Star-ground collaborative anti-jamming system and method based on integrated intelligent perception

Abstract

The application discloses a satellite-ground cooperative anti-interference system based on a comprehensive perception integrated intelligent cognition, which comprises a satellite-borne communication device module and a satellite ground station module. The satellite-borne communication device module comprises a first communication receiving and processing module, an interference detection module, an interference identification module, an interference parameter estimation module, an anti-interference strategy generation module and a first communication transmitting and processing module. The electromagnetic environment signal is received through the first communication receiving and processing module, the results of electromagnetic interference and the signal-to-interference-and-noise ratio are obtained through the interference detection module, the interference type is obtained through the interference identification module, the interference parameter is obtained through the interference parameter estimation module, the anti-interference strategy is obtained through the anti-interference strategy generation module, and the anti-interference strategy is sent through the first communication transmitting and processing module. The satellite ground station module comprises a second communication receiving and processing module, a second communication transmitting and processing module and an anti-interference strategy synchronization module. The anti-interference strategy is synchronized to the anti-interference strategy synchronization module, and the data to be sent is processed through the anti-interference strategy.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of wireless communication networks, and particularly relates to a satellite-ground cooperative anti-interference system and method based on integrated intelligent sensing. BACKGROUND

[0002] Satellite communication anti-interference technology is a key technology for ensuring efficient and reliable information transmission and satellite constellation security. It is related to the stable operation of satellite communication in a complex electromagnetic environment, and is of great significance for resisting intentional or unintentional radio frequency signal interference, preventing information leakage and network paralysis, and is a necessary means to cope with dynamic interference situation.

[0003] Satellite communication is widely used in many fields, but the link is easily disturbed by the complex electromagnetic environment, and the security is challenged. Anti-interference technology is of great significance, and there are many types of existing technologies, such as antennas, spread spectrum, etc. Spread spectrum and frequency hopping are widely used, and there are multi-mode systems that can switch to interference-resistant systems, but at the present stage, the anti-interference system selection depends on ground setting, the parameters are limited, and it cannot be adjusted adaptively on the satellite, lacks autonomous decision-making ability, and is difficult to cope with dynamic interference. SUMMARY

[0004] The application provides a satellite-ground cooperative anti-interference system and method based on integrated intelligent sensing.

[0005] The integrated sensing data frame structure realizes the processing of different tasks by dividing sensing time slots, synchronization time slots and communication time slots, realizes the organic combination of the three functions of sensing, synchronization and communication in the satellite communication system, and provides a framework basis for anti-interference strategies. Through real-time detection and identification of interference and combined with a double-layer decision mechanism, an effective anti-interference strategy can be adaptively generated, which not only improves the anti-interference ability and communication efficiency of the system, but also ensures the stability and reliability of data transmission.

[0006] The first aspect of the application provides a satellite-ground cooperative anti-interference system based on integrated intelligent sensing, comprising:

[0007] The satellite-borne communication device module comprises a first communication receiving processing module, an interference detection module, an interference identification module, an interference parameter estimation module, an anti-interference strategy generation module, a first communication transmitting processing module; is used for receiving electromagnetic environment signals and data transmitted by a satellite ground station through the first communication receiving processing module, obtaining the results of electromagnetic interference and the signal-to-interference-and-noise ratio based on the electromagnetic environment signals through the interference detection module, obtaining the interference type based on the electromagnetic environment signals through the interference identification module, obtaining the interference parameters based on the electromagnetic environment signals through the interference parameter estimation module, obtaining the anti-interference strategy based on the interference type, the signal-to-interference-and-noise ratio and the interference parameters through the anti-interference strategy generation module, transmitting the anti-interference strategy through the first communication transmitting processing module, and transmitting the anti-interference strategy through the first communication transmitting processing module.

[0008] The satellite ground station module comprises a second communication receiving processing module, a second communication transmitting processing module and an anti-interference strategy synchronization module, is used for receiving the anti-interference strategy through the second communication receiving processing module and synchronizing to the anti-interference strategy synchronization module, and transmitting data processed through the anti-interference strategy through the second communication transmitting processing module.

[0009] The second aspect of the present application provides a star-ground collaborative anti-interference method based on the integrated intelligent cognition, which is applied to the star-ground collaborative anti-interference system, the star-ground collaborative anti-interference system comprises a satellite-borne communication device module and a satellite ground station module, and comprises the following steps:

[0010] The preset periodic task comprises, in time sequence, a perception periodic task, a synchronization periodic task and a communication periodic task, and the periodic tasks are executed simultaneously on the satellite-borne communication device and the satellite ground station;

[0011] The perception periodic task: the satellite-borne communication device collects electromagnetic environment signals, and obtains an anti-interference strategy based on the electromagnetic environment signals;

[0012] The synchronization periodic task: the satellite-borne communication device transmits the anti-interference strategy to the satellite ground station, and the satellite ground station updates the anti-interference strategy;

[0013] The communication periodic task: the satellite ground station transmits data information to the satellite-borne communication device by using the anti-interference strategy, and the satellite-borne communication device demodulates to obtain the data information.

[0014] Preferably, the step of obtaining the anti-interference strategy based on the electromagnetic environment signals specifically comprises:

[0015] The signal-to-interference-and-noise ratio and the results of electromagnetic interference are detected based on the electromagnetic environment signals by using the energy method;

[0016] obtaining an interference type and an interference parameter based on the electromagnetic environment signal and the result of the electromagnetic interference;

[0017] obtaining the anti-interference strategy based on the interference type, the signal-to-interference-and-noise ratio and the interference parameter by deep reinforcement learning DQN.

[0018] Preferably, the step of obtaining the signal-to-interference-and-noise ratio and the result of the electromagnetic interference based on the electromagnetic environment signal by the energy detection method specifically comprises:

[0019] presetting a first threshold range and a signal power;

[0020] obtaining an interference power based on the electromagnetic environment signal by the energy detection method, and obtaining a noise power based on the electromagnetic environment signal by acquisition;

[0021] obtaining a signal-to-interference-and-noise ratio based on the signal power, the interference power and the noise power, and calculating an expression as follows:

[0022]

[0023] wherein Ps is the signal power, PJ is the interference power, and PN is the noise power;

[0024] obtaining the result of the electromagnetic interference by comparing the signal-to-interference-and-noise ratio with the first threshold range.

[0025] Preferably, the step of obtaining the interference type and the interference parameter based on the electromagnetic environment signal and the result of the electromagnetic interference specifically comprises:

[0026] if the result of the electromagnetic interference is that there is interference, obtaining the interference type based on the electromagnetic environment signal by an interference identification module of the spaceborne communication device, and obtaining the interference parameter based on the electromagnetic environment signal by an interference parameter estimation module of the spaceborne communication device.

[0027] Preferably, the step of obtaining the anti-interference strategy based on the interference type, the signal-to-interference-and-noise ratio and the interference parameter by the deep reinforcement learning DQN specifically comprises:

[0028] pre-constructing a modulation mode set, a frequency point set, an information rate set, a channel coding code rate set and a transmission power set, wherein the modulation mode set contains BPSK, DSSS and FHSS;

[0029] constructing an anti-interference strategy set by traversing and combining the modulation mode set, the frequency point set, the information rate set, the channel coding code rate set and the transmission power set;

[0030] obtaining the modulation mode based on the signal-to-interference-and-noise ratio and the interference type through a rule-based decision tree;

[0031] obtaining the anti-interference strategy based on the modulation mode, the anti-interference strategy set, the interference type and the interference parameter through the deep reinforcement learning DQN.

[0032] Preferably, the step of obtaining the modulation mode based on the signal-to-interference-and-noise ratio and the interference type through the rule-based decision tree specifically comprises:

[0033] a second threshold range is preset;

[0034] If the interference type is any one of single-tone interference, narrowband interference and comb interference, the modulation mode remains unchanged. If the interference type is any one of wideband noise interference and sweep interference, it is further judged whether the signal-to-interference-and-noise ratio is greater than the upper limit value of the second threshold range. If yes, the modulation mode is updated to BPSK. If the signal-to-interference-and-noise ratio hits the second threshold range, the modulation mode is updated to DSSS. If the signal-to-interference-and-noise ratio is less than the lower limit value of the second threshold range, the modulation mode is updated to FHSS.

[0035] Preferably, the step of obtaining the anti-interference strategy based on the modulation mode, the anti-interference strategy set, the interference type and the interference parameter through the deep reinforcement learning DQN specifically comprises:

[0036] time sequence interference information is constructed based on the interference parameter and the interference type, and a calculation expression is:

[0037]

[0038] In the formula, f j,t is the center frequency point of the interference parameter, B J is the interference signal bandwidth of the interference parameter, A J is the interference amplitude value of the interference parameter, C J is the interference type, P J is the interference power of the interference parameter;

[0039] Different four-dimensional tuples are constructed according to the modulation mode dimension based on the anti-interference strategy set, and a calculation expression is:

[0040]

[0041] In the formula, P is the transmission power value of the anti-interference strategy, f s is the frequency point value of the anti-interference strategy, R is the information rate value of the anti-interference strategy, and η is the channel coding code rate of the anti-interference strategy;

[0042] The signal-to-interference-and-noise ratio reward value, the information rate reward value, and the spectrum utilization reward value are calculated based on the four-dimensional tuple and the timing interference information.

[0043] The adaptive anti-interference strategy is obtained by the deep reinforcement learning DQN based on the maximum Q value of the signal-to-interference-and-noise ratio reward value, the information rate reward value, and the spectrum utilization reward value, and the calculation expression is as follows:

[0044]

[0045] In the formula, reward SINR , reward R , and reward η are respectively the signal-to-interference-and-noise ratio reward value, the information rate reward value, and the spectrum utilization reward value, A t+1 is the anti-interference strategy, and θ and θ' are respectively the policy network weight and the target network weight. The value of the policy network weight is updated to the target network weight by training the policy network.

[0046] Preferably, the step of updating the value of the policy network weight to the target network weight by training the policy network specifically includes the following steps.

[0047] Pre-constructing samples containing states, rewards, actions, and target actions;

[0048] Training the network policy based on the samples to obtain a loss function, so as to realize the updating of the value of the policy network weight to the target network weight, and the calculation expression is as follows:

[0049] .

[0050] Preferably, the steps of calculating the signal-to-interference-and-noise ratio reward value, the information rate reward value, and the spectrum utilization reward value based on the four-dimensional tuple and the timing interference information specifically include the following steps.

[0051] The preset signal power P s , the first information rate R l , the second information rate R m , the third information rate R h , the first information rate result V R,l , the second information rate result V R,m , the third information rate result V R,h , the first spectrum utilization η l , the second spectrum utilization η m , the third spectrum utilization η h , the first spectrum utilization result V η,l , and the second spectrum utilization result V η,m, the third spectrum utilization result V η,h ;

[0052] constructing the signal-to-interference-and-noise ratio reward value reward SINR , the information rate reward value reward R , the spectrum utilization reward value reward η The formula for calculating the reward value reward

[0053]

[0054]

[0055] .

[0056] The present application has the following advantages and positive effects compared with the prior art due to the adoption of the above technical solutions:

[0057] The integrated sensing and communication data frame structure realizes the processing of different tasks by dividing sensing time slots, synchronization time slots and communication time slots, realizes the organic combination of the three functions of sensing, synchronization and communication in the satellite communication system, and provides a framework basis for the anti-interference strategy.

[0058] By real-time detection and identification of interference and combining a double-layer decision mechanism, an effective anti-interference strategy can be adaptively generated, which not only improves the anti-interference capability and communication efficiency of the system, but also ensures the stability and reliability of data transmission. Specifically, real-time detection and identification of interference: the satellite-borne communication device collects electromagnetic signals in the environment, and based on electromagnetic signal processing, obtains a number of elements of the following signals: the result of electromagnetic interference, the type of interference, the signal-to-interference-and-noise ratio and the interference parameters; an anti-interference strategy is adaptively generated based on the elements through a double-layer decision, the first layer decision adopts a rule-based decision tree method, which is based on expert experience, constructs a rule-based anti-interference intelligent decision engine, and automatically makes decisions and operations according to a set of predefined rules, the rule engine can automatically make anti-interference decisions according to input data and expert anti-interference library, output parameter control actions, and also supports subsequent model evolution incremental learning. Through the second layer decision of deep reinforcement learning: for the parameter selection problem after the system changes, a data-based deep reinforcement learning method (DQN) is adopted, the sensing interference situation is taken as the state, the signal-to-interference-and-noise ratio, the information rate and the spectrum utilization rate are taken as the joint optimization target, a parameter strategy model is formed, the selection weights of each multi-dimensional parameter selection action facing State are saved, and finally the parameter decision model of power P, code rate and information rate is obtained through the DQN method. BRIEF DESCRIPTION OF DRAWINGS

[0059] The specific embodiments of the present application will be further described in detail below in conjunction with the accompanying drawings, in which:

[0060] Figure 1 A schematic diagram of a framework of a star-ground collaborative anti-interference system based on the integrated intelligent cognition of common sense according to the present application;

[0061] Figure 2 A task module diagram in a star-ground collaborative anti-interference method based on the integrated intelligent cognition of common sense according to the present application;

[0062] Figure 3 A flow overview diagram in a star-ground collaborative anti-interference method based on the integrated intelligent cognition of common sense according to the present application;

[0063] Figure 4 A schematic diagram of a double-layer anti-interference strategy generation method of reinforced learning multi-dimensional parameter regulation in a star-ground collaborative anti-interference method based on the integrated intelligent cognition of common sense according to the present application;

[0064] Figure 5 A performance comparison diagram of a star-ground collaborative anti-interference method based on the integrated intelligent cognition of common sense according to the present application and a method without an anti-interference strategy; DETAILED DESCRIPTION

[0065] The present application will be further described below in conjunction with the accompanying drawings and specific embodiments. The advantages and features of the present application will be more apparent according to the following description and claims. It should be noted that the accompanying drawings are all greatly simplified and use non-precise ratios, only for the purpose of facilitating and clarifying the purpose of explaining the embodiments of the present application.

[0066] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present application are only used to explain the relative positional relationship, motion condition, etc. between components in a certain specific posture (as shown in the drawings), and if the specific posture changes, the directional indications will also change accordingly.

[0067] Embodiment One

[0068] Referring to Figure 1 The first aspect of the present application provides a star-ground collaborative anti-interference system based on the integrated intelligent cognition of common sense, comprising:

[0069] The satellite-borne communication device module comprises a first communication receiving processing module, an interference detection module, an interference identification module, an interference parameter estimation module, an anti-interference strategy generation module, and a first communication transmitting processing module. The module is used for receiving electromagnetic environment signals and data transmitted by a satellite ground station through the first communication receiving processing module, obtaining the results of electromagnetic interference and the signal-to-interference-and-noise ratio based on the electromagnetic environment signals through the interference detection module, obtaining the interference type based on the electromagnetic environment signals through the interference identification module, obtaining the interference parameters based on the electromagnetic environment signals through the interference parameter estimation module, obtaining the anti-interference strategy based on the interference type, the signal-to-interference-and-noise ratio, and the interference parameters through the anti-interference strategy generation module, transmitting the anti-interference strategy through the first communication transmitting processing module, and transmitting the data processed by the anti-interference strategy through the first communication transmitting processing module.

[0070] The satellite ground station module comprises a second communication receiving processing module, a second communication transmitting processing module, and an anti-interference strategy synchronization module. The module is used for receiving the anti-interference strategy through the second communication receiving processing module and synchronizing the anti-interference strategy to the anti-interference strategy synchronization module, and transmitting the data processed by the anti-interference strategy through the second communication transmitting processing module.

[0071] In the sensing period, the first communication transmitting processing module collects electromagnetic environment signals through the first communication receiving processing module, obtains the results of electromagnetic interference based on the electromagnetic environment signals through the interference detection module, obtains the interference type based on the electromagnetic environment signals through the interference identification module, obtains the interference parameters based on the electromagnetic environment signals through the interference parameter estimation module, and obtains the anti-interference strategy based on the interference type, the signal-to-interference-and-noise ratio, and the interference parameters through the anti-interference strategy generation module. In the synchronization period, the second communication receiving processing module receives the anti-interference strategy and updates the anti-interference strategy synchronization module to the latest value, ensuring that the anti-interference measures of the entire communication link remain consistent, guaranteeing the real-time consistency of the anti-interference strategies of the satellite-borne communication device and the satellite ground station, and ensuring the consistency of the communication mechanism. In the communication period, the second communication transmitting processing module encodes and modulates the data information to be transmitted according to the synchronized anti-interference strategy, and then transmits the data to the first communication receiving processing module through the uplink. After receiving the data, the first communication receiving processing module performs demodulation and decoding operations to obtain the original data information. In the sensing, synchronization, and communication period tasks, the satellite-borne communication device and the satellite ground station ensure cooperative work to ensure the timely transmission and processing of information, and improve the response speed and flexibility of the system.

[0072] Embodiment Two

[0073] Reference Figure 1 , Figure 2 and Figure 3The second aspect of this invention provides a satellite-ground cooperative anti-interference method based on integrated sensing and intelligent cognition, applied to the aforementioned satellite-ground cooperative anti-interference system. The satellite-ground cooperative anti-interference system includes a spaceborne communication equipment module and a satellite ground station module, and includes the following steps:

[0074] The preset periodic tasks are arranged in sequence according to time: sensing periodic tasks, synchronization periodic tasks, and communication periodic tasks. The periodic tasks are executed simultaneously on the onboard communication equipment and the satellite ground station.

[0075] Sensing cycle task: The onboard communication equipment collects electromagnetic environment signals and obtains anti-interference strategies based on the electromagnetic environment signals;

[0076] Synchronization periodic task: The onboard communication equipment transmits anti-interference strategies to the satellite ground station, and the satellite ground station updates the anti-interference strategies;

[0077] Communication cycle task: The satellite ground station uses anti-interference strategies to transmit data information to the onboard communication equipment, which then demodulates and acquires the data information.

[0078] See Figure 1 Preferably, the steps for obtaining anti-interference strategies based on electromagnetic environment signals specifically include:

[0079] The signal-to-interference-plus-noise ratio and electromagnetic interference results are obtained by energy method based on electromagnetic environment signals;

[0080] The interference type and interference parameters are obtained based on the results of electromagnetic environment signals and electromagnetic interference.

[0081] Deep reinforcement learning (DQN) is used to obtain anti-interference strategies based on interference type, signal-to-interference-plus-noise ratio (SNR), and interference parameters.

[0082] During the sensing cycle, the onboard communication equipment generates or updates anti-interference strategies and transmits them to the satellite ground station. During the synchronization cycle, the satellite ground station receives the anti-interference strategies and updates them to synchronize and respond to interference conditions in the current environment, ensuring that the anti-interference measures throughout the communication link remain consistent. This guarantees the real-time consistency of the anti-interference strategies between the onboard communication equipment and the satellite ground station, ensuring the consistency of the communication mechanism. During the communication cycle, the satellite ground station encodes and modulates the data to be transmitted according to the synchronized anti-interference strategy, and then transmits the data to the onboard communication equipment via the uplink. After receiving the data, the onboard communication equipment performs demodulation and decoding operations to obtain the original data information. In the sensing, synchronization, and communication cycle tasks, the onboard communication equipment and the satellite ground station work together to ensure timely information transmission and processing, improving the system's response speed and flexibility. This embodiment does not impose any limitations on the duration of the sensing cycle, synchronization cycle, and communication cycle tasks. Figure 5The anti-interference performance of the application is superior to that without the anti-interference strategy.

[0083] Referring to Figure 1 Preferably, the step of obtaining the results of the electromagnetic interference based on the signal-to-interference-and-noise ratio obtained based on the electromagnetic environment signal and the electromagnetic interference specifically comprises:

[0084] The first threshold range and the signal power are preset;

[0085] The interference power obtained based on the electromagnetic environment signal is detected by the energy method, and the noise power obtained based on the electromagnetic environment signal is detected by collection;

[0086] The signal-to-interference-and-noise ratio is obtained based on the signal power, the interference power and the noise power, and the calculation expression is:

[0087]

[0088] In the formula, Ps is the signal power, PJ is the interference power, and PN is the noise power.

[0089] The results of the electromagnetic interference are obtained by comparing the signal-to-interference-and-noise ratio with the first threshold range.

[0090] The first threshold range quantifies whether there is electromagnetic interference and the interference degree, and is set by performance requirements, experience and related standards, which is not limited in the embodiment. The signal power is used as an input parameter for calculating the signal-to-interference-and-noise ratio, and the interference power obtained based on the electromagnetic environment signal is extracted,

[0091] The noise power is used to evaluate the influence degree on the communication signal. The calculated signal-to-interference-and-noise ratio is compared with the pre-set first threshold range: if the signal-to-interference-and-noise ratio exceeds the threshold range, it indicates that there is electromagnetic interference, and further analysis and corresponding anti-interference measures need to be taken.

[0092] Referring to Figure 1 Preferably, the step of obtaining the interference type and the interference parameter based on the electromagnetic environment signal and the results of the electromagnetic interference specifically comprises:

[0093] If the results of the electromagnetic interference are that there is interference, the interference type is obtained based on the electromagnetic environment signal by the interference identification module of the spaceborne communication device, and the interference parameter is obtained based on the electromagnetic environment signal by the interference parameter estimation module of the spaceborne communication device.

[0094] The interference type and the interference parameter can be accurately obtained by the interference identification module and the interference parameter estimation module of the spaceborne communication device. Identifying the interference type and estimating the interference parameter enable the space-ground collaborative anti-interference system to flexibly cope with various complex electromagnetic scenarios, guarantee the communication stability, and reduce the problems of communication interruption, bit error rate and the like caused by the interference.

[0095] Preferably, the step of obtaining the anti-interference strategy by the deep reinforcement learning DQN based on the interference type, the signal-to-interference-and-noise ratio and the interference parameter specifically comprises:

[0096] A pre-constructed modulation mode set, a frequency point set, an information rate set, a channel coding rate set and a transmission power set are constructed, wherein the modulation mode set comprises BPSK, DSSS and FHSS;

[0097] The modulation mode set, the frequency point set, the information rate set, the channel coding rate set and the transmission power set are combined to construct an anti-interference strategy set;

[0098] The modulation mode is obtained based on the signal-to-interference-and-noise ratio and the interference type through a rule-based decision tree;

[0099] The anti-interference strategy is obtained based on the modulation mode, the anti-interference strategy set, the interference type and the interference parameter through the deep reinforcement learning DQN.

[0100] A pre-constructed modulation mode set is constructed, wherein the modulation mode set comprises BPSK, DSSS and FHSS. These modulation modes have different anti-interference characteristics and are suitable for different electromagnetic environments.

[0101] Meanwhile, a frequency point set, an information rate set, a channel coding rate set and a transmission power set are constructed. The embodiment does not limit the set of the frequency point set, the information rate set, the channel coding rate set and the transmission power set. The frequency point set covers the selectable communication frequency points; the information rate set determines the selectable range of data transmission rate; the channel coding rate set includes different coding rate options for error correction and improving transmission reliability; and the transmission power set specifies the possible value range of signal transmission power. The modulation mode set, the frequency point set, the information rate set, the channel coding rate set and the transmission power set are combined to construct a comprehensive anti-interference strategy set. Each combination represents a possible anti-interference strategy configuration. The first layer decision obtains the modulation mode based on the signal-to-interference-and-noise ratio and the interference type through a rule-based decision tree. The decision tree selects the modulation mode matched therewith according to the preset rule. The second layer decision is continuously learned and trained by the DQN, and selects the strategy capable of maximizing the long-term reward from the anti-interference strategy set as the output according to the input state information such as the modulation mode, the interference type and the interference parameter.

[0102] Referring to Figure 4 , preferably, the step of obtaining the modulation mode based on the signal-to-interference-and-noise ratio and the interference type through the rule-based decision tree specifically comprises:

[0103] A second threshold range is preset;

[0104] The interference type is any one of single-tone interference, narrowband interference, comb interference, and the modulation mode remains unchanged. If the interference type is any one of wideband noise interference and sweep interference, it is further determined whether the signal-to-interference-and-noise ratio is greater than the upper limit value of the second threshold range. If yes, the modulation mode is updated to BPSK. If the signal-to-interference-and-noise ratio hits the second threshold range, the modulation mode is updated to DSSS. If the signal-to-interference-and-noise ratio is less than the lower limit value of the second threshold range, the modulation mode is updated to FHSS.

[0105] The interference type is determined to be one of single-tone interference, narrowband interference, or comb interference. If it is one of the above types, the current modulation mode remains unchanged. If the interference type is one of wideband noise interference or sweep interference, it is further determined whether the signal-to-interference-and-noise ratio is related to the second threshold range. If the signal-to-interference-and-noise ratio is greater than the upper limit value of the second threshold range, the modulation mode is updated to BPSK. The BPSK modulation mode has high spectral efficiency under certain signal-to-noise ratio conditions. If the signal-to-interference-and-noise ratio hits the second threshold range, i.e., the signal-to-interference-and-noise ratio is between the upper limit value and the lower limit value, the modulation mode is updated to DSSS. The DSSS modulation mode uses pseudo-random coding for spread spectrum communication and has strong anti-interference ability under medium signal-to-noise ratio conditions, which is suitable for coping with such conditions with certain interference but not excellent channel conditions. If the signal-to-interference-and-noise ratio is less than the lower limit value of the second threshold range, it indicates that the current channel is disturbed greatly and the signal-to-noise ratio is low. At this time, the modulation mode is updated to FHSS. The FHSS modulation mode avoids interference by fast frequency hopping, which can effectively improve the reliability of communication in a low signal-to-noise ratio environment and reduce the impact of interference on communication.

[0106] Referring to Figure 4 Preferably, the step of obtaining the anti-interference strategy based on the modulation mode, the anti-interference strategy set, the interference type, and the interference parameter by deep reinforcement learning DQN specifically includes:

[0107] The time-series interference information is constructed based on the interference parameter and the interference type, and the calculation expression is:

[0108]

[0109] In the formula, f j,t is the center frequency of the interference parameter, B J is the interference signal bandwidth of the interference parameter, A J is the interference amplitude of the interference parameter, C J is the interference type, P J is the interference power of the interference parameter.

[0110] Different four-dimensional tuples are constructed based on the anti-interference strategy set according to the modulation mode dimension, and the calculation expression is:

[0111]

[0112] where P is the transmit power value of the anti-jamming strategy, f s is the frequency value of the anti-jamming strategy, R is the information rate value of the anti-jamming strategy, and η is the channel coding rate of the anti-jamming strategy.

[0113] The signal-to-interference-and-noise ratio reward value, the information rate reward value, and the spectrum utilization reward value are calculated based on the four-dimensional tuple and the timing interference information.

[0114] The adapted anti-jamming strategy is obtained by the deep reinforcement learning DQN based on the signal-to-interference-and-noise ratio reward value, the information rate reward value, and the spectrum utilization reward value, and the calculation expression is:

[0115]

[0116] where reward SINR , reward R , and reward η are the signal-to-interference-and-noise ratio reward value, the information rate reward value, and the spectrum utilization reward value, respectively, A t+1 is the anti-jamming strategy, θ and θ' are the policy network weight and the target network weight, respectively, and the value of the policy network weight is updated to the target network weight through training the policy network.

[0117] The signal-to-interference-and-noise ratio reward value can reflect the effect of the strategy on improving the signal-to-interference-and-noise ratio under different interference conditions, and is one of the quantitative strategy values. The information rate reward value quantifies the quantitative performance of the information transmission rate in the anti-jamming strategy. The spectrum utilization reward value quantifies the utilization efficiency of the spectrum resource by the strategy, prompting the system to select a strategy that can effectively resist interference and reasonably utilize the spectrum. Quantifying the above values helps to achieve a balance between resource utilization and performance in the communication system. The policy network weight is updated based on multiple reward values using deep reinforcement learning DQN, enabling the system to dynamically adapt to changing interference environments and communication demands. As the training progresses, the policy network weight is continuously updated to the target network weight, enabling the system to adaptively find the optimal anti-jamming strategy. This dynamic learning and optimization mechanism ensures that the communication system can always select the most adaptive anti-jamming strategy under different electromagnetic environments, different interference types, and parameter changes.

[0118] Referring to Figure 4 , preferably, the step of updating the value of the policy network weight to the target network weight through training the policy network specifically includes:

[0119] pre-constructing samples containing states, rewards, actions, and target actions;

[0120] Training the network strategy based on the samples to obtain a loss function, which realizes the updating of the value of the policy network weight to the target network weight, and the calculation expression is:

[0121] .

[0122] By pre-building samples containing state, reward, action, target action, the deep reinforcement learning algorithm is provided with rich data support. These samples cover various states of the communication system under different interference scenarios and the corresponding anti-interference actions and rewards obtained, enabling the policy network to fully learn the characteristics and patterns of the optimal policy under different conditions, thereby achieving more accurate policy decisions. Training the network policy based on samples obtains a loss function to update the weights, which helps to accelerate the convergence process of the policy network. By calculating the loss function and backpropagating the error, the parameters of the policy network can be accurately adjusted, enabling the network to learn effective anti-interference strategies. At the same time, this method based on target network weight update can increase the stability of training, avoid errors in the weight update process, and ensure that the system gradually tends to the optimal policy selection during training. As training progresses, the policy network weights are constantly updated to the target network weights, enabling the system to dynamically adapt to changing electromagnetic environments and interference conditions. At different time steps or scenarios, the system can adjust the policy in a timely manner based on new sample data to ensure that it always maintains good anti-interference performance in complex and variable communication environments, effectively improving the reliability and stability of the communication system.

[0123] Referring to Figure 4 , preferably, the steps of calculating the signal-to-interference-and-noise ratio reward value, the information rate reward value, and the spectrum utilization reward value based on the four-dimensional tuple and the time-series interference information specifically include:

[0124] preset signal power P s , first information rate R l , second information rate R m , third information rate R h , first information rate result V R,l , second information rate result V R,m , third information rate result V R,h , first spectrum utilization η l , second spectrum utilization η m , third spectrum utilization η h , first spectrum utilization result V η,l , second spectrum utilization result V η,m , third spectrum utilization result V η,h ;

[0125] The calculation formulas of the signal-to-interference-and-noise ratio reward value reward SINR , the information rate reward value reward R , and the spectrum utilization reward value reward η are constructed as follows:

[0126]

[0127]

[0128] .

[0129] first information rate R l second information rate R m third information rate R h first information rate result V R,l second information rate result V R,m third information rate result V R,h first spectrum utilization η l second spectrum utilization η m third spectrum utilization η h first spectrum utilization result V η,l second spectrum utilization result V η,m third spectrum utilization result V η,h The above variables are not limited in the embodiment.

[0130] In the description of the present application, it should be noted that the terms "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship commonly used when the product of the present application is used, and are only for the convenience of describing the present application and simplifying the description, and therefore cannot be understood as indicating or implying that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first", "second" and the like are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0131] It should also be noted that unless otherwise explicitly specified and limited, the terms "set", "connected" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be directly connected, or indirectly connected through an intermediate medium, or it can be connected inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0132] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific execution of the above-described system and device can be referred to the corresponding process in the foregoing method embodiment.

[0133] The embodiments of the present application are explained in detail above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments. Even if various changes are made to the present application, if the changes fall within the scope of the claims of the present application and equivalents thereof, they are still within the protective scope of the present application.

Claims

1. A star-ground collaborative anti-interference system based on inter-sensory integrated intelligent cognition, characterized in that, Comprising: The satellite communication device module comprises a first communication receiving processing module, an interference detection module, an interference identification module, an interference parameter estimation module, an anti-interference strategy generation module, a first communication transmission processing module; for receiving electromagnetic environment signals and data transmitted by satellite ground station through the first communication receiving processing module, obtaining the results of electromagnetic interference and signal-to-interference ratio based on the electromagnetic environment signals through the interference detection module, obtaining the interference type based on the electromagnetic environment signals through the interference identification module, obtaining the interference parameters based on the electromagnetic environment signals through the interference parameter estimation module, obtaining the anti-interference strategy based on the interference type, the signal-to-interference ratio and the interference parameters through the anti-interference strategy generation module, transmitting the anti-interference strategy through the first communication transmission processing module, transmitting the anti-interference strategy through the first communication transmission processing module; The step of obtaining the anti-interference strategy based on the interference type, the signal-to-interference ratio and the interference parameters through the anti-interference strategy generation module further comprises: A second threshold range is preset; If the interference type is any one of single-tone interference, narrowband interference and comb interference, the modulation mode remains unchanged; if the interference type is any one of wideband noise interference and sweep interference, it is further judged whether the signal-to-interference ratio is greater than the upper limit value of the second threshold range, if yes, the modulation mode is updated to BPSK, if the signal-to-interference ratio hits the second threshold range, the modulation mode is updated to DSSS, and if the signal-to-interference ratio is less than the lower limit value of the second threshold range, the modulation mode is updated to FHSS; The satellite ground station module comprises a second communication receiving processing module, a second communication transmission processing module and an anti-interference strategy synchronization module, and is used for receiving the anti-interference strategy through the second communication receiving processing module and synchronizing to the anti-interference strategy synchronization module, and transmitting data processed by the anti-interference strategy through the second communication transmission processing module.

2. A method for satellite-ground collaborative anti-jamming based on integrated intelligent cognition of common sense, applied to the satellite-ground collaborative anti-jamming system of claim 1, wherein the satellite-ground collaborative anti-jamming system comprises a satellite-borne communication device module and a satellite ground station module, characterized in that, Comprising the following steps: The preset periodic task comprises, in time sequence, a perception periodic task, a synchronization periodic task and a communication periodic task, and the periodic tasks are executed simultaneously on the satellite communication device and the satellite ground station; The perception periodic task: the satellite communication device collects electromagnetic environment signals and obtains an anti-interference strategy based on the electromagnetic environment signals; The synchronization periodic task: the satellite communication device transmits the anti-interference strategy to the satellite ground station, and the satellite ground station updates the anti-interference strategy; The communication periodic task: the satellite ground station transmits data information to the satellite communication device using the anti-interference strategy, and the satellite communication device demodulates to obtain the data information.

3. The method of claim 2, wherein the method is characterized by, The step of obtaining the anti-interference strategy based on the electromagnetic environment signals comprises: Obtaining the signal-to-interference ratio and the results of electromagnetic interference based on the electromagnetic environment signals through energy detection; Obtaining the interference type and the interference parameters based on the electromagnetic environment signals and the results of electromagnetic interference; Obtaining the anti-interference strategy based on the interference type, the signal-to-interference ratio and the interference parameters through deep reinforcement learning DQN.

4. The method of claim 3, wherein the method further comprises: The step of obtaining the results of the signal-to-noise ratio and the electromagnetic interference based on the electromagnetic environment signal through the energy method specifically includes: a first threshold range and a signal power are preset; an interference power is obtained based on the electromagnetic environment signal through the energy method, and a noise power is obtained based on the electromagnetic environment signal through collection; a signal-to-noise ratio is obtained based on the signal power, the interference power and the noise power, and the expression is: wherein Ps is the signal power, PJ is the interference power, and PN is the noise power; a result of electromagnetic interference is obtained by comparing the signal-to-noise ratio with the first threshold range.

5. The method of claim 3, wherein the method further comprises: The step of obtaining the interference type and the interference parameter based on the electromagnetic environment signal and the result of the electromagnetic interference specifically includes: if the result of the electromagnetic interference is that there is interference, the interference type is obtained based on the electromagnetic environment signal through an interference identification module of the satellite communication device, and the interference parameter is obtained based on the electromagnetic environment signal through an interference parameter estimation module of the satellite communication device.

6. The method of claim 3, wherein the method further comprises: The step of obtaining the anti-interference strategy based on the interference type, the signal-to-noise ratio and the interference parameter through the deep reinforcement learning DQN specifically includes: a modulation mode set, a frequency point set, an information rate set, a channel coding code rate set and a transmission power set are pre-constructed, wherein the modulation mode set includes BPSK, DSSS and FHSS; the modulation mode set, the frequency point set, the information rate set, the channel coding code rate set and the transmission power set are combined to construct an anti-interference strategy set; a modulation mode is obtained based on the signal-to-noise ratio and the interference type through a rule-based decision tree; the anti-interference strategy is obtained based on the modulation mode, the anti-interference strategy set, the interference type and the interference parameter through the deep reinforcement learning DQN.

7. The method of claim 6, wherein the method further comprises: The step of obtaining the anti-interference strategy based on the modulation mode, the anti-interference strategy set, the interference type and the interference parameter through the deep reinforcement learning DQN specifically includes: time sequence interference information is constructed based on the interference parameter and the interference type, and the expression is: where f j,t is the center frequency of the interference parameter, B J is the interference signal bandwidth of the interference parameter, C J is the interference type, P J is the interference power of the interference parameter; different four-dimensional tuples are constructed based on the anti-interference strategy set according to the modulation mode dimension, and the expression is: wherein P is a transmit power value of the anti-jamming strategy, f s is a frequency point value of the anti-jamming strategy, R is an information rate value of the anti-jamming strategy, and η is a channel coding code rate of the anti-jamming strategy. a signal-to-noise ratio reward value, an information rate reward value and a spectrum utilization reward value are calculated based on the four-dimensional tuples and the time sequence interference information; an adapted anti-interference strategy is obtained based on the maximum Q value of the signal-to-noise ratio reward value, the information rate reward value and the spectrum utilization reward value through the deep reinforcement learning DQN, and the expression is: wherein reward SINR , reward R , reward η are the signal-to-interference-plus-noise ratio reward value, the information rate reward value, and the spectrum utilization reward value, respectively, A t+1 is an anti-interference strategy, and θ and θ' are a policy network weight and a target network weight, respectively, a value of the policy network weight being updated to the target network weight through training of the policy network.

8. The method of claim 7, wherein the method further comprises: The step of updating the value of the policy network weight to the target network weight specifically includes: samples containing states, rewards, actions and target actions are pre-constructed; a loss function is obtained based on the samples to train a network strategy, so that the value of the policy network weight is updated to the target network weight, and the expression is: 。 9. The method of claim 7, wherein the method further comprises: The step of calculating the signal-to-noise ratio reward value, the information rate reward value and the spectrum utilization reward value based on the four-dimensional tuples and the time sequence interference information specifically includes: Preset signal power P s , first information rate R l , second information rate R m , third information rate R h , first information rate result V R,l , second information rate result V R,m , third information rate result V R,h , first spectrum utilization η l , second spectrum utilization η m , third spectrum utilization η h , first spectrum utilization result V η,l , second spectrum utilization result V η,m , third spectrum utilization result V η,h ; The signal-to-interference-and-noise ratio reward value reward SINR The information rate reward value reward R The spectrum utilization reward value reward η The calculation formula of wherein PJ is the interference power, PN is the noise power, f j,t is the center frequency of the interference parameter.

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