the correlation peaks, the Doppler offset, and the Doppler rate of change as the satellite signal includes: The satellite navigation signal, the number of acquisition threshold correlation peaks, the signal power, the full width at half maximum (FWHM) of the correlation peaks, the Doppler offset, and the Doppler rate of change are concatenated into a multidimensional vector; The multidimensional vector is used as a satellite signal.

5. A satellite navigation spoofing interference suppression device, used to perform the satellite navigation spoofing interference suppression method according to any one of claims 1-4, characterized in that, include: The acquisition module is used to acquire satellite signals received by the receiving device, the satellite signals including satellite navigation signals transmitted by at least one satellite; The classification module is used to perform the task of inputting the satellite signal into a pre-trained fuzzy KNN algorithm model to identify spoofing signals in the satellite signal; The driver module is used to drive the receiving device to stop receiving the spoofing signal.

6. The satellite navigation spoofing interference suppression device according to claim 5, characterized in that, The acquisition module includes: The acquisition unit is used to acquire the carrier signal received by the receiving device; The demodulation unit is used to demodulate the carrier signal to obtain a satellite navigation signal transmitted by at least one satellite, as a satellite signal.

7. A satellite navigation spoofing interference suppression device, characterized in that, The device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the satellite navigation spoofing interference suppression method according to any one of claims 1-4.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the satellite navigation spoofing interference suppression method according to any one of claims 1-4.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the satellite navigation spoofing interference suppression method according to any one of claims 1-4.

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