CN113705363B

CN113705363B - Method and system for identifying uplink signals of specific satellites

Info

Publication number: CN113705363B
Application number: CN202110888830.2A
Authority: CN
Inventors: 张光云; 陈玮玮; 张丹; 刘冬; 曾春娥; 陈丹凤; 钟秋平
Original assignee: Chengdu Dechen Borui Technology Co ltd
Current assignee: Chengdu Dechen Borui Technology Co ltd
Priority date: 2021-08-06
Filing date: 2021-08-06
Publication date: 2024-06-21
Anticipated expiration: 2041-08-06
Also published as: CN113705363A

Abstract

The invention discloses a method for identifying a specific satellite uplink signal, which comprises the following steps: acquiring a signal to be identified; performing end point detection on the signal to be identified, collecting specific data of the signal to be identified, and at least counting duration time of the signal to be identified; the duration of the signal to be identified is processed based on the identification model, and an identification result is obtained, wherein the identification result at least comprises whether the signal to be identified belongs to a specific satellite uplink signal or not, and whether the acquired satellite signal belongs to the specific satellite uplink signal or not can be quickly and accurately identified.

Description

Method and system for identifying uplink signals of specific satellites

Technical Field

The invention relates to the technical field of signal detection, in particular to a method and a system for identifying a specific satellite uplink signal.

Background

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 meanwhile, because of the special use scene, the radio service monitoring range is a relatively blank field, so that a perfect knowledge and monitoring means are established for the satellite mobile communication system.

Therefore, there is a need for an efficient identification method for a specific satellite uplink signal.

Disclosure of Invention

An aspect of an embodiment of the present disclosure provides a method for identifying a specific satellite uplink signal, including: acquiring a signal to be identified; performing end point detection on the signal to be identified, collecting specific data of the signal to be identified, and at least counting duration time of the signal to be identified; and processing the duration time of the signal to be identified based on the identification model to obtain an identification result, wherein the identification result at least comprises whether the signal to be identified belongs to a specific satellite uplink signal.

In some embodiments, the identification model comprises an alignment model, a cluster judgment model and a matching model which are connected in sequence; the comparison module is used for processing the duration time of the signal to be identified and outputting at least one satellite type of the signal to be identified as a first prediction result; the cluster judgment model is used for processing the first prediction result and outputting at least one satellite type corresponding to at least one cluster center of the first prediction result as a second prediction result; the matching model is used for processing the second prediction result and outputting the probability that the signal to be identified is the specific satellite uplink signal; and if the probability meets a preset condition, the signal to be identified is determined to belong to the specific satellite uplink signal.

In some embodiments, the alignment model, the cluster determination model, and the matching model are jointly trained based on training samples, and update parameters synchronously.

In some embodiments, the training sample based joint training comprises:

Obtaining training samples, wherein the training samples comprise graphic neural network data and labels of sample nodes in the graphic neural network data, the graphic neural network data comprises feature vectors of the sample nodes, feature vectors of edges connecting the sample nodes and the graphic neural network structure, and the labels of the sample nodes are satellite categories of the sample nodes;

And inputting the training sample into an initial comparison model, synchronously updating parameters of the initial comparison model, the initial clustering judgment model and the initial matching model based on a result output by the initial matching model, and obtaining a trained comparison model, a trained clustering judgment model and a trained matching model.

In some embodiments, the particular data is time-frequency waterfall plot data.

In some embodiments, the performing endpoint detection on the signal to be identified, collecting specific data of the signal to be identified, and at least counting the duration of the signal to be identified includes:

Filtering the input signal, filtering the signal based on the sampling rate, and collecting multi-frame AD original data; carrying out framing treatment on each frame of AD original data, wherein each frame of AD original data at least comprises time frequency amplitude information; storing the acquired AD original data as time-frequency waterfall diagram data; removing background noise interference for each channel of the acquired time-frequency waterfall diagram data; and carrying out persistence check of the signal existence for each channel, counting the continuous signal persistence frame number of each channel, and calculating the signal duration.

In some embodiments, the persistence check for signal presence for each channel is related to a total number of frames that the signal is persisting, a total number of frames that are less than a threshold in magnitude, and a number of frames that occur consecutively in magnitude that are less than a threshold.

Processing the signal to be identified containing the specific data based on a statistical model and outputting the duration of the signal to be identified; the training sample data of the statistical model is signal data containing the specific data, and the label is the duration of the signal data.

An aspect of embodiments of the present disclosure provides an apparatus for identifying a specific satellite uplink signal, including: the acquisition module is used for acquiring the signal to be identified; the statistics module is used for carrying out end point detection on the signal to be identified, collecting specific data of the signal to be identified and at least counting duration time of the signal to be identified; the identification module is used for processing the duration time of the signal to be identified based on the identification model to obtain an identification result, and the identification result at least comprises whether the signal to be identified belongs to a specific satellite uplink signal or not. .

An aspect of embodiments of the present description provides an apparatus for identifying a particular satellite uplink signal, the apparatus comprising at least one storage medium for storing computer instructions and at least one processor; the at least one processor is configured to execute the computer instructions to implement operations corresponding to the method for identifying the specific satellite uplink signal.

An aspect of the embodiments of the present disclosure provides a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, implement a method for identifying a specific satellite uplink signal.

Drawings

The present specification will be further described by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:

fig. 1 is a schematic view of an application scenario of an identification device for a specific satellite uplink signal 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, shown in accordance with 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, as shown in accordance with some embodiments of the application;

FIG. 4 is a schematic block diagram of an exemplary processing engine shown in accordance with some embodiments of the application;

FIG. 5 is a flow chart of a method of identifying a particular satellite uplink signal according to some embodiments of the present description;

FIG. 6 is an exemplary flow chart of counting the duration of signals to be identified according to some embodiments of the present description

FIG. 7 is an exemplary flow chart of a method of identifying based on an identification model, shown in accordance with some embodiments of the present description;

FIG. 8 is an exemplary flow chart of a joint training alignment model, a cluster determination model, a matching model, shown in accordance with some embodiments of the present description.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.

It should be appreciated that "system," "apparatus," "unit," and/or "module" as used in this specification is a method for distinguishing between different components, elements, parts, portions, or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.

A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.

The satellite in this scheme is artificial satellite, which is a device built by human, launched into space by space flight vehicles such as rocket, space plane, etc., and encircling the earth or other planets like natural satellite. In a mobile satellite communication system, a user segment needs to access a mobile satellite communication network through a ground segment for mobile communication. The communication terminal representing the user section can have different expression forms, such as a handheld terminal or a vehicle-mounted terminal, and the user terminal is used for realizing the setting and acquisition of the communication state of the terminal user by installing a wireless receiving and transmitting antenna so as to complete the communication. For mobile satellite communication networks with different frequency bands, one of the embodiments is that the frequencies used by the user terminals are different, and the user terminals which communicate with different frequency bands have different communication functions and design methods.

The satellite mobile terminal has the characteristics of strong portability, concealment, no region restriction on communication and the like, so that the satellite mobile terminal becomes the first choice of various emergency communication and has wider application. In order to realize effective monitoring of the satellite mobile terminal, the national radio monitoring center carries out intensive research on a monitoring method of uplink signals of main flow satellite mobile communication terminals such as L-band maritime satellites, iridium satellites and the like.

The embodiment of the application provides a method for identifying a specific satellite uplink signal, in particular to the identification of a satellite mobile communication terminal uplink signal such as a maritime satellite. The method principle of the embodiment of the application can be applied to the identification of various satellite uplink signals. It should be understood that the application scenario of the system and method of the present application is merely some examples or embodiments of the present application, and it is possible for those skilled in the art to apply the present application to other similar scenarios according to these drawings without the need of inventive labor. Although the application has been described mainly with reference to marine satellites, it should be noted that the principles of the application are applicable to other satellites as well, and that identification of the uplink signals of these satellites can be achieved in accordance with the principles of the application.

Fig. 1 is a schematic diagram of an application scenario of an exemplary specific satellite uplink signal identification device according to some embodiments of the application. In some embodiments, the application scenario 100 may be configured to identify a particular satellite uplink signal, or the like. The method can be applied to corresponding communication control scenes such as satellite monitoring, satellite identification, satellite management and the like. 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.

Server 110 may be used to identify a particular satellite uplink signal. In some embodiments, the method can be specifically used for identifying the maritime satellite uplink signals, so that maritime satellite monitoring is realized, and the identification technology can be applied to various fields of government departments, national defense armies, news media, customs, foreign exchange, combat readiness communication and the like. The server 110 may identify whether the acquired signal belongs to a specific satellite uplink signal based on the acquired information, thereby determining satellite information.

Server 110 refers to a system with computing capabilities, and in some embodiments, server 110 may be a single server or a group of servers. The server farm may be centralized or distributed (e.g., server 110 may be a distributed system). In some embodiments, 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, the server 110 may be directly connected to the user terminal 130 and/or the storage device 140 to access stored information and/or data. In some embodiments, server 110 may be implemented on a cloud platform. For 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-layer cloud, or 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 shown in FIG. 2 of the present application.

In some embodiments, server 110 may include a processing engine 112. The processing engine 112 may process information and/or data related to satellite signals. For example, processing engine 112 may identify in the information data acquired by information source 150 whether the acquired satellite signals belong to a particular satellite uplink signal. 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), a special 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, and the like, or any combination thereof.

The 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 through the network 120. For example, the processing engine 112 may send information of the identified 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, the network 120 may include a cable network, a wired 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), a Public Switched Telephone Network (PSTN), a Bluetooth (TM) 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 switching 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, or the like, or any combination thereof. In some embodiments, the mobile device 130-1 may include a smart home device, a wearable device, a mobile device, a virtual reality device, an augmented reality device, or 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 embodiments, the wearable device may include a wristband, 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, or the like, or any combination thereof. In some embodiments, the virtual reality device and/or augmented virtual reality device may include a virtual reality helmet, virtual reality glasses, virtual reality eyepieces, augmented reality helmet, augmented reality glasses, augmented reality eyepieces, and the like, or any combination thereof. For example, the virtual reality device and/or the augmented reality device may include google glass ^TM、RiftCon^TM、Fragments^TM、GearVR^TM, or the like.

In some embodiments, the user terminal 130 may be a mobile terminal configured to collect satellite signals. The user terminal 130 may send and/or receive information related to the WeChat 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 captured by the installation in the user terminal 130 to the processing engine 112 or processor installed in the user terminal 120 via a 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 input and/or import signal data to be identified via a user interface. The processing engine 112 may receive the input signal data via a user interface. For another example, the user may input a request to identify the satellite signal via a user interface implemented on the user terminal 130. In some embodiments, in response to the identification request, the user terminal 130 may directly process satellite signal data via a processor of the user terminal 130 based on signal acquisition devices installed in the user terminal 130 described elsewhere in the present application. In some embodiments, in response to the identification request, the user terminal 130 may send the identification request to the processing engine 112 for determining whether the satellite signal belongs to a particular satellite uplink signal based on the information source 150 or signal acquisition device installed elsewhere in the present application. In some embodiments, the user interface may facilitate the presentation or display of information and/or data (e.g., signals) related to satellite identification received from the processing engine 112. For example, the information and/or data may include results indicative of satellite identification content, or satellite information corresponding to the identified satellite signals, etc. 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.

The storage device 140 may store data and/or instructions. In some embodiments, storage device 140 may store data obtained from information source 150. Storage device 140 may store data and/or instructions that processing engine 112 may perform 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), and the like, or any combination thereof. Exemplary mass storage may include magnetic disks, optical disks, solid state drives, and the like. Exemplary removable storage may include flash drives, floppy disks, optical disks, memory cards, compact disks, tape, and the like. Exemplary volatile read-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 (ddr sdram), 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 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 disk read-only memory, and the like. In some embodiments, the storage device 140 may execute on a cloud platform. For 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-layer cloud, or the like, or any combination thereof.

In some embodiments, the 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., the server 110, the user terminal 130). In some embodiments, the storage device 140 may be part of the server 110.

The information source 150 is an acquisition terminal for acquiring satellite signals and uploading the acquired satellite signals. In some embodiments, information source 150 may be used to provide acquired satellite pictures, signal data, satellite video, signal audio, etc. to the system. The information source 150 may be in the form of a single central server, a plurality of servers connected via a network, or a plurality of personal devices. When the information source 150 is in the form of a plurality of personal devices, the devices may be configured to upload text, voice, image, video, etc. to the cloud server in a manner that generates content (user-GENERATED CONTENTS) by a user, so that the cloud server communicates with the plurality of personal devices connected thereto to form the information source 150.

Satellite system 160 may include one or more satellites, such as satellite 160-1, satellite 160-2, and satellite 160-3. The information source 150 is configured to collect signal data of a certain satellite in the satellite system 160 or the whole satellite system 160, and send the collected 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, channel interval, whether there is a frequency shift, etc., according to the signal data, and determine the satellite system to which the signal belongs.

It should be noted that the above description is intended to be illustrative, and not limiting on 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, according to some embodiments of the 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.

Processor 210 (e.g., logic circuitry) may execute computer instructions (e.g., program code) and perform the functions of 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, processor 210 may make a determination of the satellite signal to which it belongs at the signal data acquired by information source 150. For another example, the processor 210 may determine whether it belongs to a particular satellite uplink signal 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. Processor 210 may also send the identified information or decision results to 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), wherein the electrical signals encode structured data and/or instructions for processing by the processing circuit. The processing circuitry may perform logic calculations and then encode conclusions, results, and/or instructions into an electrical signal. The interface circuit may then send the electrical signal from the processing circuit via the bus.

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 (ASICs), application specific instruction set processors (ASIPs), central Processing Units (CPUs), graphics Processors (GPUs), physical Processors (PPUs), microcontrollers, digital Signal Processors (DSPs), field Programmable Gate Arrays (FPGAs), advanced RISC Machines (ARM), programmable Logic Devices (PLDs), 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. It should be noted, however, that the computing device 200 of the present application may also include multiple processors, and thus, operations and/or method steps performed by one processor as described in the present application may also be performed by multiple processors, either in combination or separately. For example, if the processors of computing device 200 perform steps a and B simultaneously in the present application, 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 both the first and second processors performing steps a and B).

Memory 220 may store data/information obtained from user terminal 130, storage device 140, and/or any other component of application scenario 100. In some embodiments, memory device 220 may include a mass memory device, a removable memory device, a volatile read-write memory device, a read-only memory (ROM), and the like, or any combination thereof. For example, mass storage may include magnetic disks, optical disks, solid state drives, and the like. Removable storage devices may include flash memory, floppy disks, optical disks, memory cards, zip disks, tape, and the like. Volatile read-write memory can include Random Access Memory (RAM). The RAM may include Dynamic RAM (DRAM), double rate synchronous dynamic RAM (ddr sdram), static RAM (SRAM), thyristor RAM (T-RAM), zero capacitor RAM (Z-RAM), and the like. The ROM may include Mask 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 exemplary methods described herein. For example, the memory 220 may store programs for the processing engine 112 for determining satellite signals.

I/O230 may input and/or output signals, data, information, etc. In some embodiments, the I/O230 may enable a user to interact with the 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, and the like, or a combination thereof. Examples of output devices may include a display device, speakers, a printer, a projector, etc., or a combination thereof. Examples of display devices may include Liquid Crystal Displays (LCDs), light Emitting Diode (LED) based displays, flat panel displays, curved screens, television devices, cathode Ray Tubes (CRTs), touch screen screens, and the like, or any combination thereof.

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 data transmission and/or reception, and/or any combination of these 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 include, for example, a Bluetooth link, a Wi-FiTM link, a WiMaxTM 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, as shown in accordance with some embodiments of the application. In some embodiments, the mobile device 300 shown in fig. 3 may be used by a user. The user can be a national defense army related person, a news media related person, a customs related person, an outsourcing related person, a combat readiness communication related person 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 controller (not shown), may also be included within mobile device 300. In some embodiments, a mobile operating system 370 (e.g., iOS TM, android TM, windowsPhone TM) and one or more applications 380 may be loaded from storage 390 into memory 360 for execution by CPU 340. 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 processing engine 112. User interaction with the information stream may be accomplished 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 functions thereof described herein, a computer hardware platform may be used as a 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. If the computer is properly programmed, the computer can also be used as a server.

Those of ordinary skill in the art will understand that elements of the application scenario 100 may be performed by electrical and/or electromagnetic signals when the elements are performed. For example, when the processing engine 112 processes a task such as making a determination or identifying information, the processing engine 112 may operate logic circuitry in its processor to process the task. When the processing engine 112 transmits data (e.g., current predicted information of the target satellite) to the user terminal 130, the processor of the processing engine 112 may generate an electrical signal that encodes the data. The processor of the processing engine 112 may then send the electrical signal to the output port. If the user terminal 130 communicates with the processing engine 112 via a wired network, the output port may be physically connected to a cable, which may further transmit electrical signals to an 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 saves data from a storage medium (e.g., storage device 140), it may send an electrical signal to a read/write device of the storage medium, which may read or write structured data in the storage medium. The structural data may be transmitted to the processor in the form of electrical signals over a bus of the electronic device. 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 application.

As shown in fig. 4, in some embodiments, processing engine 112 may include an acquisition module 410, a statistics module 420, and an identification module 430. Processing engine 140 may be implemented on various components (e.g., processor 210 of computing device 200 as shown in fig. 2). For example, at least a portion of processing engine 140 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 acquire data and/or information related to the application scenario 100. In some embodiments, the acquisition module 410 is primarily configured to acquire the signal to be identified, and the acquisition module 410 may acquire 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, and the like. 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. Acquisition module 410 may send the signal data to other modules (e.g., statistics module 420) for further processing. For another example, the acquisition module 410 may acquire satellite signal data from the storage device 140. As yet another example, the acquisition module 410 may acquire the recognition model from the storage device 140.

The statistics module 420 can be used to further process the signal data acquired by the acquisition module 410 to output the desired data. In some embodiments, the statistics module 420 may perform endpoint detection on the signal to be identified, and collect specific data of the signal to be identified to at least count the duration of the signal to be identified. In some embodiments, the specific data of the signal to be identified may be time-frequency waterfall diagram data, and in some embodiments, the specific data of the signal to be identified may also be other data for reflecting the frequency of the signal. In some embodiments, the specific data of the signal to be identified may also be other data for reflecting the start time of the signal to be identified, and in some embodiments, the specific data of the signal to be identified may also be other data for reflecting the vanishing time of the signal to be identified.

The identification module 430 can identify one or more objects from data and/or information related to the application scenario 100. In some embodiments, the recognition module 430 may determine a recognition result based on a duration of processing the signal to be recognized by the recognition model, where the recognition result includes at least whether the signal to be recognized belongs to a specific satellite uplink signal, and in some embodiments, the recognition result may further include a satellite system to which the signal to be recognized belongs; in some embodiments, the identification result may further include specific information of the satellite system, such as an owner, etc.

In some embodiments, the processing engine 112 may further include a training module 440, where the training module 440 is configured to train the constructed recognition model according to the acquired data, and obtain the trained recognition model.

In some embodiments, the recognition model may be derived based on training. In some embodiments, the trained samples may include existing satellite signal data images. The satellite signal data images may be acquired in various ways, such as, for example, historically acquired, network acquired, etc. In some embodiments, satellite signal data image data may be enhanced to increase the number of sample images. Methods of data enhancement include, but are not limited to, flipping, rotating, scaling, cropping, panning, adding noise, and the like. In some embodiments, the status data of the sample image may be marked, either manually or by a computer program. For example only, the model may be trained with the sample image as input and the corresponding satellite type as the correct standard (Ground Truth). While the model parameters may be adjusted inversely based on the differences between the predicted output of the model (e.g., the result of the prediction) and the correct criteria. When a certain preset condition is met, for example, the number of training sample images reaches a predetermined number, the predicted accuracy of the model is greater than a certain predetermined accuracy threshold, or the value of the loss function (LossFunction) is less than a certain preset value, the training process will stop and the trained model will be designated as the state detection model. For more details on the recognition model in this specification, see the following contents of fig. 5 and fig. 6, which are not repeated here.

In some embodiments, the processing engine 112 may obtain the recognition model. In some embodiments, the recognition model may include a trained machine learning model. For example, the trained machine learning model may include You Only Look Once (YOLO) model, enhanced Haar model, fasterR-CNN model, mask R-CNN model, or the like, or any combination thereof. In some embodiments, the processing engine 112 may obtain the recognition 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 machine learning model may be trained using 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. The trained machine learning model may be used as an identification model for identifying satellite signal data.

The modules in the processing engine 112 may be connected to or in communication with each other via wired or wireless connections. The wired connection may include a metal cable, fiber optic cable, hybrid cable, or the like, or any combination thereof. The wireless connection may include a Local Area Network (LAN), wide Area Network (WAN), bluetooth, zigbee network, near Field Communication (NFC), or the like, 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 statistics module 420 as a single module that may identify a mobile terminal and a target associated with the mobile terminal.

It should be understood that the system shown in fig. 4 and its modules 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 then be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design 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 as provided on a carrier medium such as a magnetic disk, 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 of the present specification and its modules may be implemented not only with hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also with software executed by various types of processors, for example, and with 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 of description only and is not intended to limit the present description to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. For example, the acquisition module and the identification 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, each module in the processing engine may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the present description.

Fig. 5 is a flow chart of a method of identifying a particular satellite uplink signal according to some embodiments of 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, the process 500 may be stored as instructions in a storage medium (e.g., the storage device 140 or the memory 220 of the computing device 200) and invoked and/or executed by a processor (e.g., the storage device 140), the processing engine 112 of the server 110, the processor 220 of the computing device 200, or one or more modules of the processing engine 112 shown in fig. 4. The operation of the illustrated process 500 presented below is intended to be illustrative. In some embodiments, process 500 may be accomplished with one or more additional operations not described above and/or without one or more operations discussed. In addition, 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 steps of:

step 510, a signal to be identified is acquired.

Specifically, this step may be performed by the acquisition module 410.

In some embodiments, the signal to be identified may be obtained directly from the information source 150. In order to better explain the principle of the scheme, the scheme is developed by combining the characteristics of a maritime satellite signal system.

In some embodiments, a feature sample library of all maritime satellite uplink signal burst durations may be established based on the acquired signals, and the signal durations of the maritime satellite uplink signals in all communication modes may be summarized as a priori knowledge according to the time characteristics of the maritime satellite uplink signals, and the maritime satellite uplink signal duration feature sample library may be established.

In some embodiments, in order to ensure the comprehensiveness and accuracy of the marine satellite uplink signal duration feature sample library, the duration features of all marine satellite uplink signals may be enumerated and summarized as completely as possible when the time feature library is established, so as to cover the duration of all marine satellite uplink signals, and the time feature library is fully established, so that the marine satellite uplink signal detection method based on the time-frequency waterfall diagram is more accurate and comprehensive.

In some embodiments, a simple preprocessing may be performed on the number of acquired signals. In some embodiments, preprocessing of the data includes noise reduction, data normalization, feature normalization, and the like. In some embodiments, the acquired signal may include significant noise, so that the data preprocessing may perform preliminary noise reduction on the acquired signal data.

And step 520, performing endpoint detection on the signal to be identified, collecting specific data of the signal to be identified, and at least counting the duration of the signal to be identified.

Specifically, this step may be performed by statistics module 420.

In some embodiments, the specific operation of step 520 may be implemented using a model, such as processing the signal to be identified containing the specific data based on a statistical model and outputting the duration of the signal to be identified.

In some embodiments, the training sample data of the statistical model is signal data comprising the particular data, and the label is the duration of the signal data.

Training of the statistical model may be accomplished by training module 440, by way of example only, and the model may be trained with historical base information as input and appropriate similarity values corresponding to the historical base information as the correct criteria (Ground Truth). Meanwhile, the model parameters can be reversely adjusted according to the difference between the predicted output of the model and the correct standard. When a certain preset condition is met, for example, the number of training samples reaches a predetermined number, the prediction accuracy of the model is greater than a certain predetermined accuracy threshold, or the value of the Loss Function (Loss Function) is smaller than a certain preset value, the training process is stopped, and the trained model is designated as the first discriminant model.

In some embodiments, other schemes may be used to implement statistics of the duration of the signal to be identified, and specific reference may be made to the relevant content of fig. 6, which is not described herein.

And step 530, processing the duration of the signal to be identified based on the identification model to obtain an identification result, wherein the identification result at least comprises whether the signal to be identified belongs to a specific satellite uplink signal.

In particular, this step may be performed by the identification module 430.

In some embodiments, reference may be made to the details of fig. 7, and details thereof are omitted herein.

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 may be made by one of ordinary skill in the art in light of the description of the application. However, such changes and modifications do not depart from the scope of the present application.

Fig. 6 is an exemplary flow chart of counting the duration of a signal to be identified, according to some embodiments of the present description, and in particular, the flow 600 may be performed by the statistics module 420.

As shown in fig. 6, the process 600 may include the steps of:

in step 610, filtering is performed on the input signal, filtering is performed on the signal based on the sampling rate, and multi-frame AD raw data is collected.

In some embodiments, when the input signal is subjected to filtering processing, appropriate parameters can be selected according to different sampling rates, signals in a non-attention frequency band are filtered, and then multi-frame AD original data are collected.

Step 620, frame-dividing the AD raw data of each frame, where the AD raw data of each frame includes at least time-frequency amplitude information.

In some embodiments, after a certain period of time of multi-frame original AD data is collected, amplitude information of each channel included in each frame of data reflects two-dimensional information of frequency amplitude. In some embodiments, the acquisition interval time between frames can be set, and is exactly consistent with the hardware working time sequence, and the stored multi-frame time-frequency waterfall diagram data is associated with time, so that the three-dimensional information of the time-frequency amplitude is actually reflected.

And step 630, saving the acquired AD original data as time-frequency waterfall diagram data.

In some embodiments, the acquired AD raw data may also be saved as other suitable data forms, such as a histogram, a waveform chart, etc.

Step 640, removing background noise interference for each channel of the acquired time-frequency waterfall diagram data.

In some embodiments, removing background noise interference for each channel of the acquired time-frequency waterfall diagram data may ensure accuracy of the identification. In some embodiments, to further improve the signal detection accuracy and reduce the omission factor, the adjacent channel amplitude correlation needs to be checked to prevent the signal from jittering on the adjacent channel.

Step 650, perform a persistence check of the signal existence for each channel, count the number of continuous signal persistence frames for each channel, and calculate the signal duration.

In some embodiments, the signal presence persistence check is implemented by counting a total number of signal persistence frames M, a total number of frames N having an amplitude less than a threshold, and a number of frames K having an amplitude less than the threshold, wherein the value of the total number of signal persistence frames M is related to the signal duration of the channel.

In some embodiments, whether a signal is present may be determined from:

If the amplitude of the channel signal is larger than the threshold, the channel signal exists, M is added with 1, and if the amplitude is larger than the threshold, the K value returns to 0;

as described above for the adjacent channel amplitude correlation check, although the channel amplitude is less than the threshold, there is a unique channel amplitude in the adjacent channel that is greater than the threshold, the channel signal is present, M is increased by 1, and the K value is 0;

if M of the channel is greater than a predetermined value, indicating that the channel has been for a certain period of time, after which the channel occasionally has a frame amplitude that, although less than a threshold, is N less than the predetermined value and K is also less than the predetermined value during a recent persistence check on the channel, then it is determined that the channel signal is present, M is increased by 1, and both N and K values are increased by 1;

If N is greater than the preset value or K is greater than the preset value, the continuous characteristic of the channel signal is not met, the disappearance of the channel signal can be judged, K is added with 1, the signal continuity check is finished, the result values of M and K are transferred into the signal pattern judgment, the judgment is finished that the parameter values of M and N are returned to 0, and the K value is added with 1 when the amplitude is smaller than the threshold.

FIG. 7 is an exemplary flow chart of a method of identifying based on an identification model, shown in accordance with some embodiments of the present description.

As shown in fig. 7, the recognition model includes a comparison model 710, a cluster determination model 720, and a matching model 730, which are sequentially connected.

The comparison module 710 is configured to process the duration of the signal to be identified, and output at least one satellite type of the signal to be identified as a first prediction result; the cluster judgment model 720 is configured to process the first prediction result, and output at least one satellite type corresponding to at least one cluster center of the first prediction result as a second prediction result; the matching model 730 is configured to process the second prediction result, and output a probability that the signal to be identified is the specific satellite uplink signal; and if the probability meets a preset condition, the signal to be identified is determined to belong to the specific satellite uplink signal.

In some embodiments, the identification model may enable detection of a marine satellite uplink signal if the signal duration of a channel meets the marine satellite uplink signal characteristics, pattern matching. In some embodiments, a specific recognition procedure for the recognition model is as follows:

The duration of the signal to be identified obtained by the statistics module 420 is input into the comparison model 710, at least one satellite type of the signal to be identified is output by the comparison model 710 as a first prediction result, then the first prediction result is input into the clustering judgment model 720, the clustering judgment model 720 performs clustering judgment on the received at least one satellite type to obtain at least one satellite type corresponding to at least one clustering center as a second prediction result, then the second prediction result is input into the matching model 730, the matching model 730 performs pattern matching detection on the at least one satellite type and a specific satellite uplink signal, wherein the time error is within an allowable range, the probability that the signal to be identified is the specific satellite uplink signal is output as a third prediction result, and finally if the third prediction result, namely the probability is greater than a certain threshold value, such as 95%, the signal to be identified can be considered to belong to the specific satellite uplink signal.

In some embodiments, the comparison model 710, the cluster determination model 720, and the matching model 730 may be jointly trained based on training samples, and update parameters synchronously, and details of the joint training are described in the related description of fig. 8, which is not repeated here.

In some embodiments, the comparison model 710, the cluster determination model 720, and the matching model 730 may also be obtained by separate training: the comparison model 710, the cluster determination model 720, and the matching model 730 may be trained based on training samples, respectively.

Specifically, the comparison model 710 may be machine-learned based on a number of labeled training samples from the initial comparison model 710. Wherein the training samples of the alignment model 710 may be the duration of a plurality of satellite signals. The labels of the training samples of the comparison model 710 may be satellite types corresponding to the duration of the satellite signals. The specific content of the tag corresponds to the content output by the comparison module 710 to be obtained.

The cluster determination model 720 may be machine-learning trained based on a number of labeled training samples for the initial cluster determination model 720. The training samples of the cluster determination model 720 may be satellite type data, among others. Similarly, the specific content of the label of the training sample of the cluster determination model 720 corresponds to the content output by the cluster determination model 720 to be obtained, for example, may include a cluster center corresponding to google satellite type data.

Training samples of the comparison model 710 and the cluster judgment model 720 and labels of the training samples can be obtained from historical data. For example, training samples of the comparison model 710 may be obtained from type data of a plurality of satellite signals recorded in the history data. In some embodiments, the training samples of the comparison model 710, the cluster determination model 720, and the labels of the training samples may be obtained by an online platform (e.g., website, application, etc.). In some embodiments, the training samples of the comparison model 710 and the cluster determination model 720 and the labels of the training samples may also be obtained by manual input, calling related interfaces, and the like. In some embodiments, the training samples of the comparison model 710, the cluster determination model 720, and the labels of the training samples may also be obtained in any other manner.

The matching model 730 may be machine-learning trained on the initial matching model 730 based on a plurality of labeled training samples. The training samples of the matching model 730 may be a plurality of satellite signal data serving as a cluster center. The specific content of the tag of the training sample of the matching model 730 corresponds to the content to be output by the matching model 730, e.g. may include the probability that the satellite signal is a specific satellite uplink signal.

In some embodiments, training samples of the matching model 730 may be obtained based on the cluster determination model 720. The manner in which the training samples of the matching model 730 are obtained may include, but is not limited to, obtaining from local historical data of the cluster determination model 720, obtaining from the comparison model 710 and cluster determination model 720 data downloaded from an online platform, and so forth. The labels of the training samples of the matching model 730 may be obtained from a variety of ways including, but not limited to, historical data, call related interfaces, online platforms, and the like.

In some embodiments, the training module 440 may train the comparison model 710, the cluster determination model 720, and the matching model 730 by common methods based on training samples. For example, training may be based on a gradient descent method, an adaptive matrix estimation (Adaptive momentestimation, adam) method. In some embodiments, training is ended when the trained comparison model 710, cluster determination model 720, and matching model 730 satisfy a preset condition. The preset condition may be that the loss function result converges or is smaller than a preset threshold value, etc. In some embodiments, the loss function may be a cross entropy loss function or a least squares loss function.

It should be noted that although the comparison model 710, the cluster determination model 720, and the matching model 730 are described separately above, in some embodiments they may be combined into one model that may determine the satellite type and whether it belongs to a particular satellite uplink signal based on different satellite data. For example, the model is input with the duration, the vanishing time, the start time, and the like of a plurality of satellite signals, and is output as a result of determination as to whether or not the model belongs to a specific satellite uplink signal. The model training process and the discriminating process and the predicting evaluating process can be performed separately. In some embodiments, the training process may be performed on the server 110, or may be performed on another device, and the trained model is applied to the server 110.

FIG. 8 is an exemplary flow diagram of a joint training comparison model 710, a cluster determination model 720, a matching model 730, according to some embodiments of the present description. Specifically, FIG. 8 may be performed by training module 440.

In some embodiments, the comparison model 710, the cluster determination model 720, and the matching model 730 may be jointly trained based on training samples, with the parameters updated synchronously. As shown in fig. 8, the process 800 may include:

step 810, obtaining training samples.

In some embodiments, training module 440 may obtain training samples. The training samples may include graph neural network data and labels of sample nodes in the graph neural network data.

The training samples comprise graphic neural network data and labels of sample nodes in the graphic neural network data, the graphic neural network data comprises feature vectors of the sample nodes, feature vectors of edges connecting the sample nodes and the graphic neural network structure, and the labels of the sample nodes are satellite categories of the sample nodes.

In some embodiments, the labels of the sample nodes may be categories to which the entity objects correspond. In some embodiments, the labels of the sample nodes may be obtained by manual entry, reading of stored data, invoking a related interface, or other means.

And step 820, inputting the training samples into the initial comparison model 710, synchronously updating parameters of the initial comparison model 710, the initial cluster judgment model 720 and the initial match model 730 based on the result output by the initial match model 730, and obtaining a trained comparison model 710, a trained cluster judgment model 720 and a trained match model 730.

In some embodiments, the training module 440 may train by common methods based on training samples. Specifically, the training samples are input into an initial comparison model 710, feature vectors of sample nodes in the training samples are sequentially processed by the initial comparison model 710, the initial clustering judgment model 720 and the initial matching model 730, a prediction result of the sample nodes is output, and training is performed by a common method based on the prediction result of the sample nodes and a loss function constructed by sample labels, and parameters of all models are updated. For example, training may be based on a gradient descent method, an adaptive matrix estimation (Adaptive momentestimation, adam) method. Preferably, the loss function may be a cross entropy loss function or a least squares loss function.

The identification of specific satellite uplink signals of embodiments of the present description has benefits including, but not limited to, the following: 1. in the aspect of signal layer processing, the existing identification method is to perform frequency domain analysis on satellite uplink signals, and adopt an analysis method of sampling and demodulation, and the scheme combines the intermittent duration time characteristic of the signals in the time domain, so that the detection accuracy is ensured; 2. the existing identification method is time-consuming in identification and time-sharing removal operation, low in accuracy and unstable, and the scheme is automatically realized by combining a training model, so that accuracy is guaranteed, and meanwhile efficiency is improved. 3. The combined training of the comparison model 710, the cluster judgment model 720 and the matching model 730 not only reduces the number of required samples, but also improves the training efficiency.

The embodiment of the specification also provides an identification device of a specific satellite uplink signal, which comprises at least one storage medium and at least one processor, wherein the at least one storage medium is used for storing computer instructions; the at least one processor is configured to perform the foregoing method for identifying a specific satellite uplink signal, where the method includes: acquiring a signal to be identified; performing end point detection on the signal to be identified, collecting specific data of the signal to be identified, and at least counting duration time of the signal to be identified; and processing the duration time of the signal to be identified based on the identification model to obtain an identification result, wherein the identification result at least comprises whether the signal to be identified belongs to a specific satellite uplink signal.

The present description also provides a computer-readable storage medium. The storage medium stores computer instructions, and when the computer reads the computer instructions in the storage medium, the computer realizes the identification method of the specific satellite uplink signal, and the method comprises the following steps: acquiring a signal to be identified; performing end point detection on the signal to be identified, collecting specific data of the signal to be identified, and at least counting duration time of the signal to be identified; and processing the duration time of the signal to be identified based on the identification model to obtain an identification result, wherein the identification result at least comprises whether the signal to be identified belongs to a specific satellite uplink signal.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.

Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.

Furthermore, those skilled in the art will appreciate that the various aspects of the specification can be illustrated and described in terms of several patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the present 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 a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the specification may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.

The computer storage medium may contain a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. 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 through any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.

The computer program code necessary for operation of portions of the present description 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 language, visual Basic, fortran2003, perl, COBOL2002, PHP, ABAP, a dynamic programming language such as Python, ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer or 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 form of network, 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 the use of services such as software as a service (SaaS) in a cloud computing environment.

Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative 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 included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing processing device or mobile device.

Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure does not imply that the subject matter of the present description requires more features than are set forth in the claims. Indeed, less than all of the features of a single embodiment disclosed above.

In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.

Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims

1. A method for identifying a particular satellite uplink signal, comprising:

Acquiring a signal to be identified;

Performing end point detection on the signal to be identified, collecting specific data of the signal to be identified, and at least counting duration time of the signal to be identified;

Processing the duration time of the signal to be identified based on an identification model to obtain an identification result, wherein the identification result at least comprises whether the signal to be identified belongs to a specific satellite uplink signal or not;

The identification model comprises a comparison model, a clustering judgment model and a matching model which are connected in sequence;

the comparison module is used for processing the duration time of the signal to be identified and outputting at least one satellite type of the signal to be identified as a first prediction result;

The cluster judgment model is used for processing the first prediction result and outputting at least one satellite type corresponding to at least one cluster center of the first prediction result as a second prediction result;

the matching model is used for processing the second prediction result and outputting the probability that the signal to be identified is the specific satellite uplink signal as a third prediction result;

If the third prediction result is larger than a preset threshold value, the signal to be identified is determined to belong to the specific satellite uplink signal;

The comparison model, the clustering judgment model and the matching model are jointly trained based on training samples, and parameters are synchronously updated;

The joint training includes:

2. The method for identifying a specific satellite uplink signal according to claim 1, wherein the performing endpoint detection on the signal to be identified, collecting specific data of the signal to be identified, and at least counting duration of the signal to be identified includes:

filtering the input signal, filtering the signal based on the sampling rate, and collecting multi-frame AD original data;

Carrying out framing treatment on each frame of AD original data, wherein each frame of AD original data at least comprises time frequency amplitude information;

Storing the acquired AD original data as time-frequency waterfall diagram data;

Removing background noise interference for each channel of the acquired time-frequency waterfall diagram data;

And carrying out persistence check of the signal existence for each channel, counting the continuous signal persistence frame number of each channel, and calculating the signal duration.

3. The method of claim 2, wherein the persistence check for signal presence for each channel is related to a total number of frames that the signal is continuous with, a total number of frames that the amplitude is less than a threshold, and a number of frames that the amplitude is continuously less than a threshold.

4. The method for identifying a specific satellite uplink signal according to claim 2, wherein the performing endpoint detection on the signal to be identified, collecting specific data of the signal to be identified, and at least counting duration of the signal to be identified includes:

processing the signal to be identified containing the specific data based on a statistical model and outputting the duration of the signal to be identified;

The training sample data of the statistical model is signal data containing the specific data, and the label is the duration of the signal data.

5. An apparatus for identifying a particular satellite uplink signal, comprising:

The acquisition module is used for acquiring the signal to be identified;

The statistics module is used for carrying out end point detection on the signal to be identified, collecting specific data of the signal to be identified and at least counting duration time of the signal to be identified;

the identification module is used for processing the duration time of the signal to be identified based on the identification model to obtain an identification result, wherein the identification result at least comprises whether the signal to be identified belongs to a specific satellite uplink signal or not;

The identification model comprises a comparison model, a clustering judgment model and a matching model which are connected in sequence; the comparison module is used for processing the duration time of the signal to be identified and outputting at least one satellite type of the signal to be identified as a first prediction result; the cluster judgment model is used for processing the first prediction result and outputting at least one satellite type corresponding to at least one cluster center of the first prediction result as a second prediction result; the matching model is used for processing the second prediction result and outputting the probability that the signal to be identified is the specific satellite uplink signal as a third prediction result; if the third prediction result is larger than a preset threshold value, the signal to be identified is determined to belong to the specific satellite uplink signal;

The training module is used for training the constructed recognition model according to the acquired data to acquire the recognition model after training; the comparison model, the clustering judgment model and the matching model are jointly trained based on training samples, and parameters are synchronously updated;

The joint training includes: obtaining training samples, wherein the training samples comprise graphic neural network data and labels of sample nodes in the graphic neural network data, the graphic neural network data comprises feature vectors of the sample nodes, feature vectors of edges connecting the sample nodes and the graphic neural network structure, and the labels of the sample nodes are satellite categories of the sample nodes; and inputting the training sample into an initial comparison model, synchronously updating parameters of the initial comparison model, the initial clustering judgment model and the initial matching model based on a result output by the initial matching model, and obtaining a trained comparison model, a trained clustering judgment model and a trained matching model.

6. A communication mode determination apparatus comprising at least one storage medium for storing computer instructions and at least one processor; the at least one processor is configured to execute the computer instructions to implement the method of any one of claims 1-4.

7. A computer readable storage medium storing computer instructions which, when executed by a processor, implement a method as claimed in any one of claims 1 to 4.