CN113676243A - Satellite signal monitoring and analyzing method and system - Google Patents

Satellite signal monitoring and analyzing method and system Download PDF

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Publication number
CN113676243A
CN113676243A CN202110903456.9A CN202110903456A CN113676243A CN 113676243 A CN113676243 A CN 113676243A CN 202110903456 A CN202110903456 A CN 202110903456A CN 113676243 A CN113676243 A CN 113676243A
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China
Prior art keywords
signal
satellite
frequency
monitoring
analysis
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Inventor
张光云
陈玮玮
张丹
刘冬
曾春娥
陈丹凤
钟秋平
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Chengdu Dechen Borui Technology Co ltd
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Chengdu Dechen Borui Technology Co ltd
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Priority to CN202110903456.9A priority Critical patent/CN113676243A/en
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/1851—Systems using a satellite or space-based relay
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/35—Constructional details or hardware or software details of the signal processing chain
    • G01S19/37—Hardware or software details of the signal processing chain

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  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Astronomy & Astrophysics (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Monitoring And Testing Of Transmission In General (AREA)

Abstract

The invention discloses a satellite signal monitoring and analyzing method, which comprises the following steps: acquiring a signal to be detected; performing primary screening on a signal to be detected to obtain a signal subjected to primary screening; estimating an approximate value of a mean value and an approximate value of a mean square error of the frequency band noise of the primarily screened signal; obtaining a judgment threshold of the primarily screened signal based on the evaluation model; further filtering the preliminarily screened signals by utilizing a decision threshold to obtain signals meeting the conditions; and calculating the center frequency of the signal meeting the conditions to finish the detection of the signal. A fast and accurate detection of the signal can be achieved.

Description

Satellite signal monitoring and analyzing method and system
Technical Field
The invention relates to the technical field of satellite signal monitoring and analysis, in particular to a method and a system for monitoring and analyzing satellite signals.
Background
With the development and application of a new generation satellite mobile communication system, the satellite mobile communication is developed to be broadband and high-speed, which is a great challenge for radio monitoring, and meanwhile, due to the universality and particularity of the application of the satellite mobile terminal, a research and development organization with capability is promoted to develop a signal detection system suitable for radio management requirements to effectively detect and monitor satellite signals, so that technical support for radio management is more and more urgent.
Therefore, a more effective and accurate method for monitoring and analyzing satellite signals is needed.
Disclosure of Invention
One aspect of the embodiments of the present specification provides a method for monitoring and analyzing satellite signals, including: acquiring satellite signals to be analyzed in a frequency band; analyzing whether the acquired satellite signal belongs to a satellite uplink signal or a satellite downlink signal; analyzing whether the satellite signals generate frequency hopping or not according to the frequency variation of the same signal; determining a signal frequency point to be analyzed in a satellite signal to be analyzed; setting signal analysis parameters; and acquiring satellite signal characteristics based on the signal analysis parameters, and determining the satellite signal identity information and the signal rule thereof.
In some embodiments, the analyzing whether the acquired satellite signal belongs to a satellite uplink signal or a satellite downlink signal includes: under a TDD working mode, adjusting the receiving bandwidth and RBW to a preset range, and observing a time-frequency diagram of a signal; if the frequency of the corresponding signal center in the time-frequency diagram does not change along with the time, the signal center is identified as an uplink signal; if the central frequency changes along with the time, the signal presents the inclined characteristic in the time-frequency diagram, and the signal is a downlink signal.
In some embodiments, the analyzing whether the frequency change of the same signal is for the satellite signal subjected to the frequency hopping includes: in the TDD working mode, after the communication connection is established, if the frequency hopping is monitored, whether the front and back 2 frequencies have consistency on the signal characteristics is observed to determine whether the frequency changes of the same signal.
In some embodiments, the signal analysis parameters include center frequency, monitoring bandwidth, direction finding bandwidth, RBW, built-in amplifier gain, attenuation coefficient.
One aspect of embodiments of the present specification provides a satellite signal monitoring and analysis system, including: the acquisition module is used for acquiring satellite signals to be analyzed in a frequency band; the first analysis module is used for analyzing whether the acquired satellite signal belongs to a satellite uplink signal or a satellite downlink signal; the second analysis module is used for analyzing whether the satellite signals subjected to frequency hopping are frequency changes of the same signal; the first determination module is used for determining a signal frequency point to be analyzed in a satellite signal to be analyzed; the setting module is used for setting signal analysis parameters; and the second determination module is used for acquiring satellite signal characteristics based on the signal analysis parameters and determining the satellite signal identity information and the signal rule thereof.
One aspect of an embodiment of the present specification provides a license plate recognition device in a complex scene, the device including at least one storage medium and at least one processor, the at least one storage medium storing computer instructions; the at least one processor is configured to execute the computer instructions to implement operations corresponding to the satellite signal monitoring and analyzing method.
An aspect of the embodiments of the present specification provides a computer-readable storage medium, which stores computer instructions, and when the computer instructions in the storage medium are read by a computer, the method for monitoring and analyzing satellite signals is implemented.
Drawings
The present description will be further described by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:
FIG. 1 is a schematic diagram of an application scenario of a satellite signal monitoring and analysis system according to some embodiments of the present application;
FIG. 2 is a schematic diagram of exemplary hardware and/or software components of an exemplary computing device on which a processing engine may be implemented, according to some embodiments of the present application;
FIG. 3 is a schematic diagram of exemplary hardware and/or software components of an exemplary mobile device on which one or more terminals may be implemented in accordance with some embodiments of the present application;
FIG. 4 is a schematic block diagram of an exemplary processing engine shown in accordance with some embodiments of the present application;
FIG. 5 is a flow diagram of a method of satellite signal monitoring analysis, according to some embodiments of the present description;
FIG. 6 is a schematic workflow diagram of a first analysis module shown in accordance with some embodiments of the present description;
FIG. 7 is a schematic diagram of training of a classification model according to some embodiments of the present description;
FIG. 8 is a training diagram of an assessment model according to some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system", "device", "unit" and/or "module" as used in this specification is a method for distinguishing different components, elements, parts or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
China has broad breadth, complex geographic environment and serious problem of wide coverage of ground cellular networks in remote areas. The satellite mobile communication system is used as an extension and extension of a terrestrial cellular communication system, is not limited by regions and weather, and has strong portability, so that the satellite mobile communication system becomes a first choice for various emergency communications. With the development and application of the new generation satellite mobile communication system, the satellite mobile communication is developed to be broadband and high-speed, which is a great challenge for radio monitoring, and because of the special use scene, the radio service monitoring range is a relatively blank field, so that the establishment of perfect understanding and monitoring means for the satellite mobile communication is doubtless.
The embodiment of the application provides a satellite signal monitoring and analyzing method, and the method principle of the embodiment of the application can be applied to monitoring and analyzing of various satellite signals. It should be understood that the application scenarios of the system and method of the present application are merely examples or embodiments of the present application, and those skilled in the art can also apply the present application to other similar scenarios without inventive effort based on these drawings.
FIG. 1 is a schematic diagram of an exemplary monitoring system according to some embodiments of the present application. In some embodiments, the application scenario 100 may be configured to monitor for analysis of a particular satellite signal, or the like. The method can be applied to corresponding communication control scenes such as satellite monitoring, satellite identification and satellite management. The application scenario 100 may include a server 110, a network 120, a user terminal 130, a storage device 140, and an information source 150. The server 110 may include a processing engine 112. In some embodiments, server 110, user terminal 130, storage device 140, and information source 150 may be connected to and/or communicate with each other via a wireless connection (e.g., network 120), a wired connection, or a combination thereof.
The computing system 110 may be used to monitor and analyze satellite signals. In some embodiments, the method can be specifically used for identifying the maritime satellite signal, so as to realize the monitoring of the maritime satellite, and the identification technology can be applied to many fields such as government departments, defense armies, news media, customs, foreign affairs, combat readiness communication and the like. The computing system 110 may obtain satellite signal characteristics based on the signal analysis parameters, thereby determining satellite signal identity information and signal regularity thereof, and completing signal monitoring and analysis.
Computing system 110 refers to a system having computing capabilities, and in some embodiments, server 110 may be a single server or a group of servers. The set of servers can be centralized or distributed (e.g., the servers 110 can be a distributed system). In some embodiments, the server 110 may be local or remote. For example, server 110 may access information and/or data stored in user terminal 130 and/or storage device 140 via network 120. As another example, server 110 may be directly connected to user terminal 130 and/or storage device 140 to access stored information and/or data. In some embodiments, the server 110 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof. In some embodiments, server 110 may be implemented on a computing device 200 having one or more of the components illustrated in FIG. 2 in the present application.
In some embodiments, the server 110 may include a processing engine 112. The processing engine 112 may process information and/or data related to the satellite signals. For example, the processing engine 112 may be obtaining an analytical signal to be monitored from the information 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, the processing engine 112 may include one or more hardware processors, such as a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an application specific instruction set processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.
Network 120 may facilitate the exchange of information and/or data. In some embodiments, one or more components in the application scenario 100 (e.g., the server 110, the user terminal 130, the storage device 140, and the information source 150) may send information and/or data to other components in the application scenario 100 over the network 120. For example, the processing engine 112 may transmit information related to the identified satellite signals to the user terminal 130 via the network 120. In some embodiments, the network 120 may be a wired network or a wireless network, or the like, or any combination thereof. By way of example only, network 120 may include a cable network, a wireline network, a fiber optic network, a telecommunications network, an intranet, the Internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), the Public Switched Telephone Network (PSTN), Bluetooth, etcTMA network, a ZigBee network, a Near Field Communication (NFC) network, or the like, or any combination thereof. In some embodiments, network 120 may include one or more network access points. For example, the network 120 may include wired or wireless network access points, such as base stations and/or internet exchange points 120-1, 120-2, …, through which one or more components of the application scenario 100 may connect to the network 120 to exchange data and/or information.
In some embodiments, the user terminal 130 may include a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, a desktop computer 130-4, and the like, or any combination thereof. In some embodiments, mobile device 140-1 may include a smart home device, a wearable device, a mobile device, a virtual reality device, an augmented reality device, and the like, or any combination thereof. In some embodiments, the smart home devices may include smart lighting devices, smart appliance control devices, smart monitoring devices, smart televisions, smart cameras, interphones, and the like, or any combination thereof. In some embodimentsIn some embodiments, the wearable device may include a bracelet, footwear, glasses, helmet, watch, clothing, backpack, smart accessory, or the like, or any combination thereof. In some embodiments, the mobile device may include a mobile phone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, a point of sale (POS) device, a laptop computer, a desktop computer, etc., or any combination thereof. In some embodiments, the virtual reality device and/or the enhanced virtual reality device may include a virtual reality helmet, virtual reality glasses, virtual reality eyecups, augmented reality helmets, augmented reality glasses, augmented reality eyecups, and the like, or any combination thereof. For example, the virtual reality device and/or the augmented reality device may include a google glassTM、RiftConTM、FragmentsTM、GearVRTMAnd the like.
In some embodiments, user terminal 130 may be a mobile terminal configured to acquire satellite signals. The user terminal 130 may send and/or receive information related to satellite signal identification to the processing engine 112 or a processor installed in the user terminal 130 via a user interface. For example, the user terminal 130 may transmit satellite signal data acquired by the user terminal 130 installed to the processing engine 112 or processor installed in the user terminal 120 via the user interface. The user interface may be in the form of an application implemented on the user terminal 130 for identifying satellites. A user interface implemented on the user terminal 130 may facilitate communication between the user and the processing engine 112. For example, a user may enter and/or import signal data that needs to be identified via a user interface. The processing engine 112 may receive input signal data via a user interface. As another example, the user may input a request for monitoring analysis of satellite signals via a user interface implemented on the user terminal 130. In some embodiments, in response to the monitoring analysis request, the user terminal 130 may directly process the satellite signal data via a processor of the user terminal 130 based on a signal acquisition device installed in the user terminal 130 as described elsewhere in this application. In some embodiments, in response to the monitoring analysis request, the user terminal 130 may send the monitoring analysis request to the processing engine 112 for enabling monitoring analysis of the satellite signals based on determining a center frequency of the satellite signals by the information source 150 or a signal acquisition device installed elsewhere in the application. In some embodiments, the user interface may facilitate presenting or displaying information and/or data (e.g., signals) related to satellite monitoring analysis received from the processing engine 112. For example, the information and/or data may include results indicative of content of a satellite monitoring analysis, or satellite information indicative of a corresponding satellite signal obtained by the monitoring analysis, or the like. In some embodiments, the information and/or data may be further configured to cause the user terminal 130 to display the results to the user.
Storage device 140 may store data and/or instructions. In some embodiments, the storage device 140 may store data obtained from the information source 150. Storage device 140 may store data and/or instructions that processing engine 112 may execute or use to perform the exemplary methods described herein. In some embodiments, storage device 140 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), the like, or any combination thereof. Exemplary mass storage devices may include magnetic disks, optical disks, solid state drives, and the like. Exemplary removable memories may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tape, and the like. Exemplary volatile read and write memory can 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), zero capacitance random access memory (Z-RAM), and the like. Exemplary ROMs may include mask-type read-only memory (MROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory, and the like. In some embodiments, the storage device 140 may execute on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.
In some embodiments, a storage device 140 may be connected to the network 120 to communicate with one or more components (e.g., server 110, user terminal 130) in the application scenario 100. One or more components in the application scenario 100 may access data or instructions stored in the storage device 140 via the network 120. In some embodiments, the storage device 140 may be directly connected to or in communication with one or more components in the application scenario 100 (e.g., server 110, user terminal 130). In some embodiments, the storage device 140 may be part of the server 110.
The information source 150 is composed of the signals to be monitored and analyzed from various satellite systems to be monitored and analyzed. In some embodiments, the information source 150 may be used to provide satellite pictures, signal data, satellite video, signal audio, etc. to the system. Information source 150 may include one or more satellites, such as satellite 150-1, satellite 150-2, and satellite 150-3. The information source 150 is configured to provide signal data of a certain satellite in the satellite system or the entire satellite system 150, and transmit the signal data to the network 120 or the user terminal 130 through a wireless connection, so as to analyze characteristics of the signal, such as frequency, and the like, according to the signal data, determine satellite signal identity information and signal regularity thereof, and thus complete monitoring and analysis of the signal.
It should be noted that the above description is intended to be illustrative, and not to limit the scope of the 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 may be combined in various ways to obtain additional and/or alternative exemplary embodiments. For example, the information source 150 may be configured with a storage module, a processing module, a communication module, and the like. However, such changes and modifications do not depart from the scope of the present application.
FIG. 2 is a schematic diagram of exemplary hardware and/or software components of an exemplary computing device on which a processing engine may be implemented in accordance with some embodiments of the present application. As shown in FIG. 2, computing device 200 may include a processor 210, memory 220, input/output (I/O)230, and communication ports 240.
The processor 210 (e.g., logic circuitry) may execute computer instructions (e.g., program code) and perform the functions of the processing engine 112 in accordance with the techniques described herein. In some embodiments, the processor 210 may be configured to process data and/or information related to one or more components of the application scenario 100. For example, the processor 210 may center the frequency of the signal data acquired by the information source 150. As another example, the processor 210 may determine the satellite signal identity information and its signal regularity based on characteristics of a series of signal data. The processor 210 may also be configured to acquire a satellite system corresponding to the identified satellite. The processor 210 may also transmit the identified information or determination result to the server 110. In some embodiments, the processor 210 may send a notification to the associated user terminal 130.
In some embodiments, processor 210 may include interface circuitry 210-a and processing circuitry 210-b therein. The interface circuit may be configured to receive electrical signals from a bus (not shown in fig. 2), where the electrical signals encode structured data and/or instructions for processing by the processing circuit. The processing circuitry may perform logical computations and then encode the conclusions, results and/or instructions into electrical signals. The interface circuit may then send the electrical signals from the processing circuit via the bus.
The computer instructions may include, for example, routines, programs, objects, components, data structures, procedures, modules, and functions that perform particular functions described herein. For example, the processor 210 may process information related to satellite signals obtained from the user terminal 130, the storage device 140, and/or any other component of the application scenario 100. In some embodiments, processor 210 may include one or more hardware processors, such as microcontrollers, microprocessors, Reduced Instruction Set Computers (RISC), Application Specific Integrated Circuits (ASIC), application specific instruction set processors (ASIP), Central Processing Units (CPU), Graphics Processors (GPU), Physical Processors (PPU), microcontrollers, Digital Signal Processors (DSP), Field Programmable Gate Arrays (FPGA), Advanced RISC Machines (ARM), Programmable Logic Devices (PLD), any circuit or processor capable of executing one or more functions, or the like, or any combination thereof.
For illustration only, only one processor is depicted in computing device 200. However, it should be noted that the computing device 200 in the present application may also include multiple processors, and thus, operations and/or method steps performed by one processor as described herein may also be performed jointly or separately by multiple processors. For example, if in the present application, the processors of computing device 200 perform steps a and B simultaneously, it should be understood that steps a and B may also be performed jointly or separately by two or more different processors in computing device 200 (e.g., a first processor performing step a, a second processor performing step B, or a first processor and a second processor performing steps a and B together).
The memory 220 may store data/information obtained from the user terminal 130, the storage device 140, and/or any other component of the application scenario 100. In some embodiments, memory device 220 may include a mass memory device, a removable memory device, a volatile read-write memory, a read-only memory (ROM), the like, or any combination thereof. For example, mass storage may include magnetic disks, optical disks, solid state drives, and so forth. The removable storage device may include flash memory, floppy disks, optical disks, memory cards, zip disks, tapes, and the like. The volatile read and write memory may include Random Access Memory (RAM). RAM may include Dynamic RAM (DRAM), double-data-rate synchronous dynamic RAM (DDRSDRAM), Static RAM (SRAM), thyristor RAM (T-RAM), zero-capacitor RAM (Z-RAM), and the like. The ROM may include Masked ROM (MROM), Programmable ROM (PROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), compact disk ROM (CD-ROM), digital versatile disk ROM, and the like. In some embodiments, memory 220 may store one or more programs and/or instructions to perform the example methods described herein. For example, the memory 220 may store a program for the processing engine 112 to determine vehicle values.
I/O230 may input and/or output signals, data, information, and the like. In some embodiments, I/O230 may enable a user to interact with processing engine 112. In some embodiments, I/O230 may include input devices and output devices. Examples of input devices may include a keyboard, mouse, touch screen, microphone, etc., or a combination thereof. Examples of output devices may include a display device, speakers, printer, projector, etc., or a combination thereof. Examples of a display device may include a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) based display, a flat panel display, a curved screen, a television device, a Cathode Ray Tube (CRT), a touch screen, etc., or any combination thereof.
The communication port 240 may be connected to a network (e.g., network 120) to facilitate data communication. The communication port 240 may establish a connection between the processing engine 112 and the user terminal 130, the information source 150, or the storage device 140. The connection may be a wired connection, a wireless connection, any other communication connection that may enable transmission and/or reception of data, and/or any combination of such connections. The wired connection may include, for example, an electrical cable, an optical cable, a telephone line, etc., or any combination thereof. The wireless connection may comprise, for example, BluetoothTMLink, Wi-FiTMLink, WiMaxTMA link, a WLAN link, a ZigBee link, a mobile network link (e.g., 3G, 4G, 5G), etc., or any combination thereof. In some embodiments, the communication port 240 may be and/or include a standardized communication port, such as RS232, RS485, and the like.
Fig. 3 is a schematic diagram of exemplary hardware and/or software components of an exemplary mobile device on which a user terminal may be implemented, according to some embodiments of the present application. In some embodiments, the mobile device 300 shown in FIG. 3 may be used by a user. The user can be related to national defense military personnel, news media related personnel, customs related personnel, foreign exchange related personnel, combat readiness communication related personnel and the like.
As shown in FIG. 3, mobile device 300 may include a communication platform 310, a display 320, a Graphics Processing Unit (GPU)330, a Central Processing Unit (CPU)340, I/O350, memory 360, and storage 390. In some embodiments, any other suitable component, including but not limited to a system bus or a controller (not shown), may also be included in mobile device 300. In some embodiments, the operating system 370 (e.g., iOS) may be movedTM、AndroidTM、WindowsPhoneTM) And one or more applications 380 are loaded from storage 390 into memory 360 toIs executed by CPU 340. The application 380 may include a browser or any other suitable mobile application for receiving and rendering information related to image processing or other information from the processing engine 112. User interaction with the information flow may be enabled through the I/O350 and provided to the processing engine 112 and/or other components of the application scenario 100 through the network 120.
To implement the various modules, units, and their functions described herein, a computer hardware platform may be used as the hardware platform for one or more of the components described herein. A computer with user interface elements may be used to implement a Personal Computer (PC) or any other type of workstation or terminal device. The computer may also function as a server if appropriately programmed.
One of ordinary skill in the art will appreciate that when an element of the application scenario 100 executes, the element may execute via an electrical and/or electromagnetic signal. For example, when processing engine 112 processes a task, such as making a determination or identifying information, processing engine 112 may operate logic circuits in its processor to process the task. When the processing engine 112 transmits data (e.g., frequency bins of an analysis signal to be monitored) to the user terminal 130, the processor of the processing engine 112 may generate an electrical signal encoding the data. The processor of the processing engine 112 may then send the electrical signal to an output port. If the user terminal 130 communicates with the processing engine 112 over a wired network, the output port may be physically connected to a cable that may further transmit the electrical signals to the input port of the server 110. If the user terminal 130 communicates with the processing engine 112 over a wireless network, the output port of the processing engine 112 may be one or more antennas that may convert electrical signals to electromagnetic signals. In an electronic device, such as user terminal 130 and/or server 110, when its processor processes instructions, issues instructions, and/or performs actions, the instructions and/or actions are performed by electrical signals. For example, when a processor retrieves or stores data from a storage medium (e.g., storage device 140), it may send electrical signals to a read/write device of the storage medium, which may read or write structured data in the storage medium. The configuration data may be transmitted to the processor in the form of electrical signals via a bus of the electronic device. Herein, an electrical signal may refer to an electrical signal, a series of electrical signals, and/or one or more discrete electrical signals.
FIG. 4 is a schematic block diagram of an exemplary processing engine shown in accordance with some embodiments of the present application.
As shown in fig. 4, in some embodiments, the processing engine 112 may include an acquisition module 410, a first analysis module 420, a second analysis module 430, a first determination module 440, a setup module 450, a second determination module 460. The processing engine 110 may be implemented on various components (e.g., the processor 210 of the computing device 200 shown in fig. 2). For example, at least a portion of processing engine 110 may be implemented on a computing device as shown in FIG. 2 or a mobile device as shown in FIG. 3.
The acquisition module 410 may be used to acquire satellite signals to be analyzed in a frequency band. In some embodiments, the obtaining module 410 is primarily for obtaining signals to be monitored and analyzed, and the obtaining module 410 may obtain data and/or information related to the application scenario 100 from one or more components of the application scenario 100, such as the information source 150, the storage device 140. For example, the acquisition module 410 may acquire signal data from the information source 150. The signal data may be transferred in various forms such as a picture form, a data form, and the like. The acquisition module 410 may send the signal data to other modules (e.g., the statistics module 420) for further processing. As another example, the acquisition module 410 may acquire satellite signal data from the storage device 140.
In some embodiments, the acquisition module 410 may initiate a monitor search mode, in which the signal of interest is observed as a satellite signal to be analyzed.
In some embodiments, the acquisition module 410 determines the illegal signal because 1610MHz-1660.5MHz is a dedicated frequency band for satellite mobile air service, such as when a non-satellite communication signal is found.
The first analysis module 420 may be configured to analyze whether the acquired satellite signal belongs to a satellite uplink signal or a satellite downlink signal.
In some embodiments, the first analysis module 420 may implement the analysis of whether the acquired satellite signal belongs to a satellite uplink signal or a satellite downlink signal based on the classification model. For more description of the classification model, refer to the content of fig. 7, which is not repeated herein.
In some embodiments, the first analysis module 420 may specifically determine that: in the TDD operating mode, the uplink and downlink share the frequency, and since the coverage area of the downlink frequency is wide, the satellite downlink signal is found in the monitored frequency band, the receiving bandwidth and RBW of the receiver/spectrometer should be adjusted to be appropriate, and the time-frequency diagram (such as a waterfall diagram) of the signal is observed. If the central frequency of the corresponding signal in the time-frequency diagram does not change along with the time, the signal is an uplink signal (without Doppler frequency shift), and if the central frequency changes along with the time, the signal presents a tilt characteristic in the time-frequency diagram, the signal is a downlink signal (with Doppler frequency shift).
Further description of the distinction between uplink and downlink common frequency signals in this specification is omitted here for brevity.
The second analysis module 430 is used for analyzing whether the satellite signals subjected to frequency hopping are frequency changes of the same signal.
In some embodiments, the second analysis module 430 may analyze whether it is a frequency variation of the same signal for satellite signals where frequency hopping occurs based on the evaluation model. For more description of the evaluation model, refer to the content of fig. 8, which is not described herein.
In some embodiments, the specific analysis principle of the second analysis module 430 is: in the FDD working mode, after the communication connection is established, the use frequency is not changed; in the TDD operating system, after the communication connection is established, the frequency used still jumps, so that the device with broadband direction finding capability can be used to observe whether the front and back 2 frequencies have consistency in signal characteristics to determine whether the found frequency change of the same signal is the same.
The first determining module 440 is configured to determine a frequency point of a signal to be analyzed in a satellite signal to be analyzed. In some embodiments, the frequency point of the signal of interest found in the search may be locked by the first determination module 440.
The setting module 450 is used for setting signal analysis parameters; in some embodiments, the signal analysis parameters include center frequency, monitoring bandwidth, direction finding bandwidth, RBW, built-in amplifier gain, attenuation coefficient, and the like.
The second determining module 460 is configured to obtain satellite signal characteristics based on the signal analysis parameters, and determine the identity information of the satellite signal and the signal rule thereof. In some embodiments, by monitoring a signal time domain, a signal frequency domain, a signal modulation domain, a signal feature domain, and the like, and observing a signal spectrum, a signal sound, and the like, it is possible to analyze a modulation type, a coincidence rate, a communication system to which the signal belongs, a signal occurrence rule, and the like of the signal.
In some embodiments, the processing engine 112 may further include a storage module (not shown), and the storage module may record the monitored data for performing offline analysis, for example, the stored data may be used to collect related data, such as audio, if spectrum, IQ data, etc. of the signal, which can reflect the characteristics of the signal, so as to perform further analysis of the signal.
In some embodiments, the processing engine 112 may further include a training module 470, where the training module 470 is configured to train the constructed classification model and the evaluation model according to the acquired data, and obtain a trained classification model and the trained evaluation model.
In some embodiments, the classification model and the evaluation model may be obtained based on training. In some embodiments, the trained samples may include existing satellite signal data. The satellite signal data may be acquired in a variety of ways, such as historically acquired, network acquired, and the like. In some embodiments, data enhancement may be performed on the satellite signal data to increase the number of sample images. Methods of data enhancement include, but are not limited to, flipping, rotating, scaling, cropping, translating, adding noise, and the like. In some embodiments, the state data of the sample data may be marked, which may be done manually or by a computer program. For example only, the satellite signal data may be used as an input of the classification model, and the classification model may be trained with the classification result of the corresponding satellite uplink signal and satellite downlink signal as a correct criterion (Ground Truth). While the model parameters may be adjusted in the reverse direction based on the difference between the predicted output of the model (e.g., the decision threshold of the predicted signal) and the correct criteria. When a predetermined condition is satisfied, for example, the number of training sample images reaches a predetermined number, the predicted accuracy of the model is greater than a predetermined accuracy threshold, or the value of the loss function (LossFunction) is less than a predetermined value, the training process is stopped, and the trained model is designated as the classification model. For more detailed description of the model in this specification, refer to the contents of subsequent fig. 7 and 8, which are not described herein again.
In some embodiments, the processing engine 112 may obtain a classification model. In some embodiments, the classification model may include a trained machine learning model. For example, the trained machine learning model may include a You only look once (YoLO) model, an enhanced Haar model, a FasterR-CNN model, a Mask R-CNN model, the like, or any combination thereof. In some embodiments, the processing engine 112 may obtain the classification model directly from the storage device 140 via the network 120. In some embodiments, the processing engine 112 may obtain a machine learning model and train the machine learning model. For example, a set of sample images and a set of object recognition results (e.g., positive or negative labels, labels of object types) corresponding to the set of sample images may be used to train a machine learning model. The trained machine learning model can be used as a classification model for analyzing whether the satellite signal belongs to the satellite uplink signal or the satellite downlink signal according to the signal data.
In some embodiments, the processing engine 112 may obtain an evaluation model. In some embodiments, the evaluation model may comprise a trained machine learning model. For example, the trained machine learning model may include a You only look once (YoLO) model, an enhanced Haar model, a FasterR-CNN model, a Mask R-CNN model, the like, or any combination thereof. In some embodiments, processing engine 112 may obtain the evaluation model directly from storage 140 via network 120. In some embodiments, the processing engine 112 may obtain a machine learning model and train the machine learning model. For example, a set of sample images and a set of object recognition results (e.g., positive or negative labels, labels of object types) corresponding to the set of sample images may be used to train a machine learning model. The trained machine learning model can be used as an evaluation model for analyzing whether the frequency change of the same signal is generated for the satellite signal with frequency jump.
The modules in the processing engine 112 may be connected to or in communication with each other via a wired connection or a wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, and the like, or any combination thereof. The wireless connection may include a Local Area Network (LAN), a Wide Area Network (WAN), bluetooth, zigbee network, Near Field Communication (NFC), etc., or any combination thereof. Two or more modules may be combined into one module, and any one module may be split into two or more units. For example, the acquisition module 410 may be integrated in the first analysis module 420 as a single module that may identify a mobile terminal and an object associated with the mobile terminal.
It should be understood that the system and its modules shown in FIG. 4 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules in this specification may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).
It should be noted that the above description of the processing engine and its modules is for convenience only and should not limit the present disclosure to the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. For example, the acquiring module and the first analyzing module in fig. 4 may be different modules in one system, or may be one module to implement the functions of the two modules. For another example, the processing engine may share one memory module with each module, and each module may have its own memory module. Such variations are within the scope of the present disclosure.
Fig. 5 is a flow diagram illustrating a method for satellite signal monitoring analysis according to some embodiments in accordance with the present description. In some embodiments, the process 500 shown in FIG. 5 may be implemented in the application scenario 100 shown in FIG. 1. For example, process 500 may be stored as instructions in a storage medium (e.g., storage device 140 or memory 220 of computing device 200) and invoked and/or executed by one or more modules in a processor (e.g., storage device 140), processing engine 112 of server 110, processor 220 of computing device 200, or processing engine 112 shown in fig. 4. The operations of the illustrated process 500 presented below are intended to be illustrative. In some embodiments, process 500 may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the operations of process 500 are illustrated in fig. 5 and described below is not intended to be limiting.
As shown in fig. 5, the process 500 may include the following steps:
step 510, acquiring a satellite signal to be analyzed in a frequency band.
In particular, this step may be performed by the obtaining module 410.
In some embodiments, the signal to be identified may be obtained directly from the information source 150.
In some embodiments, a simple pre-processing may be performed on the number of acquired signals. In some embodiments, the preprocessing of the data includes performing noise reduction, data normalization, feature normalization, and the like. In some embodiments, the acquired signal may include significant noise, so that the data pre-processing may be performed with a preliminary noise reduction of the acquired signal data.
Step 520, analyzing whether the acquired satellite signal belongs to a satellite uplink signal or a satellite downlink signal.
In particular, this step may be performed by the first analysis module 420.
In some embodiments, the first analysis module 420 is mainly used for analyzing whether the acquired satellite signal belongs to a satellite uplink signal or a satellite downlink signal. In some embodiments, the specific analysis flow is as shown in fig. 6.
Step 601, in a TDD working mode, adjusting the receiving bandwidth and RBW to a preset range, and observing a time-frequency diagram of a signal.
Step 602, if the frequency of the corresponding signal center in the time-frequency diagram does not change along with the time, the signal center is determined to be an uplink signal; if the central frequency changes along with the time, the signal presents the inclined characteristic in the time-frequency diagram, and the signal is a downlink signal.
In some embodiments, since the high-orbit satellite mobile communication system is a synchronous orbit satellite, the positional relationship between the synchronous orbit satellites is fixed, and therefore the link is easy to maintain, but the iridium satellite belongs to a low-orbit satellite, the orbit height is 777.84km, the orbit period is 100 minutes and 28 seconds, and the satellite is an extraorbital plane. Due to the low altitude and fast operation speed of the satellite, the doppler shift varies widely and rapidly.
In some embodiments, the low-orbit satellite constellation is comprised of a plurality of satellites in a plurality of orbits. Since the low-earth orbit satellite and the earth are not synchronous, the constellation is constantly changed, the relative position of each satellite is constantly changed, and the relative position relationship (such as link distance, link direction angle, link pitch angle and the like) between the different orbit satellites is time-varying. Not only does the interplanetary link (link for communication between satellites) antenna require some tracking capability, but the satellite link is also difficult to maintain, requiring a switch once in about 250 seconds. According to the Doppler frequency shift definition, downlink signals of the iridium communication system have different degrees of Doppler frequency shifts.
The doppler shift is caused by relative motion between the transmitting and receiving parties in communication. The satellite in the satellite mobile communication system moves to the ground at a high speed. And therefore a larger range of doppler shifts. In some embodiments, the satellite doppler shift may be analyzed using relative doppler:
Figure BDA0003199923400000121
wherein, in the formula fDIs the Doppler shift, f0Is the carrier frequency, vDFor the radial relative velocity between the transmitting and receiving terminals, c is the speed of light, and the following table shows the frequency range of the satellite link and the schematic type of the orbit:
Figure BDA0003199923400000122
it can be seen from the above table that the low orbit mobile satellite has an iridium satellite and a globalstar, but the uplink and downlink of the globalstar are not frequency-shared, so that the frequency-sharing problem only needs to be solved for the iridium satellite, and therefore, if doppler frequency shift is found to occur in the signals of the iridium satellite frequency bands 1610 to 1626.5, the signals are judged to be downlink signals, and other satellites only need to pay attention to the uplink frequency range for signal monitoring.
In TDD operating mode, uplink and downlink share frequency, and since the coverage area of the downlink frequency is wide, a satellite is found to be a 95Kbands downlink signal in a monitored frequency band, the receiving bandwidth and RBW of a receiver/spectrometer should be adjusted to be appropriate, and a time-frequency diagram (such as a waterfall diagram) of the signal is observed. If the central frequency of the corresponding signal in the time-frequency diagram does not change along with the time, the signal is an uplink signal (without Doppler frequency shift), and if the central frequency changes along with the time, the signal presents a tilt characteristic in the time-frequency diagram, the signal is a downlink signal (with Doppler frequency shift).
In step 530, it is analyzed whether the frequency of the satellite signal is changed or not.
In particular, this step may be performed by the second analysis module 430.
In some embodiments, in the FDD operating mode, the frequency of use does not change after the communication connection is established; in the TDD operating system, after the communication connection is established, the frequency still hops, and it is possible to observe whether the front and rear 2 frequencies have consistency in signal characteristics by using a device with a broadband direction finding capability, so as to determine that the found frequency change of the same signal is present.
In some embodiments, this step may be implemented by an evaluation model, and specific descriptions about the evaluation model may refer to the related contents in fig. 7, which are not described herein again.
And 540, determining a signal frequency point to be analyzed in the satellite signal to be analyzed.
In particular, this step may be performed by the first determination module 440. The frequency point of the signal of interest found in the search is locked by the first determination module 440.
Step 550, setting signal analysis parameters.
In particular, this step may be performed by the setup module 450.
In some embodiments, the signal analysis parameters include center frequency, monitoring bandwidth, direction finding bandwidth, RBW, built-in amplifier gain, attenuation coefficient.
In some embodiments, the signal analysis parameters are as shown in the following table:
serial number Parameter(s) Setting reference Remarks for note
1 Center frequency Frequency of signal
2 Monitoring bandwidth Not less than 2 times of signal bandwidth
3 Bandwidth of direction finding Less than or equal to the signal bandwidth Is slightly less than the signal bandwidth
4 RBW 1/100 with monitoring bandwidth
5 Built-in amplifier Adjusting amplifier gain according to signal magnitude
6 Attenuation of 0
And 560, acquiring satellite signal characteristics based on the signal analysis parameters, and determining the satellite signal identity information and the signal rule thereof.
In particular, this step may be performed by the second determination module 460.
In some embodiments, the second determining module 460 may analyze the modulation type, the compliance rate, the affiliated communication system, the signal occurrence rule, and the like of the signal by monitoring the time domain, the frequency domain, the modulation domain, the feature domain, and the like of the signal, observing the features of the signal spectrum, the sound, and the like.
In some embodiments, flow 500 may further include step 570 (not shown): and recording the monitored data, and then performing off-line analysis, for example, acquiring related data which can reflect signal characteristics, such as signal audio, intermediate frequency spectrum, IQ data and the like, based on the stored data, and further analyzing the signals.
It should be noted that the above description is provided for illustrative purposes only, and is not intended to limit the scope of the present application. Various changes and modifications will occur to those skilled in the art based on the description herein. However, such changes and modifications do not depart from the scope of the present application.
In some embodiments, the classification model 730 may be machine learning trained on the initial classification model 720 based on a large number of labeled sample data 710. Wherein the training samples of the initial classification model 720 may be satellite signal data. Similarly, the specific content of the label of the training sample of the initial classification model 720 corresponds to the content to be output by the classification model 730, such as the classification result of the satellite uplink signal and the satellite downlink signal corresponding to the input satellite signal.
In some embodiments, the classification model 730 may be trained jointly based on training samples, with parameters being updated synchronously. In some embodiments, the training module may obtain training samples. The training samples may include the graph neural network data and labels for sample nodes in the graph neural network data.
The training samples comprise graph neural network data and labels of sample nodes in the graph neural network data, the graph neural network data comprises satellite signal data of the sample data 710, and the labels of the sample nodes are classification results of the sample data 710. In some embodiments, the labels of the sample nodes may be obtained by manual entry, reading stored data, invoking an associated interface, or other means.
After sample data is obtained, the training samples are input into the initial classification model 720 to train the initial classification model 720.
In some embodiments, the training module may train by conventional methods based on the training samples. Specifically, the training samples are input into the initial classification model 720, and are trained by a common method based on the prediction results of the sample nodes and the loss function constructed by the sample labels, and parameters of the model are updated at the same time. For example, the training may be performed based on a gradient descent method, an Adaptive matrix estimation (Adam) method. Preferably, the loss function may be a cross entropy loss function or a least squares loss function.
The classification model 730 is obtained after the training is completed. The trained initial classification model 720 can be used as the final classification model 730. Finally, the classification model 730 can be used for classifying the actual signal data to obtain the classification result of whether the satellite signal belongs to the satellite uplink signal or the satellite downlink signal.
FIG. 8 is a training diagram of an assessment model according to some embodiments of the present description. In particular, fig. 8 may be performed by a training module.
In some embodiments, the assessment model 830 may be machine learning trained on the initial assessment model 820 based on a large amount of labeled sample data 810. Wherein the training samples of the initial evaluation model 820 may be satellite signal data. Similarly, the specific content of the label of the training sample of the initial evaluation model 820 corresponds to the content of the output of the evaluation model 830 to be obtained, such as may include the analysis result of whether the input satellite signal subjected to frequency hopping corresponds to the frequency variation of the same signal.
In some embodiments, the evaluation model 830 may be trained jointly based on training samples, with parameters being updated synchronously. In some embodiments, the training module may obtain training samples. The training samples may include the graph neural network data and labels for sample nodes in the graph neural network data.
The training sample comprises graph neural network data and a label of a sample node in the graph neural network data, the graph neural network data comprises satellite signal data of the sample data 810, and the label of the sample node is an evaluation result of the sample data 810. In some embodiments, the labels of the sample nodes may be obtained by manual entry, reading stored data, invoking an associated interface, or other means.
After sample data is obtained, the training sample may be input to the initial evaluation model 820 to train the initial evaluation model 820.
In some embodiments, the training module may train by conventional methods based on the training samples. Specifically, a training sample is input into the initial evaluation model 820, training is performed by a common method based on a prediction result of a sample node and a loss function constructed by a sample label, and parameters of the model are updated at the same time. For example, the training may be performed based on a gradient descent method, an Adaptive matrix estimation (Adam) method. Preferably, the loss function may be a cross entropy loss function or a least squares loss function.
The training is completed to obtain an evaluation model 830. The trained initial assessment model 820 can be used as the final assessment model 730. Finally, the evaluation model 930 can be used to evaluate the actual signal data, and to evaluate whether the frequency hopped satellite signal is the result of frequency variation of the same signal.
The identification of particular satellite signals of embodiments of the present description has beneficial effects including, but not limited to, the following: 1. the acquired signal data can be analyzed and monitored in a frequency band, so that abnormal signals can be found; 2. the model is adopted to analyze whether the acquired satellite signal belongs to a satellite uplink signal or a satellite downlink signal, so that the signal monitoring and analyzing efficiency can be improved; 3. and the model is adopted to analyze whether the satellite signals with frequency hopping are frequency changes of the same signal, so that the abnormity can be further eliminated, and the monitoring accuracy is improved. It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.
The embodiment of the present specification further provides a star signal monitoring and analyzing device, which includes at least one storage medium and at least one processor, where the at least one storage medium is used to store computer instructions; the at least one processor is configured to perform the aforementioned method for identifying a specific satellite signal, the method comprising: acquiring satellite signals to be analyzed in a frequency band; analyzing whether the acquired satellite signal belongs to a satellite uplink signal or a satellite downlink signal; analyzing whether the satellite signals generate frequency hopping or not according to the frequency variation of the same signal; determining a signal frequency point to be analyzed in a satellite signal to be analyzed; setting signal analysis parameters; and acquiring satellite signal characteristics based on the signal analysis parameters, and determining the satellite signal identity information and the signal rule thereof.
The embodiment of the specification also provides a computer readable storage medium. The storage medium stores computer instructions, and after the computer reads the computer instructions in the storage medium, the computer implements the method for monitoring and analyzing the vehicle state, wherein the method comprises the following steps: acquiring satellite signals to be analyzed in a frequency band; analyzing whether the acquired satellite signal belongs to a satellite uplink signal or a satellite downlink signal; analyzing whether the satellite signals generate frequency hopping or not according to the frequency variation of the same signal; determining a signal frequency point to be analyzed in a satellite signal to be analyzed; setting signal analysis parameters; and acquiring satellite signal characteristics based on the signal analysis parameters, and determining the satellite signal identity information and the signal rule thereof.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran2003, Perl, COBOL2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or processing device. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing processing device or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims (10)

1. A method for monitoring and analyzing satellite signals, comprising:
acquiring satellite signals to be analyzed in a frequency band;
analyzing whether the acquired satellite signal belongs to a satellite uplink signal or a satellite downlink signal;
analyzing whether the satellite signals generate frequency hopping or not according to the frequency variation of the same signal;
determining a signal frequency point to be analyzed in a satellite signal to be analyzed;
setting signal analysis parameters;
and acquiring satellite signal characteristics based on the signal analysis parameters, and determining the satellite signal identity information and the signal rule thereof.
2. The method for monitoring and analyzing satellite signals according to claim 1, wherein the analyzing whether the acquired satellite signals belong to satellite uplink signals or satellite downlink signals comprises:
under a TDD working mode, adjusting the receiving bandwidth and RBW to a preset range, and observing a time-frequency diagram of a signal;
if the frequency of the corresponding signal center in the time-frequency diagram does not change along with the time, the signal center is identified as an uplink signal;
if the central frequency changes along with the time, the signal presents the inclined characteristic in the time-frequency diagram, and the signal is a downlink signal.
3. The method of claim 2, wherein the analyzing whether the frequency change of the same signal is the satellite signal with frequency jump comprises:
in the TDD working mode, after the communication connection is established, if the frequency hopping is monitored, whether the front and back 2 frequencies have consistency on the signal characteristics is observed to determine whether the frequency changes of the same signal.
4. The method of claim 1, wherein the signal analysis parameters include center frequency, monitoring bandwidth, direction finding bandwidth, RBW, built-in amplifier gain, attenuation coefficient.
5. A satellite signal monitoring and analysis system, comprising:
the acquisition module is used for acquiring satellite signals to be analyzed in a frequency band;
the first analysis module is used for analyzing whether the acquired satellite signal belongs to a satellite uplink signal or a satellite downlink signal;
the second analysis module is used for analyzing whether the satellite signals subjected to frequency hopping are frequency changes of the same signal;
the first determination module is used for determining a signal frequency point to be analyzed in a satellite signal to be analyzed;
the setting module is used for setting signal analysis parameters;
and the second determination module is used for acquiring satellite signal characteristics based on the signal analysis parameters and determining the satellite signal identity information and the signal rule thereof.
6. The satellite signal monitoring analysis system of claim 5, wherein the first analysis module is further configured to:
under a TDD working mode, adjusting the receiving bandwidth and RBW to a preset range, and observing a time-frequency diagram of a signal;
if the frequency of the corresponding signal center in the time-frequency diagram does not change along with the time, the signal center is identified as an uplink signal;
if the central frequency changes along with the time, the signal presents the inclined characteristic in the time-frequency diagram, and the signal is a downlink signal.
7. The satellite signal monitoring analysis system of claim 6, wherein the second analysis module is further configured to:
in the TDD working mode, after the communication connection is established, if the frequency hopping is monitored, whether the front and back 2 frequencies have consistency on the signal characteristics is observed to determine whether the frequency changes of the same signal.
8. The satellite signal monitoring and analysis system of claim 7, wherein the signal analysis parameters include center frequency, monitoring bandwidth, direction finding bandwidth, RBW, built-in amplifier gain, attenuation coefficient.
9. A satellite signal monitoring and analysis apparatus, the apparatus comprising a processor and a memory; the memory is configured to store instructions, and the instructions, when executed by the processor, cause the apparatus to implement operations corresponding to the satellite signal monitoring and analyzing method according to any one of claims 1 to 4.
10. A computer readable medium, said storage medium storing computer instructions which, when executed by a processor, implement the satellite signal monitoring analysis method according to any one of claims 1 to 4.
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