A satellite signal analysis system

Abstract

The embodiment of the specification provides a satellite signal analysis system, comprising: a GNSS high-gain satellite decoding antenna, a power distributor, a GNSS frequency band high-gain Yagi direction-finding antenna, a low-noise radio frequency amplifier, a zero intermediate frequency receiver, a GNSS data analysis module and a central processing unit; the power distributor is in communication connection with the GNSS high-gain satellite decoding antenna and the GNSS data analysis module respectively; the low-noise radio frequency amplifier is in communication connection with the GNSS frequency band high-gain Yagi direction-finding antenna and the zero intermediate frequency receiver respectively; the zero intermediate frequency receiver is in communication connection with the power distributor and the central processing unit respectively; and the central processing unit is in communication connection with the GNSS data analysis module.

Description

[0001] Case Division Explanation:

[0002] This application is a divisional application of Chinese application filed on August 22, 2022, with application number 202211001940.3 and entitled "A Portable Satellite Navigation Interference Detection and Positioning System". Technical Field

[0003] This manual relates to the fields of GNSS satellite navigation, radio detection, and radio detection and positioning, and in particular to a satellite signal analysis system. Background Technology

[0004] GNSS (Global Navigation Satellite System) is a high-precision radio navigation and positioning system based on artificial Earth satellites. It is a space-based radio navigation and positioning system that can provide users with all-weather 3D coordinates, velocity, and time information at any location on the Earth's surface or in near-Earth space. Therefore, the detection and location of interference sources for GNSS systems is of great importance.

[0005] There are two main sources of interference to GNSS global navigation satellite systems:

[0006] Pure noise suppression interference, which is divided into CW interference and broadband noise interference, will significantly increase the noise floor of the GNSS receiver, severely degrade the signal-to-noise ratio (S / N), and make positioning impossible.

[0007] Deceptive interference can be divided into two types:

[0008] GPS simulation system: By simulating the operation mechanism of GPS, it tampers with the PRN (pseudo random noise code) of real GNSS satellites, but the intensity is much greater than that of real GNSS satellites, causing the GNSS receiver to locate the wrong position.

[0009] Transponder amplification system: The GPS frequency radio signal received at location A is transmitted to location B via a feeder, amplified, and then transmitted, making the satellite signal appear very "real".

[0010] Existing satellite navigation interference detection and positioning equipment suffers from inaccurate detection data, complex equipment structure, large size, inconvenient operation, and the need for multiple people to work together, thus failing to meet users' needs. Summary of the Invention

[0011] This specification provides one or more embodiments of a satellite signal analysis system, including: a GNSS high-gain satellite decoding antenna, a power divider, a GNSS band high-gain Yagi direction-finding antenna, a low-noise RF amplifier, a zero-IF receiver, a GNSS data parsing module, and a central processing unit; the power divider is communicatively connected to the GNSS high-gain satellite decoding antenna and the GNSS data parsing module; the low-noise RF amplifier is communicatively connected to the GNSS band high-gain Yagi direction-finding antenna and the zero-IF receiver; the zero-IF receiver is communicatively connected to the power divider and the central processing unit; the central processing unit is communicatively connected to the GNSS data parsing module. The module communication connection is as follows: The GNSS high-gain satellite decoding antenna is used to receive GNSS satellite signals and transmit the GNSS satellite signals to the power divider; the power divider is used to divide the GNSS satellite signals into two paths according to signal energy to obtain a first signal and a second signal; and transmits the first signal to the GNSS data parsing module and the second signal to the zero intermediate frequency receiver; The GNSS band high-gain Yagi direction-finding antenna is used to receive interference signals and transmit the interference signals to the low-noise RF amplifier; The low-noise RF amplifier is used to amplify the interference signals and transmit them to the zero intermediate frequency receiver; The zero-IF receiver is used to: scan the frequency band of the GNSS satellite signal corresponding to the second signal to obtain the spectrum data of the entire frequency band; based on the analysis of the background noise and the amplified interference signal, obtain the signal strength of each frequency point in the spectrum; determine the candidate direction based on the analysis of the signal strength; acquire at least two interference signals collected by the GNSS band high-gain Yagi direction-finding antenna at at least two signal acquisition points; the at least two interference signals are signals collected by the GNSS band high-gain Yagi direction-finding antenna at at least two signal acquisition points with the candidate direction as the test direction; and analyze each interference signal in the at least two interference signals respectively. The system obtains the positioning direction corresponding to each interference signal segment; it summarizes the positioning directions corresponding to all interference signals in the at least two interference signals, and determines the positioning direction with the most overlap as the direction of the interference source; and transmits the obtained data to the central processing unit; the GNSS data parsing module is used to obtain satellite analysis information corresponding to the first signal based on the processing of the first signal as the signal to be analyzed, the satellite analysis information including at least one of the following: number of satellites, satellite signal quality, signal strength, whether the signal is interfered with, the intensity of the interference, whether it is spoofed, and background noise; and transmits the satellite analysis information to the central processing unit;The central processing unit is used to comprehensively process the received information to obtain: the spectral data of the GNSS satellite signal, the parsed data of the GNSS satellite signal, and the analytical data of the GNSS satellite signal. The parsed data includes at least one of UTC time and latitude / longitude; the analytical data includes at least one of interference status, interference intensity, environmental noise, and deception status.

[0012] In some embodiments, the number of acquisition points for collecting interference signals in the multi-point positioning is determined based on a point-to-number prediction model, which is a machine learning model; the point-to-number prediction model obtains the number of acquisition points based on the processing of the spectrum data and the signal strength of each frequency point.

[0013] In some embodiments, the number of acquisition points for collecting interference signals in the multi-point positioning is obtained based on historical records, including: acquiring satellite feature vectors, wherein the vector elements of the satellite feature vectors include the spectrum data and the signal strength of each frequency point; determining historically similar satellite feature vectors based on the satellite feature vectors and historical satellite feature vectors; and determining the number of acquisition points corresponding to the historically similar satellite feature vectors as the number of acquisition points based on historical vector comparison.

[0014] In some embodiments, the number of acquisition points for collecting interference signals in the multi-point positioning is determined by combining the number of acquisition points predicted based on the model and the number of acquisition points based on historical vector comparison, including: assigning certain weight values ​​to the number of acquisition points predicted based on the model and the number of acquisition points based on historical vector comparison, respectively, and taking the weighted sum of the two as the final number of acquisition points; wherein, the weight values ​​are related to the confidence level of the data output by the point number prediction model, and the higher the confidence level, the higher the weight value assigned to the number of acquisition points predicted based on the model.

[0015] In some embodiments, obtaining satellite analysis information corresponding to the first signal includes: determining the number of satellites and the signal quality and signal strength of each satellite based on the analysis of the first signal; determining whether the signal is interfered with and the intensity of the interference based on the number of satellites and the signal quality of each satellite; obtaining at least one positioning reference data, and comparing the positioning data obtained based on the first signal with the at least one positioning reference data to determine whether the satellite is being deceived.

[0016] In some embodiments, determining whether the signal is interfered with and the intensity of the interference is based on an interference prediction model; the interference prediction model is a machine learning model, which determines whether the signal is interfered with and the intensity of the interference based on the processing of the number of satellites and the signal quality of each satellite.

[0017] In some embodiments, the system further includes a user interaction module communicatively connected to the central processing unit, the user interaction module being used to display the analysis results of the central processing unit and obtain user instructions.

[0018] In some embodiments, the system further includes a power module and a communication module that are communicatively connected to the central processing unit. The power module is used to provide power to the portable satellite navigation interference detection and positioning system based on the central processing unit, and the communication module is used to implement the communication function of the portable satellite navigation interference detection and positioning system and to acquire offline data. Attached Figure Description

[0019] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0020] Figure 1 This is a schematic diagram illustrating the application scenarios of a portable satellite navigation interference detection and positioning system according to some embodiments of this specification;

[0021] Figure 2 This is an exemplary structural configuration diagram of a portable satellite navigation interference detection and positioning system according to some embodiments of this specification;

[0022] Figure 3 This is a flowchart illustrating the process of determining the direction of an interference source according to some embodiments of this specification;

[0023] Figure 4 This is a schematic diagram of a point number prediction model according to some embodiments of this specification;

[0024] Figure 5 This is an exemplary flowchart illustrating the acquisition of satellite analysis information corresponding to the first signal according to some embodiments of this specification;

[0025] Figure 6 These are schematic diagrams of some interference prediction models based on this manual;

[0026] Figure 7 This is a schematic diagram of a portable satellite navigation interference detection and positioning device according to some embodiments of this specification. Detailed Implementation

[0027] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0028] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0029] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0030] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0031] Figure 1 This is a schematic diagram of an application scenario 100 of a portable satellite navigation interference detection and positioning system according to some embodiments of this specification.

[0032] In some embodiments, application scenario 100 can be configured as portable satellite navigation interference detection and positioning, etc. It can be applied in corresponding communication control scenarios such as portable satellite navigation interference detection and positioning, radio identification, and radio management.

[0033] Application scenario 100 may include server 110, network 120, user terminal 130, storage device 140, and signal source 150. Server 110 may include processing engine 112. In some embodiments, server 110, user terminal 130, storage device 140, and signal source 150 may be connected to and / or communicate with each other via wireless connection (e.g., network 120), wired connection, or a combination thereof.

[0034] Server 110 can be used to implement portable satellite navigation interference detection and positioning. In some embodiments, it can be specifically used to monitor radio waves such as satellites. This monitoring technology can be applied to many fields such as government departments, defense forces, news media, customs, diplomacy, and wartime communications.

[0035] Server 110 refers to a system with computing capabilities. In some embodiments, server 110 can be a single server or a group of servers. The server group can be centralized or distributed (e.g., server 110 can be a distributed system). In some embodiments, server 110 can be local or remote. For example, server 110 can access information and / or data stored in user terminal 130 and / or storage device 140 via network 120. As another example, server 110 can directly connect to user terminal 130 and / or storage device 140 to access stored information and / or data. In some embodiments, server 110 can be implemented on a cloud platform. By way of example only, the cloud platform can include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, internal cloud, multi-tiered cloud, etc., or any combination thereof.

[0036] In some embodiments, server 110 may include processing engine 112. Processing engine 112 can process information and / or data related to wireless signals. For example, processing engine 112 can implement portable satellite navigation interference detection and positioning from information data acquired by signal source 150. In some embodiments, processing engine 112 may include one or more processing engines (e.g., a single-core processing engine or a multi-core processor). By way of example only, processing engine 112 may include one or more hardware processors, such as central processing unit (CPU), application-specific integrated circuit (ASIC), application-specific instruction set processor (ASIP), graphics processing unit (GPU), physical processing unit (PPU), digital signal processor (DSP), field-programmable gate array (FPGA), programmable logic device (PLD), controller, microcontroller unit, reduced instruction set computer (RISC), microprocessor, etc., or any combination thereof.

[0037] In some embodiments, the processing engine 112 can scan the frequency band of the GNSS satellite signal corresponding to the second signal to obtain the spectrum data of the entire frequency band; based on the analysis of the background noise and the amplified interference signal, obtain the signal strength of each frequency point on the spectrum; and based on the analysis of the signal strength, obtain the direction of the interference source. In some embodiments, the processing engine 112 can determine candidate directions based on the analysis of the signal strength; acquire at least two interference signals collected by the GNSS band high-gain Yagi direction-finding antenna based on at least two signal acquisition points; the at least two interference signals are signals collected by the GNSS band high-gain Yagi direction-finding antenna at at least two signal acquisition points with the candidate direction as the test direction; based on the analysis of each interference signal in the at least two interference signals, obtain the positioning direction corresponding to each interference signal; summarize the positioning directions determined by all the interference signals in the at least two interference signals, and determine the positioning direction with the most overlap as the direction of the interference source, etc. For more descriptions of the functions that the processing engine 112 can implement, please refer to other parts of this specification, such as Figures 2-7 The corresponding content.

[0038] Network 120 can facilitate the exchange of information and / or data. In some embodiments, one or more components of application scenario 100 (e.g., server 110, user terminal 130, storage device 140, and signal source 150) can transmit information and / or data via network 120 to other components in application scenario 100. For example, processing engine 112 can transmit the analysis results of monitored radio waves to user terminal 130 via network 120. In some embodiments, network 120 can be a wired network or a wireless network, or any combination thereof. By way of example only, network 120 may include a cable network, wired network, fiber optic network, telecommunications network, intranet, Internet, local area network (LAN), wide area network (WAN), wireless local area network (WLAN), metropolitan area network (MAN), public switched telephone network (PSTN), Bluetooth™ network, ZigBee network, near field communication (NFC) network, or similar content, or any combination thereof. In some embodiments, network 120 may include one or more network access points. For example, network 120 may include wired or wireless network access points such as base stations and / or Internet exchange points 120-1, 120-2, etc. One or more components of application scenario 100 may be connected to network 120 through wired or wireless network access points to exchange data and / or information.

[0039] In some embodiments, user terminal 130 may include mobile device 130-1, tablet computer 130-2, laptop computer 130-3, desktop computer 130-4, etc., or any combination thereof. In some embodiments, mobile device 140-1 may include smart home devices, wearable devices, mobile devices, virtual reality devices, augmented reality devices, etc., or any combination thereof. In some embodiments, smart home devices may include smart lighting devices, smart appliance control devices, smart monitoring devices, smart TVs, smart cameras, walkie-talkies, etc., or any combination thereof. In some embodiments, wearable devices may include wristbands, shoes and socks, glasses, helmets, watches, clothing, backpacks, smart accessories, etc., or any combination thereof. In some embodiments, mobile devices may include mobile phones, personal digital assistants (PDAs), gaming devices, navigation devices, point-of-sale (POS) devices, laptop computers, desktop computers, etc., or any combination thereof. In some embodiments, virtual reality devices and / or augmented reality devices may include virtual reality helmets, virtual reality glasses, virtual reality goggles, augmented reality helmets, augmented reality glasses, augmented reality goggles, etc., or any combination thereof. For example, virtual reality devices and / or augmented reality devices may include Google Glass. TM RiftCon TM Fragments TM GearVR TM wait.

[0040] In some embodiments, user terminal 130 may be a mobile terminal configured to acquire radio signals. User terminal 130 may send and / or receive information related to satellite signal monitoring and identification to processing engine 112 or a processor installed in user terminal 130 via a user interface. For example, user terminal 130 may send radio signal data captured by user terminal 130 to processing engine 112 or a processor installed in user terminal 120 via the user interface. The user interface may be in the form of an application for satellite identification implemented on user terminal 130. The user interface implemented on user terminal 130 may facilitate communication between the user and processing engine 112. For example, the user may input and / or import radio signal data to be identified via the user interface. Processing engine 112 may receive the input signal data via the user interface. As another example, the user may input a request to identify radio signals via the user interface implemented on user terminal 130. In some embodiments, in response to an identification request, user terminal 130 may directly process the radio signal data via the processor of user terminal 130 based on a signal acquisition device installed in user terminal 130 as described elsewhere in this application. In some embodiments, in response to an identification request, user terminal 130 may send an identification request to processing engine 112 for determining a radio signal based on signal acquisition device located at signal source 150 or elsewhere in this application. In some embodiments, the user interface may facilitate the presentation or display of information and / or data (e.g., signals) received from processing engine 112 related to portable satellite navigation interference detection and localization (Portable Satellite Navigation Interference Detection and Localization). For example, the information and / or data may include results indicating Portable Satellite Navigation Interference Detection and Localization content, or instructions to perform Portable Satellite Navigation Interference Detection and Localization, etc. In some embodiments, the information and / or data may be further configured to cause user terminal 130 to display the results to a user.

[0041] Storage device 140 can store data and / or instructions. In some embodiments, storage device 140 can store data obtained from signal source 150. Storage device 140 can store data and / or instructions that processing engine 112 can execute or use to execute the exemplary methods described in this application. In some embodiments, storage device 140 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), etc., or any combination thereof. Exemplary mass storage may include disks, optical disks, solid-state drives, etc. Exemplary removable storage may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tapes, etc. Exemplary volatile read-write memory may include random access memory (RAM). Exemplary RAM may include dynamic random access memory (DRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), static random access memory (SRAM), thyristor random access memory (T-RAM), and zero-capacitance random access memory (Z-RAM), etc. Exemplary ROMs may include mask read-only memory (MROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), optical disc read-only memory (CD-ROM), and digital multifunction disk read-only memory, etc. In some embodiments, the storage device 140 may operate on a cloud platform. By way of example only, the cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, internal cloud, multi-tiered cloud, etc., or any combination thereof.

[0042] In some embodiments, storage device 140 may be connected to network 120 to communicate with one or more components of application scenario 100 (e.g., server 110, user terminal 130). One or more components of application scenario 100 may access data or instructions stored in storage device 140 via network 120. In some embodiments, storage device 140 may be directly connected to or communicate with one or more components of application scenario 100 (e.g., server 110, user terminal 130). In some embodiments, storage device 140 may be part of server 110.

[0043] Signal source 150 is the signal terminal that emits radio signals. For example, the signal source can be a satellite, signal generator, base station, etc. The radio signals generated by signal source 150 can be collected based on the corresponding signal acquisition device (such as various antennas) in this portable satellite navigation interference detection and positioning system.

[0044] In some embodiments, the signal source 150 may include a positioning satellite or the like that emitting positioning signals. In some embodiments, the signal source 150 may also include a signal source 150 that emits interference signals.

[0045] It should be noted that the above description is intended to be illustrative and not to limit the scope of this application. Many alternatives, modifications, and variations will be apparent to those skilled in the art. The features, structures, methods, and other characteristics of the exemplary embodiments described herein can be combined in various ways to obtain other and / or alternative exemplary embodiments. For example, signal source 150 may be configured with a storage module, a processing module, a communication module, etc. However, these variations and modifications do not depart from the scope of this application.

[0046] Figure 2 This is an exemplary structural configuration diagram of a portable satellite navigation interference detection and positioning system 200 shown in some embodiments of this specification.

[0047] like Figure 2 As shown, the portable satellite navigation interference detection and positioning system 200 includes:

[0048] GNSS high-gain satellite decoding antenna 210, power divider 220, GNSS band high-gain Yagi direction finding antenna 230, low-noise RF amplifier 240, zero intermediate frequency receiver 250, GNSS data parsing module 260, central processing unit 270.

[0049] The power divider is communicatively connected to the GNSS high-gain satellite decoding antenna and the GNSS data parsing module, respectively; the low-noise RF amplifier is communicatively connected to the GNSS high-gain Yagi direction-finding antenna and the zero-IF receiver, respectively; the zero-IF receiver is communicatively connected to the power divider and the central processing unit, respectively; and the central processing unit is communicatively connected to the GNSS data parsing module.

[0050] In some embodiments, the GNSS high-gain satellite decoding antenna is used to receive GNSS satellite signals and transmit the GNSS satellite signals to the power divider. In some embodiments, the GNSS high-gain satellite decoding antenna can be positioned directly above the entire system in physical space to receive GNSS satellite signals in the sky and provide background signals for direction finding and positioning interference sources.

[0051] In some embodiments, the power divider is used to divide the GNSS satellite signal into a first signal and a second signal; and to transmit the first signal to the GNSS data parsing module, and the second signal to the zero intermediate frequency receiver.

[0052] In some embodiments, the power divider can split the energy of one input signal into two or more outputs of equal or unequal energy, ensuring a certain degree of isolation between the output ports and preventing mutual interference. In some embodiments, the power divider can split the received signal into two paths. In some embodiments, the power divider is used to equally divide the GNSS satellite signal into two paths to obtain the first signal and the second signal. For example, the received signal can be equally divided into a first signal and a second signal according to its signal energy to ensure that the resolved signal and the signal used as the background signal are exactly the same.

[0053] In some embodiments, the GNSS-band high-gain Yagi direction-finding antenna is used to receive interference signals and transmit the interference signals to the low-noise radio frequency amplifier.

[0054] In some embodiments, the low-noise radio frequency amplifier is used to amplify the interference signal and transmit it to the zero-IF receiver. For example, a GNSS-band high-gain Yagi direction-finding antenna can receive the interference signal, amplify it using a low-noise radio frequency amplifier, and then connect it to the zero-IF receiver to assist in locating the interference source.

[0055] In some embodiments, the zero-IF receiver is used to compare and analyze the second signal as background noise with the amplified interference signal to obtain at least one of the following: spectral data, signal strength at each frequency point on the spectrum, and direction of the interference source; and transmits the obtained data to the central processing unit. For example, the zero-IF receiver can receive and compare the interference signal from a GNSS high-gain Yagi direction-finding antenna with the background noise from a GNSS high-gain satellite decoding antenna, perform signal analysis and processing, and transmit the data to the central processing unit for overall analysis of interference information and location of the interference source direction.

[0056] In some embodiments, the zero-IF receiver obtains at least one of the following: spectrum data, signal strength at each frequency point on the spectrum, and direction of the interference source.

[0057] First, the frequency band of the GNSS satellite signal corresponding to the second signal is scanned to obtain the spectrum data of the entire frequency band. Then, based on the analysis of the background noise and the amplified interference signal, the signal strength at each frequency point in the spectrum is obtained. Finally, based on the analysis of the signal strength, the direction of the interference source is obtained.

[0058] In some embodiments, the GNSS data parsing module is used to obtain satellite analysis information corresponding to the first signal based on the processing of the first signal as the signal to be analyzed. The satellite analysis information includes at least one of the following: number of satellites, signal quality of the satellites, signal strength, whether the signal is interfered with, the intensity of the interference, whether it is spoofed, and background noise; and transmits the satellite analysis information to the central processing unit.

[0059] In some embodiments, the central processing unit is used to comprehensively process the received information to obtain: spectral data of the GNSS satellite signal, parsed data of the GNSS satellite signal, and analytical data of the GNSS satellite signal. The parsed data includes at least one of UTC (Universal Time Coordinated) time and the latitude and longitude of the location. The analytical data includes at least one of interference status, interference intensity, environmental noise, and spoofing status. For example, the GNSS data parsing module can parse the GNSS satellite signal data and, by analyzing the GNSS satellite signal data, determine and detect the GNSS satellite signal and satellite communication data to obtain information such as the interference status, the strength of the interfered signal, the spoofing status, and background noise. This assists the signal receiver in identifying and locating the interference source.

[0060] In some embodiments, the central processing unit can use a low-power embedded industrial motherboard as the core of the GNSS satellite navigation and communication interference positioning system, so as to realize operations such as processing GNSS module data parsing, signal receiver data reception and analysis, parameter setting, data result display, data recording and data transmission with a small size and low power consumption.

[0061] In some embodiments, the portable satellite navigation interference detection and positioning system 200 may further include a user interaction module 2100 communicatively connected to the central processing unit. The user interaction module is used to display the analysis results of the central processing unit and to obtain user commands. For example, the user interaction module 2100 may be a 5-inch true-color TFT touchscreen for setting internal device parameters and displaying GPS satellite information, interference information, spoofing signals, satellite signals, etc., facilitating operator analysis of interference and location of interference sources.

[0062] In some embodiments, the portable satellite navigation interference detection and positioning system 200 may further include a power module 290 and a communication module 280 that are communicatively connected to the central processing unit. The power module is used to provide power to the portable satellite navigation interference detection and positioning system based on the central processing unit, and the communication module is used to realize the communication function of the portable satellite navigation interference detection and positioning system and to acquire offline data.

[0063] In some embodiments, the communication module 280 may consist of WIFI and BT. Measurement data is used for data transmission and storage for offline analysis.

[0064] In some embodiments, the power module 290 may be powered by a rechargeable lithium battery, enabling it to charge the battery with charging management functions and provide power to other modules.

[0065] In some embodiments, the portable satellite navigation interference detection and positioning system 200 can run on an embedded Linux system, providing a variety of functions such as command control, information analysis, processing, storage, display, and transmission while ensuring safety and reliability. This also facilitates the expansion and addition of more functions in the future.

[0066] It should be noted that the above description of the system and its components is for convenience only and should not be construed as limiting this specification to the embodiments described. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the various components or construct subsystems connected to other components without departing from these principles. For example, a GNSS-band high-gain Yagi direction-finding antenna and a low-noise RF amplifier can be integrated into one component. As another example, the various components can share a storage device, or each component can have its own separate storage device. Such variations are all within the scope of this specification.

[0067] like Figure 3 The diagram shown is an exemplary flowchart for determining the direction of an interference source according to some embodiments of this specification. In some embodiments, process 300 may be performed by a zero-IF receiver. In some embodiments, process 300 may include the following steps:

[0068] Step 310: Determine candidate directions based on the analysis of the signal strength.

[0069] In some embodiments, since the GNSS band high-gain Yagi direction finding antenna has good directivity, when the GNSS band high-gain Yagi direction finding antenna is facing the interference source, it can detect a relatively high signal strength. By rotating the GNSS band high-gain Yagi direction finding antenna at a high position to detect the direction of the relatively high signal strength in the surrounding area (such as higher than the preset signal strength value, which can be determined based on empirical values), and taking the direction with the relatively high signal strength as the candidate direction, the approximate direction of the interference source can be indicated.

[0070] Step 320: Obtain at least two interference signals collected by the GNSS band high-gain Yagi direction finding antenna based on at least two signal acquisition points; the at least two interference signals are signals collected by the GNSS band high-gain Yagi direction finding antenna at the at least two signal acquisition points with the candidate direction as the test direction.

[0071] In some embodiments, after determining the candidate direction, the high-gain Yagi direction-finding antenna in the GNSS band can be aligned with the candidate direction based on multiple signal acquisition points (such as two or more) to acquire the interference signal. Thus, one segment of interference signal can be acquired at one signal acquisition point. If there are two signal acquisition points, two segments of interference signal can be acquired.

[0072] Step 330: Based on the analysis of each of the at least two interference signals, the positioning direction corresponding to each interference signal is obtained.

[0073] In some embodiments, the direction of an interference source, i.e., the location direction corresponding to the interference signal, can be determined based on the analysis of a segment of interference signal. For example, the approximate location of an interference source can be determined based on a segment of interference signal, and the location direction may include the azimuth angle of the interference source direction relative to the acquisition point. Because the high-gain Yagi direction-finding antenna in the GNSS band has strong directivity, the signal strength is much higher when the GNSS band high-gain Yagi direction-finding antenna is pointed in the direction of the interference source than when it is not pointed in the direction of the interference source. Therefore, the direction of the interference source can be determined in this way.

[0074] Step 340: Summarize the positioning directions determined by all the interference signals of the at least two interference signals, and determine the positioning direction with the most overlap as the direction of the interference source.

[0075] In some embodiments, since the direction range of an interference source can be determined based on the signal acquired from a single acquisition point, the direction ranges of multiple interference sources determined by multiple acquisition points can be aggregated, and the location with the most overlap can be identified as the direction of the interference source. For example, using two acquisition points and an azimuth angle, the two lines drawn through the angle intersect at a point, which can then be considered the location of the interference source. In other words, the detected directions can be cross-compared to gradually approach the interference source and locate the GNSS interference.

[0076] In some embodiments, the more data points a system has, the more accurate the direction indication, as the environment can affect the signal. In some embodiments, to improve acquisition efficiency, the number of data points needs to be controlled in order to quickly locate interference sources.

[0077] In some embodiments, the number of acquisition points can be determined based on multiple methods. For example, in multi-point positioning, the number of acquisition points for acquiring interfering signals can be obtained based on historical records, including:

[0078] First, obtain the satellite feature vector. The vector elements of the satellite feature vector include the spectrum data and the signal strength of each frequency point. For example, satellite feature vector A (spectrum data, frequency point 1, signal strength of frequency point 1, frequency point 2, signal strength of frequency point 2, ...).

[0079] Then, based on the satellite feature vector and the historical satellite feature vectors, historically similar satellite feature vectors are determined. Historically similar satellite feature vectors can be the historical satellite feature vectors that have the minimum vector distance to the current satellite feature vector. Vector distance can be represented based on cosine distance, Chebyshev distance, Euclidean distance, etc.

[0080] The number of collection points corresponding to the historical similar satellite feature vectors is determined as the number of collection points for the current collection. In some embodiments, after determining the historical similar satellite feature vectors, the number of collection points corresponding to those historical similar satellite feature vectors can be used as the current number of collection points.

[0081] By comparing historical vectors to determine the number of sampling points, it is possible to determine the number of sampling points faster and more accurately. This allows for a reduction in the number of sampling points while ensuring accuracy and effectiveness, and improving the accuracy of determining the direction of interference sources.

[0082] In some embodiments, the number of acquisition points for acquiring the interference signal in the multi-point positioning can be determined based on a point number prediction model, which is a machine learning model.

[0083] like Figure 4 The diagram shows a point number prediction model 430. The point number prediction model 430 is based on the processing of spectrum data 410 and signal strength 420 at each frequency point to obtain the number of sampling points 440.

[0084] The point number prediction model 430 can be a machine learning model that can achieve prediction function, such as a deep neural network (DNN) or a recurrent neural network (RNN).

[0085] In some embodiments, the point number prediction model 430 can be obtained based on training. During training, training samples can be input into the initial point number prediction model to determine the model output. Then, based on the model output and training labels, the model parameters are determined iteratively according to the distribution characteristics of the loss function until training is complete (e.g., the number of iterations exceeds a threshold, or the error between the model output and the training labels is less than an error threshold). The trained initial point number prediction model is used as the point number prediction model 430. The training samples can be historical spectrum data, signal strengths at various frequency points in the historical spectrum data, and the training labels can be the number of historical sampling points corresponding to the identification of the interference source. The initial point number prediction model can refer to a point number prediction model without set parameters or with parameters using random values.

[0086] Predicting the number of data collection points using models can reduce the amount of manual calculation required, enabling the automatic determination of the necessary number of data collection points based on the actual situation, thereby improving data acquisition efficiency.

[0087] In some embodiments, the final number of collection points can be obtained by combining the number of collection points obtained by historical vector comparison and the number of collection points obtained by model prediction.

[0088] For example, the average of the number of collection points obtained by comparing historical vectors and the number of collection points obtained by model prediction can be used as the final number of collection points.

[0089] For example, weights can be assigned to the number of data points obtained through historical vector comparison and the number of data points predicted by the model, and the weighted sum of the two can be used as the final number of data points. These weights can be related to the confidence level of the model's output data; for example, the higher the confidence level, the higher the weight assigned to the number of data points predicted by the model. The confidence level of the model's output data can be obtained directly from the model or calculated using the corresponding confidence level calculation formula.

[0090] It should be noted that the above description of process 300 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 300 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0091] like Figure 5 The diagram shown is an exemplary flowchart illustrating the acquisition of satellite analysis information corresponding to the first signal according to some embodiments of this specification. In some embodiments, process 500 may be executed based on a GNSS data parsing module.

[0092] like Figure 5 As shown, process 500 may include the following steps:

[0093] Step 510: Determine the number of satellites and the signal quality and signal strength of each satellite based on the analysis of the first signal.

[0094] The GNSS data parsing module can determine the number of satellites in the air by parsing the acquired first signal. It can also obtain information such as the signal quality and signal strength of each satellite.

[0095] Step 520: Based on the number of satellites and the signal quality of each satellite, determine whether the signal is interfered with and the intensity of the interference.

[0096] In some embodiments, the GNSS data parsing module can further determine whether the signal is interfered with and the intensity of the interference based on the acquired data and the data analyzed in step 510. For example, when a large number of satellite signals are received (e.g., the number of satellites exceeds a preset value), but the signal-to-noise ratio of the satellite signals cannot be obtained or the signal-to-noise ratios are all relatively low (e.g., all below the threshold), it can be determined that the satellite signals are interfered with.

[0097] The intensity of interference can be expressed in levels or numerical values, such as severe, moderate, and slight, or level 1, level 2, etc. A higher numerical value indicates a stronger interference. In some embodiments, the intensity of interference can be comprehensively judged based on whether a signal-to-noise ratio (SNR) can be obtained, the number of satellite signals for which SNR can be obtained, and the value of the SNR. For example, if no SNR can be obtained at all, the interference intensity is considered high, such as level 10 (or severe). If no more than 50% of the satellite signals have an SNR, the interference intensity is level 5 (or moderate). If more than 90% of the satellite signals have an SNR, but the SNRs are all low (e.g., below the threshold), the interference intensity can be considered level 1 (slight).

[0098] In some embodiments, if only a few satellite signals (e.g., below a preset value) can be received at a collection point and the received satellite signals can be analyzed, it indicates that the environment may be affecting the reception of satellite signals, and the location needs to be changed.

[0099] Step 530: Obtain at least one positioning reference data, and compare the positioning data obtained based on the first signal with the at least one positioning reference data to determine whether the satellite has been deceived.

[0100] Positioning reference data can be positioning data obtained from other positioning satellites, such as positioning data based on GPS data, BeiDou data, and Galileo data. In some embodiments, positioning reference data can also be positioning data obtained from offline maps based on system content. In some embodiments, positioning data can be represented in the form of latitude and longitude.

[0101] In some embodiments, the GNSS data parsing module can compare positioning data obtained based on a first signal with positioning reference data to determine whether the satellite has been spoofed. For example, it can compare the positioning data obtained based on the first signal with positioning data obtained from one or more of the GPS, BeiDou, and Galileo satellite systems. If the positioning data obtained based on the first signal is inconsistent with the positioning reference data (or the difference exceeds a preset threshold), the satellite is considered to have been spoofed. Since the GPS, BeiDou, and Galileo satellite systems use different frequencies, if the positioning data from multiple satellite systems are inconsistent, the likelihood of spoofing is very high. Alternatively, the positioning data obtained based on the first signal can be compared with positioning data obtained from an offline map. If the two are inconsistent (or the difference exceeds a preset threshold), the satellite is considered to have been spoofed.

[0102] It should be noted that the above description of process 500 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 500 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0103] In some embodiments, the processing of the corresponding data based on a machine learning model can also determine whether the signal is interfered with and the intensity of the interference.

[0104] like Figure 6 The diagram shown illustrates some interference prediction models based on this specification. Figure 6 As shown, based on the interference prediction model 630, the number of satellites 610 and the signal quality 620 of each satellite can be processed to determine whether the signal is interfered with 640 and the intensity of the interference 650.

[0105] In some embodiments, the interference prediction model may be a convolutional neural network (CNN), a deep neural network (DNN), or a combination thereof.

[0106] In some embodiments, the input to the interference prediction model may include the number of satellites and the signal quality of each satellite. In some embodiments, the output of the interference prediction model may include whether the signal is interfered with and the intensity of the interference. In some embodiments, the signal quality of a satellite can be determined based on the strength of the satellite signal and its signal-to-noise ratio (SNR). For example, different quality levels corresponding to different signal strengths and SNRs can be set, and the corresponding signal quality can be determined based on the actual signal strength and SNR using methods such as table lookup. In some embodiments, the number of satellites and the signal quality of each satellite can be processed into vectors, matrices, sequences, or other forms and input into the interference prediction model.

[0107] As an example only, the input to the interference prediction model can be (a,b,c,d,…), where a represents the number of satellites, b represents the signal quality of the first satellite, c represents the signal quality of the second satellite, and so on.

[0108] If the number of satellites acquired is less than the number of satellite signals in the preset input model, the corresponding satellite signal quality can be represented by a specific character, such as 0. If the number of satellites acquired exceeds the number of satellite signals in the preset single input model, the data can be segmented so that each segmented data set does not exceed the number of data sets in the preset single input model. The results obtained from each data set can then be comprehensively processed to obtain the final result, such as by calculating the average or weighted summation.

[0109] In some embodiments, whether the signal in the model's output is interfered with can be represented by corresponding characters, for example, N indicates no interference and Y indicates interference. The intensity of interference can also be represented by corresponding characters, for example, 0 indicates the interference intensity when there is no interference, and 1-10 represent different interference intensities when there is interference, with larger values ​​indicating greater intensity.

[0110] In some embodiments, the interference prediction model can be trained using multiple labeled training samples. For example, multiple labeled training samples can be input into an initial interference prediction model, and a loss function can be constructed using the labels and the results of the initial interference prediction model. The parameters of the initial interference prediction model are then iteratively updated based on the loss function. When the loss function of the initial interference prediction model satisfies a preset condition, the model training is complete, and a trained interference prediction model is obtained. The preset condition may be that the loss function converges, the number of iterations reaches a threshold, etc.

[0111] In some embodiments, the training samples may include at least multiple sets of historical sample data. Each set of historical sample data may include the number of satellites in a historically acquired signal and the signal quality of each satellite. The label may be the result of whether interference occurred and the specific interference intensity value. The label may be obtained based on manual annotation.

[0112] Interference prediction models can accurately predict whether a signal is being interfered with, as well as the intensity of the interference, thereby improving data processing efficiency and accuracy.

[0113] Figure 7 This is a schematic diagram of a portable satellite navigation interference detection and positioning device 700 according to some embodiments of this specification.

[0114] In some embodiments, this portable satellite navigation interference detection and positioning system can be manufactured as follows: Figure 7 A portable device is shown, such as Figure 7 As shown, the portable satellite navigation interference detection and positioning device 700 may include a main unit, a GNSS high-gain satellite decoding antenna mounted on top of the main unit, and a GNSS high-gain Yagi direction-finding antenna mounted on the front of the main unit. For ease of use and quick information acquisition, a grip can be installed at the bottom of the main unit, and a user interaction module such as a touchscreen can be installed on the back of the main unit. Correspondingly, a power divider, a low-noise RF amplifier, a zero-IF receiver, a GNSS data parsing module, a central processing unit, a power supply module, and a communication module can be embedded within the main unit.

[0115] The portable satellite navigation interference detection and positioning device 700 can be designed as an integrated unit, with a GNSS-band high-gain Yagi direction-finding antenna at the front. By holding the device and pointing the GNSS-band high-gain Yagi direction-finding antenna in different directions, the device compares the measured interference signal with the background signal measured by the GNSS high-gain satellite decoding antenna. Based on the signal strength in each direction, the source of the interference signal is determined, and the results are displayed on a touchscreen in the form of data and waveforms. Simultaneously, the measured data can be stored or transmitted to a remote platform via a communication interface for later analysis.

[0116] In some embodiments, the portable satellite navigation interference detection and positioning device 700 can automatically detect and alarm against suppressed interference. For example, after the device is turned on, the GNSS data parsing module in the device can automatically parse GNSS satellite data. When the quality of the detected satellite signal decreases significantly (e.g., many satellites can be found, but the signal-to-noise ratio of the satellites cannot be obtained, or the signal-to-noise ratio is too low), or the number of effective GNSS satellites decreases significantly, an automatic alarm is triggered on the screen, indicating that there is an interference signal and that the interference is too strong. At the same time, the interference source can be located by frequency scanning. Here, effective GNSS satellites refer to satellites from which normal satellite signal-to-noise ratio and azimuth data can be obtained.

[0117] In some embodiments, the portable satellite navigation interference detection and positioning device 700 can automatically detect and alarm against deceptive interference. For example, after the device is turned on, the GNSS data parsing module in the device automatically parses GNSS satellite data. If the detected positioning information does not match the actual positioning information, or if the positioning information between GPS, BeiDou, and Galileo is inconsistent, it is considered to be subject to deceptive interference, and an alarm can be triggered through the display interface.

[0118] In some embodiments, the portable satellite navigation interference detection and positioning device 700 can automatically mark the actual mobile road test trajectory and GNSS interference / deception areas during mobile road testing. For example, the device can be placed in a mobile road test vehicle, and the GNSS data parsing module in the device can detect the interference status, deception status, and various satellite parameters (such as the number of satellites, satellite signal-to-noise ratio, ambient noise, interference level, GNSS spectrum, etc.) at various points during the movement. It also records the correct GNSS position information (correct GNSS position information can be obtained in the early stages of the road test when the device is not completely interfered with or deceived, allowing for positioning calibration; during the movement, correct position information can be obtained through software calibration and comparison with various satellite systems (GPS, BEIDOU, Galileo). The mobile detection vehicle plots the interference area, interference status, and various satellite parameter information.

[0119] In some embodiments, the portable satellite navigation interference detection and positioning device 700 can determine the azimuth of both suppressive and deceptive interference. For example, by using a GNSS data analysis module to locate the interference area, adjusting the direction of the high-gain Yagi direction-finding antenna in the GNSS band and observing GNSS spectrum data, the direction of the interference source is determined. Then, the interference source is located by multi-point location tracking. Alternatively, by using a GNSS data analysis module to locate the deceptive area, adjusting the direction of the high-gain Yagi direction-finding antenna in the GNSS band and observing GNSS spectrum data, the direction of the deceptive source is determined. Then, the deceptive source is located by multi-point location tracking.

[0120] It should be noted that the above description of the system and its components is for convenience only and should not be construed as limiting this specification to the embodiments described. It is understood that those skilled in the art, after understanding the principles of the system, may arbitrarily combine the various components or construct subsystems connected to other components without departing from these principles. For example, the data source selection module and the spectrum analysis and playback control module can be integrated into one component. Alternatively, the various components can share a storage device, or each component can have its own separate storage device. Such variations are all within the scope of this specification.

[0121] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0122] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0123] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0124] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0125] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0126] For each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, and documents, referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0127] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A satellite signal analysis system, characterized in that, include: The system includes a GNSS high-gain satellite decoding antenna, a power divider, a GNSS-band high-gain Yagi direction-finding antenna, a low-noise RF amplifier, a zero-IF receiver, a GNSS data parsing module, and a central processing unit. The power divider is communicatively connected to both the GNSS high-gain satellite decoding antenna and the GNSS data parsing module. The low-noise RF amplifier is communicatively connected to both the GNSS-band high-gain Yagi direction-finding antenna and the zero-IF receiver. The zero-IF receiver is communicatively connected to both the power divider and the central processing unit. The central processing unit is communicatively connected to the GNSS data parsing module. The GNSS high-gain satellite decoding antenna receives GNSS satellite signals and transmits them to the power divider. The power divider splits the GNSS satellite signal into two paths according to the signal energy, resulting in a first signal and a second signal; the first signal is transmitted to the GNSS data parsing module, and the second signal is transmitted to the zero-IF receiver; The high-gain Yagi direction-finding antenna in the GNSS band receives interference signals and transmits them to a low-noise radio frequency amplifier. The low-noise radio frequency amplifier amplifies the interference signal and transmits it to the zero intermediate frequency receiver; The zero-IF receiver scans the frequency band of the GNSS satellite signal corresponding to the second signal to obtain the spectrum data of the entire frequency band; Based on the analysis of background noise and amplified interference signals, the signal strength at each frequency point in the spectrum is obtained; Candidate directions are determined based on signal strength analysis; Acquire at least two interference signals collected by a high-gain Yagi direction finding antenna in the GNSS band based on at least two signal acquisition points; the at least two interference signals are signals collected by the high-gain Yagi direction finding antenna in the GNSS band at at least two signal acquisition points with the candidate direction as the test direction. Based on the analysis of each of the at least two interference signals, the positioning direction corresponding to each interference signal is obtained; In at least two interference signals, the positioning directions corresponding to all interference signals are summarized, and the positioning direction with the most overlap is determined as the direction of the interference source; the obtained data is transmitted to the central processing unit. The GNSS data parsing module processes the first signal, which is the signal to be analyzed, to obtain satellite analysis information corresponding to the first signal. The satellite analysis information includes at least one of the following: number of satellites, satellite signal quality, signal strength, whether the signal is interfered with, the intensity of the interference, whether it has been spoofed, and background noise. The satellite analysis information is then transmitted to the central processing unit. The central processing unit performs comprehensive processing on the received information to obtain: GNSS satellite signal spectrum data, GNSS satellite signal parsing data, and GNSS satellite signal analysis data. The parsing data includes at least one of UTC time and latitude and longitude; the analysis data includes at least one of interference status, interference intensity, environmental noise, and deception status. The number of sampling points is predicted based on the processing of spectrum data and signal strength at each frequency point to obtain the number of sampling points that collect interference signals in multi-point positioning; the number of sampling points prediction model is a machine learning model. Obtain satellite feature vectors, whose vector elements include spectral data and signal strength at each frequency point; Based on satellite feature vectors and historical satellite feature vectors, determine historically similar satellite feature vectors; The number of collection points corresponding to the feature vectors of historically similar satellites is determined as the number of collection points based on historical vector comparison.

2. The system according to claim 1, characterized in that, The number of acquisition points collecting the interfering signal in the multi-point positioning is determined by combining the number of acquisition points based on model prediction and the number of acquisition points based on historical vector comparison, including: The number of collection points predicted by the model and the number of collection points based on historical vector comparison are assigned certain weight values, and the weighted sum of the two is taken as the final number of collection points. The weight values ​​are related to the confidence level of the data output by the point number prediction model. The higher the confidence level, the higher the weight value assigned to the number of collection points predicted by the model.

3. The system according to claim 1, characterized in that, The acquisition of satellite analysis information corresponding to the first signal includes: The number of satellites and the signal quality and signal strength of each satellite are determined based on the analysis of the first signal; Based on the number of satellites and the signal quality of each satellite, determine whether the signal is interfered with and the intensity of the interference; At least one positioning reference data is acquired, and the positioning data obtained based on the first signal is compared with the at least one positioning reference data to determine whether the satellite has been deceived.

4. The system according to claim 3, characterized in that, The determination of whether the signal is interfered with and the intensity of the interference is based on an interference prediction model; The interference prediction model is a machine learning model. Based on the processing of the number of satellites and the signal quality of each satellite, the interference prediction model determines whether the signal is interfered with and the intensity of the interference.

5. The system according to claim 1, characterized in that, It also includes a user interaction module that is communicatively connected to the central processing unit. The user interaction module is used to display the analysis results of the central processing unit and obtain user instructions.

6. The system according to claim 1, characterized in that, It also includes a power module and a communication module that are communicatively connected to the central processing unit. The power module is used to provide power to the portable satellite navigation interference detection and positioning system based on the central processing unit, and the communication module is used to realize the communication function of the portable satellite navigation interference detection and positioning system and to acquire offline data.

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