CN113485212A - Broadband satellite signal intelligent identification system - Google Patents
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Abstract
The invention discloses an intelligent identification system for broadband satellite signals, which relates to the technical field of broadband radio frequency processing and intelligent rapid signal detection and solves the technical problems of complexity, difficult longitudinal association and low signal identification processing speed of the traditional satellite equipment; the system comprises a transfer switching device, a signal monitoring device and a signal control and guard device; according to the invention, full-band signal parameters are continuously and automatically identified and analyzed through intelligent monitoring application, whole network monitoring and group path control and guard are realized through resource management scheduling guidance, and satellite signals in a large broadband are identified and controlled and guard are realized; the intelligent monitoring system for the satellite broadband signals, provided by the invention, trains an intelligent model by using an artificial intelligence algorithm on the basis of large-scale sample data, so that the signal attributes can be accurately judged under the condition that the monitored signals only acquire a small number of hierarchical parameters, and then the parameters of each hierarchy are correlated, thereby solving the problem of difficult longitudinal correlation of the parameters and improving the monitoring effectiveness and the operational efficiency.
Description
Technical Field
The invention belongs to the field of satellite signal monitoring, relates to a broadband radio frequency processing and intelligent rapid signal detection technology, and particularly relates to a broadband satellite signal intelligent identification system.
Background
With the development of satellite communication technology, the bandwidth of a satellite communication frequency band is wider, the types of signals are more, and the content of the signals is more complex. At present, in the field of satellite monitoring, monitoring equipment based on radio frequency, intermediate frequency and carrier waves coexist according to different actual working scenes, and the problems of multiple types of monitoring equipment, long strain iteration period, difficult longitudinal association, narrow signal real-time processing bandwidth and the like exist in practical application, so that the requirements of the satellite technology development on the monitoring equipment cannot be met.
In view of the above, it is necessary to provide a broadband satellite signal full detection and full control system, which applies techniques such as large broadband radio frequency processing, intelligent fast signal detection/identification/decoding, and artificial intelligence to efficiently monitor a large bandwidth satellite signal.
Disclosure of Invention
The invention provides a broadband satellite signal intelligent identification system which is used for solving the technical problems of complexity, difficult longitudinal association and low signal identification processing speed of the traditional satellite equipment.
The purpose of the invention can be realized by the following technical scheme: broadband satellite signal intelligent recognition system includes:
the transfer switching equipment is used for receiving satellite radio frequency signals; the transfer switching equipment is respectively communicated and/or electrically connected with the signal monitoring equipment and the signal control and guard equipment; the signal control and guard equipment comprises demodulation decoding equipment and demodulation decoding hot backup equipment;
the signal monitoring equipment and the signal control and guard equipment are in communication connection with a first switching network and a second switching network respectively;
the intelligent broadband satellite signal identification system monitors and analyzes the broadband satellite signals according to the system working mode; the system working mode comprises a full-band automatic monitoring mode, a whole network monitoring mode, a group road control mode and an intelligent model training mode.
Preferably, the broadband satellite signal intelligent identification system adopts a B/S architecture.
Preferably, the signal monitoring equipment comprises a dual-channel radio frequency acquisition card, an SCPC/DVB TDMA demodulation processing card, a high-performance server and a GPU accelerator card;
the double-channel radio frequency acquisition card is used for receiving L-frequency band signals and is in communication connection with a DDC high-speed special network and a PCIe4.0 bus.
Preferably, the pci 4.0 bus is further in communication connection with a DVB/TDMA group SCPC unit, a plurality of GPU accelerator cards, a CPU and a storage unit;
the DVB/TDMA group SCPC unit is also connected with a gigabit network, and meanwhile, the DVB/TDMA group SCPC unit can also receive L-band signals.
Preferably, the full-band automatic monitoring mode performs automatic analysis on the full-band channel parameters, and includes:
the signal monitoring equipment carries out digital acquisition on the input broadband satellite signal to generate signal data;
performing FFT (fast Fourier transform) and DDC (direct digital control) processing on the signal data by an FPGA (field programmable gate array) to form a broadband digital frequency spectrum and a plurality of paths of DDC data;
collecting channel data in real time, and identifying signals needing important monitoring;
automatically monitoring and analyzing signals needing to be monitored in a key way through monitoring equipment, acquiring corresponding signal characteristic parameters, and storing the signal characteristic parameters;
and repeating the automatic analysis step to realize the real-time monitoring of the full-band satellite.
Preferably, the whole network monitoring mode is used to obtain a VSAT network scale, and implement whole network monitoring, and includes:
acquiring network control channel parameters of a networked control channel satellite communication network through full-band monitoring;
monitoring a network control channel in real time, and acquiring the number of stations, network parameters and station working parameters of a VSAT network;
applying for sufficient monitoring channels according to the number of the VSAT network stations; dynamically sending VSAT network parameters and platform working parameters to a monitoring channel to realize the whole-network platform control of the VSAT network;
and the signal data generated by the station control and guard of the whole website is sent to the back-end information processing system through the data transmission service.
Preferably, the group link control mode controls and guards the group link signal according to the need of the control and guard task, including:
acquiring the number of signal carriers of each group by monitoring the full frequency band;
applying for group path control equipment according to the number of the group path carriers;
performing real-time supervision on the group channel signals through group channel supervision equipment, and performing demodulation and decoding on the group channel signals through a group channel demodulation and decoding channel to obtain communication information;
collecting communication information and sending data to a back-end information processing system;
continuously controlling and guarding each group of channel signals, and dynamically adjusting resources according to carrier wave change.
Preferably, the intelligent model training mode is used for training the monitoring and supervising model, and includes:
carrying out characteristic marking on data obtained by signal monitoring to obtain target data; the target data is obtained by manual marking or machine marking, and the type of the characteristic marking comprises signal video data, analog signal data or digital signal data;
constructing an intelligent model; the intelligent model comprises one or more of an error inverse feedback neural network, an RBF neural network and a deep convolutional neural network;
and training the intelligent model through target data to obtain a monitoring and supervising model, and applying the monitoring and supervising model to signal monitoring.
Preferably, the broadband satellite signal intelligent recognition system analyzes and processes the satellite signal, and includes:
collecting, registering and managing system hardware resources, configuring a logic processing channel for each L frequency band, and providing a monitoring analysis channel for the L frequency band full frequency band;
performing radio frequency acquisition on a bandwidth signal with a specific frequency in the multi-channel L-band signal through a radio frequency acquisition card, and acquiring the DDC data of a band-pass signal;
the band-pass signal DDC data is sent to a PCIe bus through a high-speed DDC special network according to system configuration through the FPGA, and then is sent to a GPU;
identifying and analyzing signal parameters of the equipment in a full-band automatic monitoring mode, accessing an L-band analog signal into an SCPC/DVB TDMA demodulation processing card, and receiving DDC data from a DDC high-speed private network; monitoring the whole network through the FPGA and the DVB processing module according to the configuration parameters;
and performing auxiliary marking on the signal resources to obtain marking signals, and performing model training on the marking signals by using GPU resources.
Preferably, before performing radio frequency acquisition on the L-band signal, analog-to-digital conversion processing needs to be performed on the L-band signal.
Preferably, the specific frequency is a 1.2GHz bandwidth signal.
Preferably, the transit switching device is specifically an active multi-path power divider or a switching matrix.
Preferably, the demodulation decoding device is a signal control device equipped with an SCPC/DVB TDMA demodulation processing card.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention continuously and automatically identifies and analyzes full-frequency-band signal parameters through intelligent monitoring application, realizes whole-network monitoring and group control through resource management scheduling guidance, and adopts a broadband processing technology to acquire satellite signals with the bandwidth of up to 1.2GHz, FFT (fast Fourier transform) and DDC (direct digital control) processing; the satellite signals in the large broadband are identified, controlled and watched.
The broadband satellite signal intelligent identification system provided by the invention generates a signal sample database by extracting parameters and marking characteristics of each level of a large amount of signal data, trains an intelligent model by using an artificial intelligence algorithm on the basis of large-scale sample data, and achieves intelligent judgment capability, so that the signal attribute can be accurately judged under the condition that a monitored signal only acquires a small amount of level parameters, and then each level parameter is correlated, thereby solving the problem of difficult longitudinal correlation of parameters and improving monitoring effectiveness and operational efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the system connection of the present invention;
FIG. 2 is a schematic diagram of a signal monitoring device according to the present invention;
FIG. 3 is a schematic diagram illustrating the automatic signal recognition principle of the present invention;
fig. 4 is a schematic diagram of the working principle of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used herein is for the purpose of describing embodiments and is not intended to be limiting and/or limiting of the present disclosure; it should be noted that the singular forms "a," "an," and "the" include the plural forms as well, unless the context clearly indicates otherwise; also, although the terms first, second, etc. may be used herein to describe various elements, the elements are not limited by these terms, which are only used to distinguish one element from another.
Referring to fig. 1, the present embodiment provides an intelligent identification system for broadband satellite signals, including: the system comprises a transfer switching device, a plurality of signal monitoring devices and a signal control and guard device. The relay switching device in this embodiment is mainly an active multi-path power divider and/or an interaction matrix. The signal control and guard device in the application comprises a demodulation and decoding hot backup device and a demodulation and decoding device.
The transfer switching equipment is used for receiving satellite radio frequency signals and is also in communication connection with the signal control and guard equipment; the output end of the demodulation decoding equipment is simultaneously in communication connection with a plurality of signal monitoring equipment, and the output end of the signal monitoring equipment is in communication connection with the first switching network and the second switching network.
The satellite radio frequency signal is transmitted to the transfer switching equipment, and is transmitted to the signal monitoring equipment after being transferred. The DDC data is transmitted between the signal monitoring equipment and the first exchange network, and meanwhile, the monitoring data is transmitted to the second exchange network. The signal monitoring equipment also realizes operation, maintenance, operation and control through a second switching network.
The first switching network is also communicated with the cloud service device and the storage device (database).
Referring to fig. 2, the signal monitoring device in this embodiment includes a dual-channel rf acquisition card, an SCPC/DVB TDMA demodulation processing card, a high performance server, and a GPU acceleration card; the dual-channel radio frequency acquisition card comprises a signal preprocessing unit, two analog-to-digital converters (ADC 12DJ 3200) and an FPGA unit; the analog-to-digital converter ADC12DJ3200 used in this embodiment is a 2-bit ADC, which has the following advantages: 1) the fastest sampling rate 6.4GSPS is achieved in the industry under the 12-bit resolution, and the sampling rate is 18% -ADC12DJ3200 faster than that of similar equipment, so that designers can adopt the widest bandwidth, and more information can be processed in time; 2) direct Radio Frequency (RF) sampling up to 10GHz is adopted, L wave band, S wave band and C wave band are covered, and the direct Radio Frequency (RF) sampling is extended to X wave band, so that the complexity of a filter is reduced, the system architecture can be further simplified, stronger frequency agility is provided, the space of a circuit board is saved, and the number of components is reduced; 3) the device integrates the whole RF-to-bit receiver, and compared with the similar solutions, the device can reduce the space of a circuit board by 88% at most, and simultaneously, a designer can reduce the cost by simplifying the system architecture; 4) the power consumption is as low as 3W, and the input frequency range is twice that of the same kind of equipment with halved power.
The dual-channel radio frequency acquisition card is connected with an external clock, the signal preprocessing unit receives an L-frequency band signal, the satellite signal is input to the analog-to-digital converter after being preprocessed, and finally the satellite signal is input to the FPGA unit.
The unit receives a B code, namely an IRIG-B code; the FPGA unit also exchanges data with a DDC high-speed special 100G network and a PCIe4.0 bus.
The bus is also in communication connection with the tera network, the 64-core CPU, the storage unit (database), two GPU acceleration cards (GPU acceleration card 1 and GPU acceleration card 2) and a DVB/TDMA group channel SCPC unit; the DVB/TDMA cluster SCPC unit communicates with the gigabit network and also receives L-band signals.
The application provides a broadband satellite signal intelligent recognition system includes four kinds of operating modes, specifically is full frequency channel automatic monitoring mode, whole network monitoring mode, crowd's way accuse guard's mode and intelligent model training mode.
Referring to fig. 3, the intelligent model training mode is an intelligent model training mode, which trains a monitoring and supervising model by using target data labeled manually or by a machine and by using CPU and GPU resources of the system and by using an artificial intelligence algorithm, and pushes the training model to a monitoring and supervising application, thereby improving effectiveness and operational efficiency of the monitoring model; the method comprises the following steps:
carrying out characteristic marking on data obtained by signal monitoring to obtain target data; the target data is obtained by manual marking or machine marking, and the type of the characteristic marking comprises signal video data, analog signal data or digital signal data;
constructing an intelligent model; the intelligent model comprises one or more of an error inverse feedback neural network, an RBF neural network and a deep convolution neural network; the convolutional neural network is the most common and basic one in deep learning, and the convolutional layer, the activation function and the pooling layer are repeatedly used for multiple times to form the deep neural network. The learning process is automatic and features are abstractly combined. And (3) realizing broadband signal characteristic analysis by utilizing deep learning, and learning signal characteristics from a big data sample through a deep neural network.
And training the intelligent model through target data to obtain a monitoring and supervising model, and applying the monitoring and supervising model to signal monitoring. By using a machine learning method, a machine is trained through a certain batch of sample data with signal feature labels, and various features which are automatically learned by the machine are used for replacing a feature extraction module which is manually designed by research personnel, so that the aim of finally classifying and identifying the target is fulfilled. Specifically, as shown in fig. 3, after the satellite signals are collected and preprocessed, a recognition model based on transfer learning and generation of an antagonistic neural network is trained by using deep learning, and signal recognition is realized.
Referring to fig. 4, the broadband satellite signal intelligent identification system provided by the present application adopts a B/S architecture, a "platform + service + application" mode. The method comprises signal service, information service and transmission service, and comprises four applications of resource management and scheduling, full-band monitoring, whole-network monitoring and group control.
Broadband satellite signal intelligent recognition system carries out analysis processes to the satellite signal, includes:
collecting, registering and managing system hardware resources, configuring a logic processing channel for each L frequency band, and providing a monitoring analysis channel for the L frequency band full frequency band;
performing radio frequency acquisition on a bandwidth signal with a specific frequency in the multi-channel L-band signal through a radio frequency acquisition card, and acquiring the DDC data of a band-pass signal;
the band-pass signal DDC data is sent to a PCIe bus through a high-speed DDC special network according to system configuration through the FPGA, and then is sent to a GPU;
identifying and analyzing signal parameters of the equipment in a full-band automatic monitoring mode, accessing an L-band analog signal into an SCPC/DVB TDMA demodulation processing card, and receiving DDC data from a DDC high-speed private network; monitoring the whole network through the FPGA and the DVB processing module according to the configuration parameters;
and performing auxiliary marking on the signal resources to obtain marking signals, and performing model training on the marking signals by using GPU resources.
The full-band automatic monitoring mode in the application is an important support for system resource allocation and is a main basis for configuring channels by other modes. In a full-band automatic monitoring mode, resources provided by resource management and scheduling are utilized to automatically monitor and analyze related channel parameters in a full band, related results are fed back to the resource management and scheduling through a message bus, and the related monitoring results are stored in a database; the method comprises the following steps:
the signal monitoring equipment carries out digital acquisition on the input broadband satellite signal to generate signal data;
performing FFT (fast Fourier transform) and DDC (direct digital control) processing on the signal data by an FPGA (field programmable gate array) to form a broadband digital frequency spectrum and a plurality of paths of DDC data;
collecting channel data in real time, and identifying signals needing important monitoring;
automatically monitoring and analyzing signals needing to be monitored in a key way through monitoring equipment, acquiring corresponding signal characteristic parameters, and storing the signal characteristic parameters;
and repeating the automatic analysis step to realize the real-time monitoring of the full-band satellite.
The whole network monitoring mode in the application refers to the whole network monitoring of a communication network with a network control channel, VSAT network parameters are obtained through the whole frequency band monitoring, the scale of the VSAT network is obtained through the real-time network control channel, sufficient logic channels are applied to resource management and dispatching, related parameters are dynamically sent to related channels to realize the whole network monitoring, and data are sent to a rear-end information processing system through data transmission service; dynamically reporting the relevant state of each channel to the resource management scheduling, and receiving the control right of the resource management scheduling on the resource; the method comprises the following steps:
acquiring network control channel parameters of a networked control channel satellite communication network through full-band monitoring;
monitoring a network control channel in real time, and acquiring the number of stations, network parameters and station working parameters of a VSAT network;
applying for sufficient monitoring channels according to the number of the VSAT network stations; dynamically sending VSAT network parameters and platform working parameters to a monitoring channel to realize the whole-network platform control of the VSAT network;
and the signal data generated by the station control and guard of the whole website is sent to the back-end information processing system through the data transmission service.
The whole network monitors and dynamically reports the relevant state of each control channel to the resource management scheduling, and receives the control of the resource management scheduling on the resources.
The group channel monitoring mode in the application allocates a group of signal carriers to each device for monitoring according to the monitoring task requirement, allocates a logic demodulation decoding channel to the mode through the full-band automatic monitoring and acquisition of each carrier parameter and the resource management scheduling, and continuously collects the result data and sends the data to a rear-end information processing system through data transmission service; the method comprises the following steps:
acquiring the number of signal carriers of each group by monitoring the full frequency band;
applying for group channel control equipment, specifically a group channel SCPC/TDMA demodulator, according to the number of the group channel carriers;
performing real-time supervision on the group channel signals through group channel supervision equipment, and calling a plurality of group channel demodulation and decoding channels to demodulate and decode the group channel signals to acquire communication information;
the group control application collects the communication information and sends the data to the back-end information processing system;
the group control application continuously controls and guards each group of channel signals and dynamically adjusts resources according to carrier wave changes.
The application provides a broadband satellite signal intelligent recognition system has solved traditional satellite monitoring system equipment complicacy, vertical correlation is difficult, artifical participation degree is high, signal identification processing speed is slow scheduling problem, adopt techniques such as big broadband radio frequency processing, intelligent quick signal detection/discernment/decoding, artificial intelligence, the monitoring of big bandwidth satellite signal of efficient realization, promote to the automation of big bandwidth satellite signal monitoring analysis, intelligent level, improve speed and the rate of accuracy that satellite signal analysis and handled. The specific reason for the difficulty in longitudinal association is that parameters (link layer, network layer, transmission layer, application layer, etc.) of different levels of satellite signals can be associated and identified with each other, the parameters of different levels are difficult to longitudinally associate, and the monitored parameters of a certain level of the signals cannot identify signal attributes. According to the method, a large amount of signal data are subjected to parameter extraction and feature labeling at each level to generate a signal sample database, and on the basis of large-scale sample data, an artificial intelligence algorithm is used for training a monitoring model to achieve intelligent judgment capacity, so that signal attributes can be accurately judged under the condition that a monitored signal only acquires a small amount of level parameters, and then the level parameters are correlated.
The working principle of the invention is as follows:
collecting, registering and managing system hardware resources, configuring a logic processing channel for each L frequency band, and providing a monitoring analysis channel for the L frequency band full frequency band; performing radio frequency acquisition on a 1.2GHz bandwidth signal in the multi-channel L-band signal through a radio frequency acquisition card, and acquiring DDC data of a band-pass signal;
the band-pass signal DDC data is sent to a PCIe bus through a high-speed DDC special network according to system configuration through the FPGA, and then is sent to a GPU;
identifying and analyzing signal parameters of the equipment in a full-band automatic monitoring mode, accessing an L-band analog signal into an SCPC/DVB TDMA demodulation processing card, and sampling through an on-board AD (analog-to-digital) or directly receiving DDC (direct digital control) data from a DDC high-speed private network; monitoring the whole network through the FPGA and the DVB processing module according to the configuration parameters; performing auxiliary labeling on signal resources to obtain a labeled signal, and performing model training on the labeled signal by using GPU resources; and visually displaying the monitoring result.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (10)
1. Broadband satellite signal intelligent recognition system, its characterized in that includes:
the transfer switching equipment is used for receiving satellite radio frequency signals; the transfer switching equipment is respectively communicated and/or electrically connected with the signal monitoring equipment and the signal control and guard equipment; the signal monitoring equipment is used for acquiring satellite radio frequency signals; the signal control and guard equipment comprises demodulation and decoding hot backup equipment and demodulation and decoding equipment;
the signal monitoring equipment and the signal control and guard equipment are in communication connection with a first switching network and a second switching network respectively;
the intelligent broadband satellite signal identification system monitors and analyzes the broadband satellite signals according to the system working mode; the system working mode comprises a full-band automatic monitoring mode, a whole network monitoring mode, a group road control mode and an intelligent model training mode.
2. The intelligent broadband satellite signal identification system according to claim 1, wherein the intelligent broadband satellite signal identification system adopts a B/S architecture.
3. The intelligent broadband satellite signal identification system according to claim 1, wherein the signal monitoring device comprises a two-channel radio frequency acquisition card, an SCPC/DVB TDMA demodulation processing card, a high performance server and a GPU acceleration card;
the dual-channel radio frequency acquisition card is used for receiving an L frequency range signal and is in communication connection with a DDC high-speed special network and a PCIe4.0 bus.
4. A broadband satellite signal intelligent identification system as claimed in claim 3 wherein said pci e4.0 bus is further communicatively connected to DVB/TDMA group SCPC unit, a number of GPU acceleration cards, CPU and memory unit;
the DVB/TDMA group SCPC unit is also connected with a gigabit network, and meanwhile, the DVB/TDMA group SCPC unit can also receive L-band signals.
5. The system according to claim 1, wherein the full-band automatic monitoring mode performs automatic analysis on the channel parameters of the full band, and comprises:
the signal monitoring equipment carries out digital acquisition on the input broadband satellite signal to generate signal data;
performing FFT (fast Fourier transform) and DDC (direct digital control) processing on the signal data by an FPGA (field programmable gate array) to form a broadband digital frequency spectrum and a plurality of paths of DDC data;
collecting channel data in real time, and identifying signals needing important monitoring;
and automatically monitoring and analyzing the signals needing to be monitored in a key way through the monitoring equipment, acquiring corresponding signal characteristic parameters, and storing the signal characteristic parameters.
6. The system according to claim 1, wherein the whole network monitoring mode is used for obtaining VSAT network scale and implementing whole network control, and comprises:
acquiring network control channel parameters of a networked control channel satellite communication network through full-band monitoring;
monitoring a network control channel in real time, and acquiring the number of stations, network parameters and station working parameters of a VSAT network;
applying for sufficient monitoring channels according to the number of the VSAT network stations; dynamically sending VSAT network parameters and platform working parameters to a monitoring channel to realize the whole-network platform control of the VSAT network;
and the signal data generated by the station control and guard of the whole website is sent to the back-end information processing system through the data transmission service.
7. The system according to claim 1, wherein the group channel monitoring mode performs monitoring on the group channel signals according to the task requirement, and comprises:
acquiring the number of signal carriers of each group by monitoring the full frequency band;
applying for group path control equipment according to the number of the group path carriers;
performing real-time supervision on the group channel signals through group channel supervision equipment, and performing demodulation and decoding on the group channel signals through a group channel demodulation and decoding channel to obtain communication information;
collecting communication information and sending data to a back-end information processing system;
continuously controlling and guarding each group of channel signals, and dynamically adjusting resources according to carrier wave change.
8. The system according to claim 1, wherein the intelligent model training mode is used for training a monitoring and supervising model, and comprises:
carrying out characteristic marking on data obtained by signal monitoring to obtain target data; the target data is obtained by manual marking or machine marking, and the type of the characteristic marking comprises signal video data, analog signal data or digital signal data;
constructing an intelligent model; the intelligent model comprises one or more of an error inverse feedback neural network, an RBF neural network and a deep convolutional neural network;
and training the intelligent model through target data to obtain a monitoring and supervising model, and applying the monitoring and supervising model to signal monitoring.
9. A system for intelligent identification of broadband satellite signals according to claim 1, wherein the relay switching device is specifically an active multi-path power divider or a switching matrix.
10. A system for intelligent identification of broadband satellite signals according to claim 1, wherein said demodulation and decoding device is a signal control and guard device equipped with SCPC/DVB TDMA demodulation processing card.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115459835A (en) * | 2022-11-10 | 2022-12-09 | 环球数科集团有限公司 | Broadband satellite signal intelligent identification system |
| CN116346197A (en) * | 2023-02-28 | 2023-06-27 | 北京扬铭科技发展有限责任公司 | UHF frequency band specific satellite signal analysis equipment and analysis method |
| CN116502071A (en) * | 2023-06-26 | 2023-07-28 | 武汉能钠智能装备技术股份有限公司 | Key signal detection system and method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102010023352B3 (en) * | 2010-06-10 | 2011-11-10 | Deutsches Zentrum für Luft- und Raumfahrt e.V. | Method for transferring data in e.g. digital video broadcasting-return channel via satellite services, involves repeating identifying, selecting, reconstructing and removing steps if data of users are decoded in successful manner |
| CN102545994A (en) * | 2012-02-13 | 2012-07-04 | 北京波尔通导科技有限公司 | Digital video broadcasting (DVB)-carrier-based satellite identification circuit and method |
| CN108540202A (en) * | 2018-03-15 | 2018-09-14 | 西安电子科技大学 | A kind of satellite communication signals Modulation Mode Recognition method, satellite communication system |
| CN109657604A (en) * | 2018-12-17 | 2019-04-19 | 中国人民解放军战略支援部队信息工程大学 | Satellite width phase signals identification demodulation method and device based on Recognition with Recurrent Neural Network |
| CN209184710U (en) * | 2019-03-11 | 2019-07-30 | 成都迅翼卫通科技有限公司 | A kind of combined type satellite-signal identifying system |
| CN111190201A (en) * | 2020-01-08 | 2020-05-22 | 国电大渡河流域水电开发有限公司 | RFID-based Beidou high-precision monitoring device and signal transmission method thereof |
-
2021
- 2021-09-07 CN CN202111041132.5A patent/CN113485212B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102010023352B3 (en) * | 2010-06-10 | 2011-11-10 | Deutsches Zentrum für Luft- und Raumfahrt e.V. | Method for transferring data in e.g. digital video broadcasting-return channel via satellite services, involves repeating identifying, selecting, reconstructing and removing steps if data of users are decoded in successful manner |
| CN102545994A (en) * | 2012-02-13 | 2012-07-04 | 北京波尔通导科技有限公司 | Digital video broadcasting (DVB)-carrier-based satellite identification circuit and method |
| CN108540202A (en) * | 2018-03-15 | 2018-09-14 | 西安电子科技大学 | A kind of satellite communication signals Modulation Mode Recognition method, satellite communication system |
| CN109657604A (en) * | 2018-12-17 | 2019-04-19 | 中国人民解放军战略支援部队信息工程大学 | Satellite width phase signals identification demodulation method and device based on Recognition with Recurrent Neural Network |
| CN209184710U (en) * | 2019-03-11 | 2019-07-30 | 成都迅翼卫通科技有限公司 | A kind of combined type satellite-signal identifying system |
| CN111190201A (en) * | 2020-01-08 | 2020-05-22 | 国电大渡河流域水电开发有限公司 | RFID-based Beidou high-precision monitoring device and signal transmission method thereof |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115459835A (en) * | 2022-11-10 | 2022-12-09 | 环球数科集团有限公司 | Broadband satellite signal intelligent identification system |
| CN116346197A (en) * | 2023-02-28 | 2023-06-27 | 北京扬铭科技发展有限责任公司 | UHF frequency band specific satellite signal analysis equipment and analysis method |
| CN116346197B (en) * | 2023-02-28 | 2024-03-19 | 北京扬铭科技发展有限责任公司 | UHF frequency band specific satellite signal analysis equipment and analysis method |
| CN116502071A (en) * | 2023-06-26 | 2023-07-28 | 武汉能钠智能装备技术股份有限公司 | Key signal detection system and method |
| CN116502071B (en) * | 2023-06-26 | 2023-11-17 | 武汉能钠智能装备技术股份有限公司 | Key signal detection system and method |
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