Multi-standard satellite mobile communication uplink signal real-time detection and identification method and system

Abstract

The invention provides a multi-standard satellite mobile communication uplink signal real-time detection and identification method and system. The method is applied to the technical field of satellite mobile communication, and comprises the following steps: carrying out broadband sampling on an obtained receiving signal to obtain sampling data, carrying out signal carrier detection on the sampling data, judging whether the sampling data contains a target signal or not, and if so, entering the next step; carrying out channelization processing on the sampling data to obtain narrowband signals in narrowband channels corresponding to different satellite systems; and performing TDMA signal detection on the narrowband signal to obtain a signal specification, wherein the TDMA signal detection comprises energy detection, correlation detection and signal identification. The method has the advantages that the capability of accurately recognizing uplink signals of various satellite mobile communication terminals is remarkably improved, and the detection and recognition accuracy reaches 99%; the hardware cost is not increased much, and the method has a low-cost application prospect.

Description

Technical Field

[0001] This disclosure relates to the field of satellite mobile communication technology, and in particular to a method and system for real-time detection and identification of uplink signals in multi-mode satellite mobile communication. Background Technology

[0002] Currently, the mainstream technologies and products for radio security monitoring of satellite mobile communication user terminals can be broadly divided into two categories. One category consists of third-party professional monitoring equipment, targeting specific satellite systems (such as Inmarsat 4, Duraya, and Iridium). The main characteristic of this approach is the use of network control signaling-guided satellite uplink and downlink signal reception and security monitoring. The other category is a general electromagnetic spectrum monitoring technology, which utilizes spectrum analysis methods to monitor the L-band, discover and identify target spectrum characteristics, thereby confirming the communication behavior of the user terminal. Both of these solutions are widely used in practice, but both have significant shortcomings or deficiencies in actual deployment.

[0003] (1) Signal monitoring technology solution based on network control signaling This scheme performs signal reception processing based on the corresponding satellite user terminal link establishment / communication process principle. For example, the user terminal link establishment / communication process of the BGAN communication system is as follows: a. After the user powers on the device, the mobile terminal receives the initial broadcast information of the global beam, including UTC time, regional beam carrier frequency, etc. b. The mobile terminal then switches to receiving the regional beam signal, obtains the uplink registration carrier frequency, completes registration, location reporting and security mode authentication, and enters standby mode. c. When a user makes a call or is called, the user-dedicated signaling allocates the communication frequency of the narrow beam and allocates the uplink communication frequency through the user-dedicated signaling of the narrow beam. In the same carrier frequency, user data is distinguished by a temporary ID number, thereby completing user communication. d. After ending communication, return to the regional beam and re-enter standby mode.

[0004] The design principles of third-party professional monitoring equipment, considering the communication characteristics of this system, are as follows: 1) Receive global announcement signals, and obtain satellite announcement information, including spot beam area division and frequency allocation for network control signaling in the current spot beam area, through demodulation and information parsing; 2) Following the guidance of satellite announcements, receive, process, and parse the current area network control signaling signals; 3) Perform technical processing and analysis on the uplink and downlink service signals of the user terminal beam according to the current regional network control signaling instructions; 4) When an uplink signal from a user within the effective area is detected, the user can be subject to focused monitoring to obtain their identity information, location information, and corresponding communication information.

[0005] The advantage of this type of solution is that it provides relatively complete monitoring of satellite mobile communications. It can obtain the communication status and network control allocation information of all users in the current satellite beam (generally within a range of 600-800 kilometers). Guided by network control signaling, it can monitor the communication of target user terminals in key areas of interest (the uplink signal monitoring range is generally within 10-30 kilometers, and the size of the monitoring range depends on the deployment).

[0006] However, the biggest problem in practical applications is the high cost and demanding deployment requirements. Firstly, the cost of specialized equipment is relatively high. Compared to conventional electromagnetic spectrum monitoring, it requires receiving and processing satellite downlink signals, necessitating specialized satellite antennas and a larger signal reception and processing scale, thus significantly increasing costs (the cost of such specialized equipment is 5-10 times that of conventional radio surveillance equipment). Secondly, the available satellite communication systems are limited. To achieve monitoring of various satellite mobile communications, the required equipment quantity is much greater (3-4 times the amount). Furthermore, for large-scale monitoring, a multi-device network monitoring scheme needs to be considered, but the cost of large-scale network deployment will be prohibitive. Due to the need to receive satellite downlink signals, specialized operations such as antenna alignment with satellites are required. Deployment in urban environments also needs to consider factors such as signal obstruction or electromagnetic interference, demanding a high level of expertise.

[0007] (2) Conventional electromagnetic spectrum monitoring solutions Analysis of various mainstream satellite mobile communication user links reveals that they all use the L-band. By conducting radio monitoring of L-band signals, spectrum analysis technology can be used to discover the signals of various types of satellite mobile communication terminals in use within the area of ​​interest.

[0008] The advantage of this type of solution is its low cost, but its monitoring effect has significant drawbacks: First, its signal recognition capability is poor. Relying solely on spectrum analysis can only detect the presence or absence of a signal; using signal spectrum characteristics can only achieve partial recognition, and in urban environments, there are a large number of interference signals, which can easily lead to a high false alarm rate, with the recognition accuracy typically below 80-90%. Second, its signal cognition depth is insufficient. Using only spectrum characteristics and parameter estimation, it can identify parameters such as the modulation method and modulation rate of the signal, but it is not enough to support the analysis of in-depth cognitive information such as the user's current communication status and target location.

[0009] Both of the above solutions have their advantages and disadvantages, but a good solution should be based on the spectrum monitoring technology solution and increase the single-side processing capability of uplink signals of various satellite terminals. It should be an effective means to improve signal identification and cognitive processing capabilities while maintaining a low cost, and have strong practical deployment and application benefits. Summary of the Invention

[0010] This disclosure provides a method and system for real-time detection and identification of uplink signals in multi-mode satellite mobile communication, which solves the problem of supporting the analysis of in-depth cognitive information such as the user's current communication status and target location while ensuring cost.

[0011] According to a first aspect of this disclosure, a method for real-time detection and identification of uplink signals in multi-mode satellite mobile communication is provided. The method includes: The received signal is broadband sampled to obtain sampled data. The sampled data is then subjected to carrier detection to determine whether it contains a valid signal. If it does, proceed to the next step. The sampled data is channelized to obtain narrowband signals in the narrowband channels corresponding to different satellite systems; The narrowband signal is subjected to TDMA signal detection to obtain the signal specifications. The TDMA signal detection includes energy detection and correlation detection.

[0012] Furthermore, carrier detection specifically includes the following steps: The sampled data is resampled and subjected to spectral analysis to obtain the analysis results; The analysis results are subjected to carrier detection and signal parameter measurement to obtain signal parameters, which include signal frequency, bandwidth characteristic parameters, signal level, signal-to-noise ratio, and time characteristics.

[0013] Furthermore, the spectrum analysis includes a coarse analysis; the coarse analysis includes the following steps: Take the sampled data after 256 resampling points as the first data to be processed; The first data to be processed is subjected to a Hamming window and then a 256-point complex FFT analysis to obtain the first amplitude spectrum. The last 128 points of the first data to be processed are retained, and the last 128 points of the first data to be processed are taken from the resampled data to form the second data to be processed. Determine if the number of times the 256-point complex FFT analysis is performed is equal to 15. If not, treat the second data to be processed as the first data to be processed and proceed to the previous step. If yes, sum the first amplitude spectrum to obtain the total amplitude spectrum of 256 points and proceed to the next step. Divide the total amplitude spectrum by 256*15 to obtain the average amplitude spectrum, and calculate 10*log(average amplitude spectrum) to obtain the logarithmic amplitude.

[0014] Furthermore, the spectrum analysis includes fine analysis; the fine analysis specifically includes the following steps: Based on the beginning of each time slot, 8192*14 sampling points of the resampled data are extracted and divided into 14 segments, each segment having 8192 resampled points. Each segment of wide-sampled data is then windowed with a Hamming window and subjected to 8192-point complex FFT analysis to obtain the amplitude spectrum. Summing the 14 amplitude spectra correspondingly yields the sum vector; Divide the sum vector by 8192*14 to obtain the average sum vector, and calculate 10*log(average sum vector) to obtain the logarithmic sum vector.

[0015] Furthermore, the time characteristics include the end time and the start time of the signal, and the calculation of the end time and the start time includes the following steps: Based on the analysis results, narrowband signals were selected. The sliding energy of the narrowband signal, n=0,1,...,N-1. This can be expressed by the following formula: m=0,1,...,N-1-M Where N and M are both positive integers; using the Haar wavelet basis, for Perform wavelet transforms at multiple scales to obtain , express In scale The wavelet transform of the following, j=1,2,...,J, where J is a positive integer; Calculate the multi-scale wavelet product based on sliding energy. :

[0016] in, ;make If L is odd, and The local minimum point is , The local maximum point is Then the start time of the signal is The signal ends at time If L is even, and The two local maxima with the largest amplitudes are respectively and ( < Then the start time of the signal is The signal ends at time .

[0017] Furthermore, the energy detection process includes: The narrowband signal is passed through a preset window, which includes a high threshold and a low threshold. Determine whether the start time of the narrowband signal is higher than the high threshold or lower than the low threshold. If so, filter out the narrowband signal and determine that there is no TDMA signal. If not, determine that the narrowband signal has a TDMA signal and perform relevant detection on the TDMA signal.

[0018] Furthermore, the relevant detection process includes: The TDMA signal is subjected to correlation detection in order to obtain the signal specification corresponding to the TDMA signal by traversing the constructed prior knowledge base; The prior knowledge base includes preamble unique code sequences, signal modulation types, modulation rates, frame periods, and time slot allocation specifications for various satellite mobile communication signal specifications.

[0019] According to a second aspect of this disclosure, a real-time detection and identification system for multi-standard satellite mobile communication uplink signals is provided, comprising the following modules: The signal determination module is used to perform broadband sampling on the acquired received signal to obtain sampled data, perform carrier detection on the sampled data, and determine whether the sampled data contains a valid signal. If it does, proceed to the next step. A channelization module, connected to the signal determination module, is used to perform channelization processing on the sampled data to obtain narrowband signals in the narrowband channels corresponding to different satellite systems. The detection module, connected to the channelization module, is used to perform TDMA signal detection on the narrowband signal to obtain the signal specifications. The TDMA signal detection includes energy detection and correlation detection.

[0020] The beneficial effects of this disclosure are: (1) This disclosure comprehensively utilizes techniques such as spectrum gradation smoothing, threshold adaptation, and robust detection to achieve carrier detection and signal parameter measurement for signals with various modulation schemes (DPSK, pi / 4QPSK, OQPSK, GMSK, 16QAM, 16APSK, etc.), various symbol rates (8.4KBD~240KBD), and various burst frame lengths (5ms, 20ms, 80ms, 90ms, etc.); it constructs a priori knowledge base of satellite mobile communication uplink signal specifications through extensive and in-depth signal analysis, and achieves accurate signal identification and processing by utilizing relevant detection and energy detection; this disclosure outputs the detected and identified signal data in real time, which can meet the needs of subsequent signal processing. (2) This disclosure significantly improves the ability to accurately identify uplink signals of various satellite mobile communication terminals, with an accuracy rate of 99%; (3) Compared with conventional electromagnetic spectrum monitoring schemes, the hardware platform of this disclosure is basically the same, but the automated processing capability and signal specification depth of uplink signal detection and identification capability of various satellite mobile communication terminals are greatly improved; and the convenience of deployment, installation and operation is better than various professional monitoring schemes; the hardware cost increases by little and has low-cost application prospects.

[0021] It should be understood that the description in the Summary of the Invention section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0022] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein: Figure 1 A flowchart is shown below illustrating a method for real-time detection and identification of uplink signals in multi-mode satellite mobile communication provided in an embodiment of this disclosure. Figure 2 A schematic diagram of channelization processing provided in an embodiment of this disclosure is shown; Figure 3 A schematic diagram of the hardware platform provided in an embodiment of this disclosure is shown; Figure 4 This diagram illustrates a framework of a multi-standard satellite mobile communication uplink signal real-time detection and identification system provided in an embodiment of this disclosure. Figure 5 A block diagram of an electronic device provided according to an embodiment of the present disclosure is shown. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0024] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0025] First, let me explain the following: Signal parameters describe the characteristics of the signal itself and directly determine the signal's information carrying capacity, transmission quality, and anti-interference performance, such as frequency, phase, and bandwidth.

[0026] A communication system is a framework of rules for organizing signal transmission and enabling information exchange. It is defined to achieve reliable information transmission and encompasses signal transmission methods, multiplexing / multiple access technologies, synchronization mechanisms, etc. It determines the capacity and applicable scenarios of a communication system. Its essence is how to organize signal parameters to achieve communication objectives.

[0027] Signal specifications are based on communication systems and standardize signal parameters, transforming them into executable and verifiable parameter standards. A communication system contains many signal types, each with specific specifications, known as signal specifications, such as modulation type, modulation rate, frame format, frame period, time slot allocation specifications, preamble unique code sequence, and carried service information. Different signal specifications are identified through signal detection, and combinations of different signal specifications represent different communication systems.

[0028] For example, to send a voice message to a friend using a mobile phone, the voice message must first be converted according to the signal parameter specifications, such as converting voice to a digital signal and adjusting the signal frequency to 1750MHz. To transmit the converted digital signal via 4G communication, it needs to be transmitted according to pre-agreed rules, such as using orthogonal frequency division multiplexing (OFDM) and AMR voice coding. Transmitting signals via 4G also requires adherence to unified signal specification standards, such as an uplink frequency between 1710 and 1785MHz and a fixed OFDM subcarrier spacing of 15kHz. With these settings, the voice message is successfully transmitted.

[0029] It should be noted that in a single communication process, multiple types of signals work together to achieve the entire process from service initiation to completion. Different signals perform different functions, such as establishing connections, requesting resources, and transmitting service data. All types of signals are detected to determine the type of communication terminal and the content of the communication.

[0030] This disclosure provides a method for real-time detection and identification of uplink signals in multi-standard satellite mobile communication. (See also...) Figure 1 This includes the following steps: S1. Perform broadband sampling on the acquired received signal to obtain sampled data. Perform carrier detection on the sampled data to determine whether the sampled data contains a valid signal. If it does, proceed to the next step. Satellite mobile communication terminals may receive various signals, such as downlink signals from other satellites. Therefore, the first step is to identify whether the received signals contain uplink signals, i.e., valid signals, before proceeding with further processing.

[0031] The broadband sampling process includes: comprehensively analyzing the uplink signal frequency bands of various satellite mobile communication terminals, focusing on the 1616MHz to 1675MHz part with a bandwidth of 60MHz; the received signal first passes through a bandpass filter, allowing only signals in the aforementioned frequency band to pass through; then it passes through AD sampling, which adopts a zero intermediate frequency (IF) scheme to directly convert the filtered signal into a baseband signal, avoiding IF signal processing, simplifying the hardware structure, and reducing signal distortion; finally, after zero IF processing, two orthogonal signals, I and Q, are output, which are the sampled data.

[0032] The sampled data was filtered out to remove clutter interference, providing a clean raw data source for subsequent calculations and avoiding the impact of clutter on the subsequent analysis process.

[0033] Carrier detection allows us to obtain the physical and transmission characteristics of a signal. The specific process for carrier detection in this disclosure includes: S11. Resample the sampled data and perform spectrum analysis to obtain the analysis results; The sampled data is resampled to select a reasonable sampling rate that meets the requirements of FFT analysis. This disclosure utilizes sliding FFT analysis to achieve spectrum analysis.

[0034] In this disclosure, the sliding FFT analysis process includes coarse analysis and fine analysis. The coarse analysis specifically includes the following steps: S1111, Take the sampled data after 256 resampling points as the first data to be processed (0-255); S1112. After adding a Hamming window to the first data to be processed, perform 256-point complex FFT analysis to obtain the first amplitude spectrum. The last 128 points of the first data to be processed are retained, and the last 128 points of the first data to be processed are taken from the resampled data to form the second data to be processed. The second data to be processed is then subjected to 256-point complex FFT analysis after adding a Hamming window. S1113. Determine whether the number of times the 256-point complex FFT analysis is performed is equal to 15. If not, use the second data to be processed as the first data to be processed and proceed to the previous step. If yes, sum the first amplitude spectrum to obtain the total amplitude spectrum of 256 points and proceed to the next step. S1114. Divide the total amplitude spectrum by 256*15 to obtain the average amplitude spectrum, and calculate 10*log(average amplitude spectrum) to obtain the logarithmic amplitude.

[0035] The detailed analysis includes the following steps: S1121. Based on the beginning of each time slot, extract 8192*14 sampling points of the resampled data and divide them into 14 segments. Each segment has 8192 resampled points. Add a Hamming window to each segment of wide-sampled data and perform 8192-point complex FFT analysis to obtain the amplitude spectrum. S1122. Summate the 14th amplitude spectrum accordingly to obtain the sum vector; S1123. Divide the sum vector (8192 points) by 8192*14 to obtain the average sum vector, and calculate 10*log(average sum vector) to obtain the logarithmic sum vector.

[0036] The spectral analysis disclosed herein first performs a coarse analysis of 256 points, and then selects 8192 points for a precise analysis to obtain the frequency domain analysis results. The combination of coarse and precise analysis allows the former to quickly identify signal outgoings, while the latter helps to accurately calculate signal parameters in subsequent steps. Thus, through spectral graded smoothing and step-by-step filtering techniques, efficient time-frequency domain analysis and processing of signals can be achieved.

[0037] S12. Carrier detection and signal parameter measurement are performed on the analysis results to obtain signal parameters, which include signal frequency, bandwidth characteristic parameters, signal level, signal-to-noise ratio and time characteristics.

[0038] Based on the sliding FFT analysis and processing, robust detection and iterative correction techniques can be used to effectively calculate signal parameters and obtain calculation results such as signal carrier frequency, signal bandwidth, modulation type, symbol rate, and signal-to-noise ratio.

[0039] For example, based on the analysis results, the frequency and bandwidth characteristic parameters of each signal can be estimated. Simultaneously, the signal level can be estimated by accumulating the energy within the signal bandwidth, and the signal-to-noise ratio (SNR) is calculated by comparing it with the accumulated noise power in non-signal frequency bands. For the extraction of time features, distributed filtering technology is used to extract the frequency range containing the effective signal from the analysis results, obtaining a narrow-band signal. Then, the sliding energy of the narrow-band signal is calculated, and the time features are obtained by decision-making using multi-scale wavelet products.

[0040] Specifically, for narrowband signals For n=0,1,...,N-1, its sliding energy This can be expressed by the following formula: m=0,1,...,N-1-M Where N and M are both positive integers. Using the Haar wavelet basis, for... Perform wavelet transforms at multiple scales to obtain , express In scale The wavelet transform is applied to the following values, j = 1, 2, ..., J, where J is a positive integer. The multi-scale wavelet product is calculated based on the sliding energy. :

[0041] in, .make If L is odd, The local minimum point is , The local maximum point is Then the start time of the signal is The signal ends at time If L is even, The two local maxima with the largest amplitudes are respectively and ( < Then the start time of the signal is The signal ends at time The burst frame slot length and outgoing connection time of the sampled data can be calculated using the calculated start and end times.

[0042] By comparing the signal parameters obtained above with existing communication signal categories, it can be preliminarily determined whether there is a valid signal, such as whether it is a maritime satellite signal.

[0043] In summary, carrier detection enables rapid signal detection and provides the data prerequisites for subsequent specialized signal identification.

[0044] S2. The sampled data is processed by channelization to obtain narrowband signals in the narrowband channels corresponding to different satellite systems.

[0045] Different communication systems have different channelization parameters, so multi-system mobile communication requires setting multiple channelizations for parallel processing.

[0046] See Figure 2 Based on prior knowledge of uplink signals from various satellite mobile communication terminals, the broadband sampling data of the signal is buffered and uniform channelization processing is carried out, i.e., DDC extraction. Specifically, according to the frequency allocation characteristics of different communication systems in the uplink signal frequency band, several narrowband channels are divided, and the sampling data is matched according to the division of narrowband channels to obtain the narrowband signal corresponding to the narrowband channel.

[0047] Taking the uplink signal of the BGAN system as an example, the nominal bandwidth of each transponder on the Inmarsat-4 satellite is designed to be 200kHz (i.e., sub-band bandwidth). If the entire L-band range of the BGAN system is fully utilized, 170 sub-band channels can be allocated within the total bandwidth of 34MHz. In the above case, the center frequency spacing of each sub-band channel is in 200kHz increments, and the frequency offset compensation of the sub-band channel center frequency (referring to the difference between the center frequency and the lowest frequency of the sub-band channel) is 100kHz.

[0048] S3. The narrowband signal is subjected to TDMA signal detection to obtain the signal specification. The TDMA signal detection includes energy detection and correlation detection.

[0049] By detecting the carrier wave in S1, the communication system is initially determined. For example, it is confirmed that the signal is sent by Hai Satellite. After channelization, the specific communication system is identified. It is still necessary to confirm which user sent which signal. This disclosure identifies various signal specifications within a specific communication system by detecting TDMA signals.

[0050] Various satellite mobile communication terminals typically use Time Division Multiple Access (TDMA) to access uplink signals. Sampling data of the uplink signal (1616-1675MHz) is processed using uniform channelization based on the uplink signal frequency allocation patterns of systems such as BGAN, GSPS, Iridium, and Duraya to obtain different narrowband signals. These narrowband signals are all in TDMA mode; that is, energy detection and correlation detection are performed on various TDMA signals to identify the signal type and specifications.

[0051] Energy detection is used to identify the presence or absence of a signal and extract burst frames (different burst frames may be different for different users); different burst frames have multiple signal specifications, which can be distinguished by the unique code sequence of the signal preamble; correlation detection mainly involves performing correlation detection between the unique code sequence of the signal preamble and known sequences in the signal knowledge base, thereby identifying the signal specification and type.

[0052] Conventional burst detection methods, such as energy detection, are significantly affected by signal-to-noise ratio and signal accumulation length. When burst duration is short, intervals between bursts are short, or burst energy is low, the detection results are greatly affected, making energy detection alone ineffective. Since different types and specifications of signals from various satellite mobile communication terminals have different preamble unique code sequences, correlation detection of the unique codes in the TDMA signal preamble unique code sequence not only provides good detection performance but also synchronizes frames to symbols. Therefore, this disclosure employs a method combining energy detection and correlation detection, as detailed below: (1) Energy detection can utilize the time slot characteristics or frame period characteristics of various TDMA signals to predict the start time of the next signal, and use a lower detection threshold within the prediction window to improve detection performance through a dual-threshold detection method; wherein, the start time of the signal missed by the high threshold can be detected by the low threshold, and false alarms that pass through the low threshold will also be filtered out by the frame period regularity. Through energy detection, the narrowband signal is passed through a preset window, which includes a high threshold and a low threshold; it is determined whether the start time of the narrowband signal is higher than the high threshold and lower than the low threshold. If so, the narrowband signal is filtered out and it is determined that there is no TDMA signal; if not, it is determined that the narrowband signal has a TDMA signal, and the TDMA signal is subjected to correlation detection.

[0053] (2) Correlation detection requires comparing the TDMA signal specifications with known signal specifications when the TDMA signal specifications are known. Specifically, based on the analysis of uplink signal specifications of various satellite mobile communication terminals, prior knowledge of various signal specifications is obtained, such as preamble unique code sequence, signal modulation type, modulation rate, frame period, time slot allocation specifications, etc., and a prior knowledge base of various satellite mobile communication signal specifications is constructed. Correlation detection is then performed on the TDMA signal to traverse the prior knowledge base and obtain the signal specifications corresponding to the TDMA signal.

[0054] In summary, by utilizing spectrum analysis techniques, various signal measurement parameters such as carrier frequency, modulation type, and modulation bandwidth can be obtained. By combining energy detection and correlation detection methods, various satellite mobile communication signal specifications can be accurately detected and identified. Verification shows that the detection and identification accuracy of this method is greater than 99% (carrier-to-noise ratio C / N ≥ 4.0 dB), laying an important foundation for subsequent single-side demodulation, decoding, and information analysis processing.

[0055] See Figure 3 The main processing units disclosed herein are implemented using FPGA system logic programming, such as DDC decimation and resampling. TDMA signal detection is implemented by the FPGA system's built-in ARM processor, but can also be implemented by an external ARM or CPU. Specifically, the entire process is as follows: the FPGA system transmits the pre-processed signal to the ARM STM32 for higher-level identification and demodulation; the FPGA system stores the relevant data in on-chip memory, and the on-chip stored data can also be fed back to the ARM STM32 for analysis; this division of labor allows the FPGA system to leverage its hardware parallelism to process high-speed signals, while the ARM utilizes its software flexibility to complete intelligent algorithms, achieving efficient collaboration.

[0056] Based on the above technical solutions, this disclosure comprehensively utilizes techniques such as spectrum hierarchical smoothing, threshold adaptation, and robust detection to achieve carrier detection and signal parameter measurement for signals with various modulation schemes (DPSK, pi / 4QPSK, OQPSK, GMSK, 16QAM, 16APSK, etc.), various symbol rates (8.4KBD~240KBD), and various burst frame lengths (5ms, 20ms, 80ms, 90ms, etc.). Through extensive and in-depth signal analysis, a priori knowledge base of satellite mobile communication uplink signal specifications is constructed. Using correlation detection and energy detection, accurate signal identification and processing are achieved. This disclosure outputs the detected and identified signal data in real time, which can meet the needs of subsequent signal processing. This disclosure significantly improves the ability to accurately identify uplink signals from various satellite mobile communication terminals, achieving a detection and identification accuracy rate of 99%. Compared to conventional electromagnetic spectrum monitoring solutions, this disclosure has a hardware platform that is basically the same, but it significantly improves the automated processing capability and signal specification depth of uplink signal detection and identification for various satellite mobile communication terminals. In terms of ease of deployment, installation and operation, it is also superior to various specialized monitoring solutions. The increase in hardware cost is not significant, and it has the prospect of low-cost application.

[0057] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0058] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0059] The above is an introduction to the method embodiments. This disclosure also provides a multi-standard satellite mobile communication uplink signal real-time detection and identification system 400, see [link to relevant documentation]. Figure 4 It includes the following modules: The signal judgment module 401 is used to perform broadband sampling on the acquired received signal to obtain sampled data, perform carrier detection on the sampled data, and determine whether the sampled data contains a valid signal. If it does, proceed to the next step. The channelization module 402, connected to the signal determination module 401, is used to perform channelization processing on the sampled data to obtain narrowband signals in the narrowband channels corresponding to different satellite systems. The detection module 403, connected to the channelization module 402, is used to perform TDMA signal detection on the narrowband signal to obtain the signal specifications. The TDMA signal detection includes energy detection and correlation detection.

[0060] Other details can be found in the previous methods section and will not be repeated here.

[0061] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0062] Figure 5 A schematic block diagram of an electronic device 500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0063] Electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in ROM 502 or a computer program loaded into RAM 503 from storage unit 508. RAM 503 can also store various programs and data required for the operation of electronic device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. I / O interface 505 is also connected to bus 504.

[0064] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0065] The computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the multi-mode satellite mobile communication uplink signal real-time detection and identification method. For example, in some embodiments, the multi-mode satellite mobile communication uplink signal real-time detection and identification method can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the multi-mode satellite mobile communication uplink signal real-time detection and identification method described above can be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the multi-mode satellite mobile communication uplink signal real-time detection and identification method by any other suitable means (e.g., by means of firmware).

[0066] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0067] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0068] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0069] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0070] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0071] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0072] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0073] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for real-time detection and identification of uplink signals in multi-standard satellite mobile communication, characterized in that, include: The received signal is broadband sampled to obtain sampled data. The sampled data is then subjected to carrier detection to determine whether it contains a valid signal. If it does, proceed to the next step. The sampled data is channelized to obtain narrowband signals in the narrowband channels corresponding to different satellite systems; The narrowband signal is subjected to TDMA signal detection to obtain the signal specifications. The TDMA signal detection includes energy detection and correlation detection.

2. The method for real-time detection and identification of uplink signals in multi-mode satellite mobile communication according to claim 1, characterized in that, Carrier detection specifically includes the following steps: The sampled data is resampled and subjected to spectral analysis to obtain the analysis results; The analysis results are subjected to carrier detection and signal parameter measurement to obtain signal parameters, which include signal frequency, bandwidth characteristic parameters, signal level, signal-to-noise ratio, and time characteristics.

3. The method for real-time detection and identification of uplink signals in multi-mode satellite mobile communication according to claim 2, characterized in that, The spectral analysis includes a coarse analysis; the coarse analysis includes the following steps: Take the sampled data after 256 resampling points as the first data to be processed; The first data to be processed is subjected to a Hamming window and then a 256-point complex FFT analysis to obtain the first amplitude spectrum. The last 128 points of the first data to be processed are retained, and the last 128 points of the first data to be processed are taken from the resampled data to form the second data to be processed. Determine if the number of times the 256-point complex FFT analysis is performed is equal to 15. If not, treat the second data to be processed as the first data to be processed and proceed to the previous step. If yes, sum the first amplitude spectrum to obtain the total amplitude spectrum of 256 points and proceed to the next step. Divide the total amplitude spectrum by 256*15 to obtain the average amplitude spectrum, and calculate 10*log(average amplitude spectrum) to obtain the logarithmic amplitude.

4. The method for real-time detection and identification of uplink signals in multi-mode satellite mobile communication according to claim 2, characterized in that, The spectral analysis includes fine analysis; the fine analysis specifically includes the following steps: Based on the beginning of each time slot, 8192*14 sampling points of the resampled data are extracted and divided into 14 segments, each segment having 8192 resampled points. Each segment of wide-sampled data is then windowed with a Hamming window and subjected to 8192-point complex FFT analysis to obtain the amplitude spectrum. Summing the 14 amplitude spectra correspondingly yields the sum vector; Divide the sum vector by 8192*14 to obtain the average sum vector, and calculate 10*log(average sum vector) to obtain the logarithmic sum vector.

5. The method for real-time detection and identification of uplink signals in multi-mode satellite mobile communication according to claim 2, characterized in that, The time characteristics include the end time and the start time of the signal. The calculation of the end time and the start time includes the following steps: Based on the analysis results, narrowband signals were selected. The sliding energy of the narrowband signal, n=0,1,...,N-1. This can be expressed by the following formula: ,m=0,1,...,N-1-M, Where N and M are both positive integers; using the Haar wavelet basis, for Perform wavelet transforms at multiple scales to obtain , express In scale The wavelet transform of the following, j=1,2,...,J, where J is a positive integer; Calculate the multi-scale wavelet product based on sliding energy. : ， in, ;make If L is odd, and The local minimum point is , The local maximum point is Then the start time of the signal is The signal ends at time If L is even, and The two local maxima with the largest amplitudes are respectively and ( < Then the start time of the signal is The signal ends at time .

6. The method for real-time detection and identification of uplink signals in multi-mode satellite mobile communication according to claim 1, characterized in that, The energy detection process includes: The narrowband signal is passed through a preset window, which includes a high threshold and a low threshold. If the start time of the narrowband signal is higher than the high threshold or lower than the low threshold, the narrowband signal is filtered out and it is determined that there is no TDMA signal; otherwise, the narrowband signal is determined to have a TDMA signal, and the TDMA signal is subjected to relevant detection.

7. The method for real-time detection and identification of uplink signals in multi-mode satellite mobile communication according to claim 6, characterized in that, The relevant detection process includes: The TDMA signal is subjected to correlation detection in order to obtain the signal specification corresponding to the TDMA signal by traversing the constructed prior knowledge base; The prior knowledge base includes preamble unique code sequences, signal modulation types, modulation rates, frame periods, and time slot allocation specifications for various satellite mobile communication signal specifications.

8. A real-time detection and identification system for multi-standard satellite mobile communication uplink signals, used to implement the method described in any one of claims 1 to 7, characterized in that, Includes the following modules: The signal determination module is used to perform broadband sampling on the acquired received signal to obtain sampled data, perform carrier detection on the sampled data, and determine whether the sampled data contains a valid signal. If it does, proceed to the next step. A channelization module, connected to the signal determination module, is used to perform channelization processing on the sampled data to obtain narrowband signals in the narrowband channels corresponding to different satellite systems. The detection module, connected to the channelization module, is used to perform TDMA signal detection on the narrowband signal to obtain the signal specifications. The TDMA signal detection includes energy detection and correlation detection.

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