Short-wave receiving signal anti-interference optimization method and system in strong interference communication scenario

Abstract

The embodiment of the present application provides a short-wave receiving signal anti-interference optimization method and system in a strong interference communication scene, relates to the technical field of short-wave communication anti-interference control, and comprises the following steps: analyzing a multi-channel space domain receiving signal to obtain real-time interference state information and target signal space direction characteristics; performing future interference state evolution prediction based on the real-time interference state information to obtain an interference trend prediction result; calculating a predicted driving array weight to generate an array directivity diagram by using the interference trend prediction result and the target signal space direction characteristics, and reconstructing a dynamic suppression directivity diagram according to the predicted interference space distribution information to perform space filtering processing on the multi-channel space domain receiving signal, so that an anti-interference receiving signal is obtained; and tracking the communication quality change of the anti-interference receiving signal to perform signal anti-interference optimization. The present application solves the problem that the prior art cannot adapt to the continuous change of the interference state in time, and the interference suppression effect is unstable and the target signal receiving quality is easily affected.

Description

Technical Field

[0001] This invention relates to the field of shortwave communication anti-interference control technology, and in particular to a method and system for optimizing shortwave received signal anti-interference in strong interference communication scenarios. Background Technology

[0002] In existing shortwave communication scenarios with strong interference, the interference state in the communication environment usually exhibits dynamic characteristics because the propagation process of shortwave signals is easily affected by factors such as changes in ionospheric state, changes in propagation path, and changes in external interference sources.

[0003] Existing technologies typically adjust their response based on preset anti-interference parameters or the interference status detected at the current moment when performing anti-interference receiving processing. The adjustment process is mainly used to reduce the impact of interference at the current moment, but it lacks the ability to adapt in advance to the continuous changes in interference direction, interference frequency, and interference energy over time.

[0004] When interference conditions change rapidly, existing methods are prone to adjustment lag, making it difficult for the array spatial response to match the changed interference distribution in a timely manner, resulting in unstable interference suppression capabilities. Furthermore, while enhancing interference suppression, existing methods can easily cause a decrease in the target signal directional gain or fluctuations in receiving performance, affecting the quality of target signal reception and making it difficult to simultaneously meet the requirements of stable anti-interference and reliable signal reception in complex and dynamic shortwave communication environments.

[0005] In summary, existing technologies typically employ methods based on preset parameter configuration or adjustment based on the current receiving state to optimize signal reception in shortwave communication environments with strong interference. This makes it difficult to adapt to continuous changes in interference conditions in a timely manner, resulting in unstable interference suppression capabilities and easily affecting the quality of target signal reception.

[0006] It should be noted that the information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0007] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a method and system for optimizing shortwave received signals under strong interference communication scenarios. It solves the problem that existing technologies typically use fixed anti-interference parameters or adjustments based on the current receiving state to optimize signal reception in strong interference shortwave communication environments, leading to difficulties in adapting to continuously changing interference conditions, unstable interference suppression effects, and easily compromised target signal reception quality. This invention achieves the effect of improving interference suppression capability and anti-interference stability under strong interference communication environments while ensuring reliable target signal reception and stable communication link operation. The specific technical solution is as follows:

[0008] According to a first aspect of the present invention, a method for optimizing anti-interference of shortwave received signals in a strong interference communication scenario is provided. The method includes: acquiring multi-channel spatial received signals in a shortwave communication environment in real time using a phased antenna array; obtaining real-time interference state information and target signal spatial orientation features by performing broadband spectrum sensing and spatial feature analysis on the multi-channel spatial received signals; using the real-time interference state information as a state prediction input, predicting the future interference state evolution based on a pre-constructed interference trend prediction model, and obtaining an interference trend prediction result; using the interference trend prediction result and the target signal spatial orientation features as array control constraints, performing prediction-driven beam weight optimization calculation, and obtaining the prediction-driven array weights of the phased antenna array; generating an array pattern based on the prediction-driven array weights, and dynamically reconstructing the suppression region in the array pattern according to the predicted interference spatial distribution information, and obtaining a dynamic suppression pattern; using the dynamic suppression pattern to perform spatial filtering on the multi-channel spatial received signals to obtain an anti-interference received signal; tracking the communication quality changes of the anti-interference received signal, and performing signal anti-interference optimization based on the interference trend prediction model and the iterative update of the prediction-driven array weights.

[0009] In one implementation, the real-time interference state information is used as the state prediction input, and the future interference state evolution is predicted based on a pre-built interference trend prediction model to obtain the interference trend prediction result. The following processing is also performed:

[0010] Historical interference state information is retrospectively analyzed for temporal correlation to obtain interference state evolution characteristics. Ionospheric variation characteristics and multipath propagation characteristics of the shortwave propagation process are introduced to characterize the propagation environment impact of the interference state evolution characteristics, obtaining environmentally coupled interference characteristics. An interference state change mapping relationship is established based on these environmentally coupled interference characteristics, and the interference trend prediction model is trained according to this mapping relationship. Historical interference state segments of a preset time length are segmented from the historical interference state information and temporally correlated and concatenated with the real-time interference state information to form a predicted input interference state sequence. The interference trend prediction model predicts future interference state evolution based on the predicted input interference state sequence, obtaining the interference trend prediction result. The real-time interference state information includes real-time interference frequency characteristics, real-time interference spatial orientation characteristics, and real-time interference energy distribution characteristics.

[0011] In one implementation, historical interference state information is traced back to perform temporal correlation analysis of interference states to obtain interference state evolution characteristics, and the following processing is also performed:

[0012] A preset time sliding window is used to segment the historical interference state sequence into continuous time periods from the historical interference state information; a time-dimensional correlation analysis is performed on the historical interference state sequence to extract the interference frequency feature sequence, interference spatial orientation feature sequence, and interference energy distribution feature sequence; the state change law analysis is performed to obtain the interference frequency change law, spatial orientation change law, and interference energy change law, which constitute the interference state evolution characteristics.

[0013] In one implementation, ionospheric variation characteristics and multipath propagation characteristics of the shortwave propagation process are introduced to characterize the propagation environment impact of the interference state evolution characteristics, thereby obtaining environmental coupling interference characteristics. The following processing is also performed:

[0014] Based on the ionospheric variation characteristics, the influence of propagation path changes on the spatial orientation and frequency variation patterns is analyzed to obtain interference frequency drift characteristics and interference spatial drift characteristics. Based on the multipath propagation characteristics, the propagation superposition influence analysis of the energy variation pattern is performed to obtain interference energy fluctuation characteristics. The interference frequency drift characteristics, interference spatial drift characteristics, and interference energy fluctuation characteristics are fused to obtain the environmental coupling interference characteristics.

[0015] In one implementation, an interference state change mapping relationship is established based on the environmental coupling interference characteristics, and the interference trend prediction model is trained based on the interference state change mapping relationship. The following processing is also performed:

[0016] The environmental coupling interference features are constructed according to the time correlation to obtain the environmental coupling interference state sequence; the interference state evolution relationship is extracted based on the environmental coupling interference state sequence to establish the interference state mapping relationship between the environmental coupling interference state change and the future interference state change; the time series prediction model parameters are trained and optimized according to the interference state mapping relationship to construct the interference trend prediction model.

[0017] In one implementation, the interference trend prediction result and the spatial orientation characteristics of the target signal are used as array control constraints to perform prediction drive beam weight optimization calculations, thereby obtaining the prediction drive array weights of the phased antenna array. The following processing is also performed:

[0018] The spatial distribution information of predicted interference is extracted from the predicted interference trend results as a spatial constraint for predicted interference; the direction-keeping constraint of the target signal is determined according to the spatial orientation characteristics of the target signal; a joint beam optimization constraint relationship is established based on the spatial constraint of predicted interference and the direction-keeping constraint of the target signal; the element amplitude and phase control parameters of the phased antenna array are optimized according to the joint beam optimization constraint relationship to obtain the predicted driving array weights.

[0019] In one implementation, the following processing is also performed:

[0020] The multi-channel spatial received signal is subjected to time-frequency transformation to obtain multi-channel time-frequency domain signal data; broadband spectrum scanning is performed on the multi-channel time-frequency domain signal data to obtain N spectral energy distribution information of N signal components; interference features are extracted under interference frequency band positioning based on the N spectral energy distribution information to obtain Q interference frequency component information; frequency state characterization is performed based on the Q interference frequency component information to obtain the real-time interference frequency characteristics.

[0021] In one implementation, the following processing is also performed:

[0022] Based on the spatial response differences of the phased antenna array elements, spatial feature analysis is performed on the multi-channel spatial received signal to obtain N spatial direction-of-arrival information and N spatial response intensity information corresponding to the N signal components. According to the real-time interference frequency characteristics, K interference signal components are selected from the N signal components, and the real-time interference spatial orientation characteristics are determined based on the K spatial direction-of-arrival information corresponding to the K interference signal components. According to the frequency characteristics of the K interference signal components, K interference spectrum energy distribution information is selected from the N spectrum energy distribution information. Based on the K interference spectrum energy distribution information, the K spatial direction-of-arrival information, and the K spatial response intensity information, spatial energy distribution analysis is performed to obtain the real-time interference energy distribution characteristics.

[0023] In one implementation, spatial feature identification is performed on the target signal component among the N signal components, and the target signal arrival direction information is extracted from the N spatial arrival direction information as the spatial orientation feature of the target signal.

[0024] According to a second aspect of the present invention, a shortwave received signal anti-interference optimization system for strong interference communication scenarios is provided. The system comprises: a multi-channel signal acquisition unit for real-time acquisition of multi-channel spatial received signals in a shortwave communication environment via a phased antenna array; an interference feature sensing unit for obtaining real-time interference state information and target signal spatial orientation features by performing broadband spectrum sensing and spatial feature analysis on the multi-channel spatial received signals; an interference state prediction unit for using the real-time interference state information as state prediction input and predicting future interference state evolution based on a pre-constructed interference trend prediction model to obtain an interference trend prediction result; and a beam weight optimization unit for optimizing the interference trend prediction result and the target signal... Using spatial azimuth features as array control constraints, predictive drive beam weight optimization calculations are performed to obtain the predictive drive array weights of the phased antenna array; a pattern reconstruction unit is used to generate an array pattern based on the predictive drive array weights, and dynamically reconstruct the suppression region in the array pattern according to the predicted interference spatial distribution information to obtain a dynamic suppression pattern; a spatial filtering processing unit is used to perform spatial filtering processing on the multi-channel spatial received signal using the dynamic suppression pattern to obtain an anti-interference received signal; an anti-interference optimization iteration unit is used to track the communication quality changes of the anti-interference received signal and perform signal anti-interference optimization based on the interference trend prediction model and the predictive drive array weights iteratively updated.

[0025] Beneficial effects of the embodiments of the present invention:

[0026] In the solution provided by this invention, multi-channel spatial received signals in the shortwave communication environment are acquired in real time through a phased antenna array, providing a data foundation for subsequent acquisition of interference state change information and target signal spatial characteristics. By performing broadband spectrum sensing and spatial feature analysis on the multi-channel spatial received signals, real-time interference state information and target signal spatial orientation features are extracted, achieving joint sensing of the interference distribution state and target signal spatial location in the current communication environment. The real-time interference state information is used as state prediction input, and future interference state evolution is predicted based on a pre-constructed interference trend prediction model to obtain interference trend prediction results. This enables the anti-interference control process to shift from a passive response to the current interference state to an active adjustment based on future interference change trends. The interference trend prediction results and target signal spatial orientation features are used as array control constraints to perform prediction-driven beam weight optimization calculations, obtaining the prediction-driven array weights of the phased antenna array. This invention enables the array's spatial response to adapt to both future changes in interference direction and target signal reception requirements, improving the accuracy of beam control in dynamic interference environments. Based on the predicted driving array weights, an array pattern is generated, and the suppression region in the array pattern is dynamically reconstructed according to the predicted spatial distribution information of interference, resulting in a dynamic suppression pattern. This allows the array to form a suppression response that matches future spatial changes in interference, enhancing its spatial suppression capability against time-varying interference signals. The dynamic suppression pattern is then used to perform spatial filtering on the multi-channel spatial received signal to obtain an anti-interference received signal, effectively weakening the spatial components of the interference signal and improving the reception quality of the target communication signal. By tracking changes in the communication quality of the anti-interference received signal and iteratively updating it based on the interference trend prediction model and the predicted driving array weights, the prediction model output and array control parameters can be continuously optimized according to changes in the communication environment, achieving dynamic enhancement of anti-interference capability in shortwave strong interference scenarios. This achieves the effect of improving interference suppression capability and anti-interference stability in strong interference communication environments while ensuring reliable target signal reception and stable operation of the communication link. Of course, implementing any product or method of this invention does not necessarily require achieving all of the above advantages simultaneously. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This invention illustrates a flowchart of the shortwave received signal anti-interference optimization method for strong interference communication scenarios provided by the present invention.

[0029] Figure 2 A schematic diagram of the anti-interference optimization system for shortwave received signals in a strong interference communication scenario provided by the present invention is shown.

[0030] Figure labeling: 1. Multi-channel signal acquisition unit; 2. Interference feature sensing unit; 3. Interference state prediction unit; 4. Beam weight optimization unit; 5. Radiation pattern reconstruction unit; 6. Spatial filtering processing unit; 7. Anti-interference optimization iteration unit. Detailed Implementation

[0031] To facilitate understanding of the present invention, a more complete description of the invention will be given below with reference to the accompanying drawings, which illustrate preferred embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein; rather, these embodiments are provided to enable a more thorough and complete understanding of the disclosure of the invention.

[0032] Furthermore, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0033] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0034] Unless otherwise expressly stated, throughout the specification and claims, the term "comprising" or its variations such as "including" or "comprises" shall be understood to include the stated elements or components without excluding other elements or other components.

[0035] The present invention provides a method and system for optimizing shortwave received signals in strong interference communication scenarios. This method addresses the problem that existing technologies typically use fixed anti-interference parameters or adjust them based on the current receiving state to optimize signal reception in strong interference shortwave communication environments. This results in difficulty in adapting to continuous changes in interference conditions, leading to unstable interference suppression effects and easily affecting the quality of target signal reception.

[0036] Example 1: See Figure 1 The flowchart of the anti-interference optimization method for shortwave received signals in a strong interference communication scenario provided by the embodiments of the present invention includes:

[0037] Step S100: Real-time acquisition of multi-channel spatial received signals in the shortwave communication environment through a phased antenna array.

[0038] Specifically, in this embodiment, the phased antenna array is an array-type receiving device for multi-channel synchronous reception of radio space signals in a shortwave communication environment. It includes multiple array element units arranged according to a preset spatial structure, and each array element unit is connected to a corresponding radio frequency receiving channel.

[0039] Multiple array elements are used to synchronously acquire spatially propagated signals in the shortwave communication environment, and multiple received data containing amplitude, phase, and time information are obtained based on the receiving channels corresponding to each array element. The multiple received data are then associated with channel identifiers and synchronously combined to form a multi-channel spatial received signal with spatial sampling information. The multi-channel spatial received signal is used for subsequent broadband spectrum sensing, signal component analysis, and extraction of spatial features of target signals and interference signals based on the spatial response differences of array elements.

[0040] Step S200: By performing broadband spectrum sensing and spatial feature analysis on the multi-channel spatial received signal, real-time interference status information and target signal spatial orientation features are obtained.

[0041] In one implementation, the method step S200 provided by the present invention includes:

[0042] Step S200-11: Perform time-frequency transformation on the multi-channel spatial received signal to obtain multi-channel time-frequency domain signal data.

[0043] Step S200-12: By performing broadband spectrum scanning on the multi-channel time-frequency domain signal data, obtain N spectral energy distribution information of N signal components.

[0044] Step S200-13: Based on the N spectral energy distribution information, perform interference feature extraction under interference frequency band positioning to obtain Q interference frequency component information.

[0045] Step S200-14: Based on the Q interference frequency component information, perform frequency state characterization to obtain the real-time interference frequency characteristics.

[0046] In one implementation, the method step S200 provided by the present invention includes:

[0047] Step S200-21: Based on the spatial response differences of the phased antenna array elements, perform spatial feature analysis on the multi-channel spatial received signal to obtain N spatial direction of arrival information and N spatial response intensity information corresponding to the N signal components.

[0048] Step S200-22: Based on the real-time interference frequency characteristics, select K interference signal components from the N signal components, and determine the real-time interference spatial orientation characteristics based on the K spatial arrival direction information corresponding to the K interference signal components.

[0049] Step S200-23: Based on the frequency characteristics of the K interference signal components, select K interference spectrum energy distribution information from the N spectrum energy distribution information.

[0050] Step S200-24: Perform spatial energy distribution analysis based on the K interference spectrum energy distribution information, K spatial direction of arrival information and K spatial response intensity information to obtain the real-time interference energy distribution characteristics.

[0051] In one implementation, the method step S200-3 provided by the present invention includes: performing spatial feature recognition on the target signal component among the N signal components, and extracting the target signal arrival direction information from the N spatial arrival direction information as the spatial orientation feature of the target signal.

[0052] Since the multi-channel spatial received signal is synchronously acquired by multiple array elements of the phased antenna array, it simultaneously contains target communication signals, interference signals incident from different directions, and signal fluctuation information caused by changes in the shortwave propagation environment. Different types of signals have a superposition relationship in the time domain, making it difficult to distinguish them directly based on the time domain amplitude.

[0053] Therefore, in this embodiment, the multi-channel spatial received signal is first transformed by time-frequency conversion, and the time-domain sampling data in each receiving channel is mapped to the frequency-time joint characterization domain to obtain the multi-channel time-frequency domain signal data that can reflect the signal frequency distribution and its changes over time. The multi-channel time-frequency domain signal data retains the frequency occupancy status and energy change relationship of the original received signal in different time segments.

[0054] Since target signals and interference signals in shortwave communication environments typically have different frequency occupancy characteristics, energy distribution characteristics, and continuous variation characteristics, this embodiment performs broadband spectrum scanning based on the multi-channel time-frequency domain signal data. The broadband spectrum response is divided according to frequency continuity, spectral energy concentration, and frequency occupancy range. Frequency regions with relatively independent frequency response characteristics are taken as analyzable signal components, and N signal components and N spectral energy distribution information corresponding to the N signal components are obtained.

[0055] The N signal components represent the signal components corresponding to different frequency regions obtained from the broadband spectrum scanning results. They are used to describe the frequency components with independent energy response relationships in the frequency domain, and do not correspond to the number of array elements or the number of receiving channels in the phased antenna array.

[0056] The N spectral energy distribution information is used to characterize the energy changes in the frequency regions corresponding to each signal component, including the energy occupancy status, energy change trend, and persistence of each frequency region, providing an analytical basis for subsequent interference frequency identification.

[0057] Since the N spectral energy distribution information may simultaneously include the target communication signal spectral response, the background noise spectral response, and the abnormal spectral response generated by multiple interference sources, directly using all spectral information for interference state analysis will result in a mixture of normal communication components and interference components.

[0058] Therefore, this embodiment locates the interference frequency band based on the N spectral energy distribution information, and combines the energy intensity, duration of occupation and frequency change trend of each signal component in the corresponding frequency region to identify the interference characteristics of different signal components. The signal components that meet the interference identification conditions are determined as interference frequency components, and Q interference frequency component information is obtained.

[0059] It should be noted that the Q interference frequency components mentioned in this embodiment are derived from the screening results of the N spectral energy distribution information, and their quantity relationship satisfies Q≤N. N represents the total number of analyzable spectral signal components obtained after broadband spectrum scanning, and Q represents the number of frequency components confirmed to have interference characteristics after interference identification. By screening Q interference frequency components from the N spectral signal components, the influence of target communication signals and background noise on interference state analysis can be reduced, making the interference state information input to the subsequent prediction model more accurate.

[0060] Furthermore, since interference signals in shortwave communication are usually time-continuous, their frequency occupancy status may change with the working status of the interference source, the propagation path, and the communication environment, resulting in frequency drift, changes in occupancy time, or changes in the combination of interference frequencies. It is difficult to accurately reflect the dynamic evolution of the interference frequency using only the Q interference frequency components obtained within the current time window. Therefore, it is necessary to combine the detection results within a continuous time window to perform time correlation analysis on the Q interference frequency components.

[0061] Specifically, interference frequency component information corresponding to multiple time windows is continuously acquired according to a preset time interval. The interference frequency components detected in different time windows are matched according to the frequency position proximity relationship, frequency change continuity relationship and energy change correlation relationship to determine the correspondence of the same interference frequency component in different time windows, and establish the frequency state sequence formed by the interference frequency component changing with time.

[0062] For the frequency state sequence, the frequency position offset, continuous existence time, and spectral energy changes of the corresponding interfering frequency components between adjacent time windows are analyzed. The frequency position offset describes whether the interfering frequency has drifted, the continuous existence time describes whether the interfering frequency remains stably occupied, and the spectral energy changes describe the trend of interference intensity over time. Through the above analysis, the stable existence, frequency migration, addition, and disappearance states of the interfering frequency components can be identified.

[0063] After completing the time correlation analysis, the frequency position status, time duration status, and change trend status corresponding to each interference frequency component are correlated and fused to form the real-time interference frequency characteristics.

[0064] The real-time interference frequency characteristics are used to describe the dynamic interference frequency state in the current shortwave communication environment. They include not only the frequency position and energy state of the Q interference frequency components detected at present, but also the frequency migration relationship and state change trend formed by the change of each interference frequency component over time. This provides a basis for subsequent determination of interference signal components, obtaining real-time interference spatial orientation characteristics and real-time interference energy distribution characteristics by combining spatial response analysis.

[0065] Multiple elements in a phased antenna array are arranged according to a preset spatial structure, with defined spatial intervals between different elements. When a signal in a shortwave communication environment propagates to the phased antenna array in a specific direction, the arrival time of the same signal at different elements varies due to the different distances between each element and the signal propagation path. This causes phase and amplitude response changes in the signals received by each element, which are related to the spatial incident direction. The difference in spatial response of the elements represents the difference in amplitude and phase response of the same signal in the receiving channels of different elements. This difference contains information about the signal's spatial propagation path and can be used to reflect the spatial directional relationship of the signal source relative to the array.

[0066] After completing the aforementioned broadband spectrum scan and obtaining N signal components, this embodiment further extracts spatial features from the multi-channel received data corresponding to each signal component. Specifically, the frequency domain response data of each signal component in different array element receiving channels are correlated to obtain the complex response results of the signal component at each array element position. The complex response results include amplitude response information describing the strength of signal reception and phase response information describing the propagation delay differences between different array elements. The frequency domain response data is extracted from the corresponding frequency region in the multi-channel time-frequency domain signal data.

[0067] Furthermore, based on the spatial coordinate relationship between the elements in the phased antenna array, the phase difference relationship formed between the same signal component and the spatial propagation path difference is converted into a corresponding relationship, establishing a mapping relationship between the phase difference between elements and the spatial incident angle, and determining the spatial incident direction corresponding to the signal component based on the mapping relationship. The spatial incident direction reflects the correspondence between the signal propagation direction and the array reference coordinate system, and is used to characterize the angular position of the signal source relative to the antenna array, i.e., the spatial arrival direction information.

[0068] Simultaneously, the amplitude response results of each signal component on different array elements are jointly calculated to obtain the overall response level of the signal component at the array receiver. The spatial response intensity information does not represent the independent power value in a single array element's receiving channel, but rather an array spatial response index obtained by comprehensively calculating the amplitude response results of multiple array elements for the same signal component. This index reflects the overall received strength of the signal after spatial matching when incident from the corresponding spatial direction, and can be used to distinguish the received strength relationship of signals from different spatial directions.

[0069] Through the above processing, the N signal components obtained after spectrum division are respectively endowed with corresponding spatial direction attributes and spatial response intensity attributes, establishing the correlation between frequency dimension and spatial dimension, providing a data foundation for subsequent screening of interference signal components and determining the spatial distribution of interference by combining real-time interference frequency characteristics.

[0070] After spatial feature extraction, the N signal components each possess corresponding frequency attributes, spatial direction of arrival information, and spatial response intensity information. However, they simultaneously include target communication signals, background noise, and interference signals from multiple sources. To avoid mistaking normal communication signals or environmental noise for interference sources, this embodiment utilizes the aforementioned obtained real-time interference frequency features as frequency discrimination conditions to identify the interference attributes of the N signal components.

[0071] Specifically, the frequency occupancy range, center frequency position, and frequency change state corresponding to the N signal components are correlated and matched with the interference frequency position, frequency drift state, and continuous occupancy state included in the real-time interference frequency characteristics. When the frequency range of a certain signal component corresponds to the interference frequency state detected in real time, and the frequency change pattern exhibited by the signal component satisfies the interference frequency evolution characteristics, the signal component is determined to have interference attributes.

[0072] After the frequency correlation matching described above, K signal components with interference attributes are selected from the N signal components. Here, K represents the number of interference signal components determined after frequency feature matching. The Q interference frequency component information represents the abnormal frequency state identified based on spectral energy characteristics, while the K interference signal components represent the specific signal components corresponding to the abnormal frequency state after spatial feature mapping. These correspond to the frequency-level interference state description and the signal-level interference source identification result, respectively. Therefore, the K interference signal components are further determined from the N signal components by the frequency feature correlation matching of the Q interference frequency component information, satisfying K ≤ N. In other words, N represents the total number of analyzable spectral components obtained after broadband spectral scanning, while K represents the number of spectral components confirmed as interference sources after interference feature judgment. A filtering relationship is formed between the two, from frequency detection results to interference identification results.

[0073] Since the spatial direction of arrival information for each interference signal component has already been obtained through spatial feature processing, the spatial direction of arrival information for K interference signal components is further extracted and combined according to the angular distribution relationship between multiple interference directions to form a real-time interference spatial orientation feature. This real-time interference spatial orientation feature represents the spatial distribution of interference sources in the current communication environment, including the incident direction of each interference source and the relative spatial relationship between multiple interference directions, providing a spatial basis for interference direction suppression in subsequent array beam control.

[0074] The K interference signal components are derived from the interference attribute screening results of N signal components, and a correspondence between each of the N signal components and the spectral energy distribution information has been established during the broadband spectrum scanning process. Based on this correspondence, the corresponding spectral region can be located from the N spectral energy distribution information through the signal index corresponding to the K interference signal components, thereby obtaining the energy change state of each interference signal component in the frequency domain.

[0075] Specifically, based on the center frequency position, frequency occupancy range, and frequency change state corresponding to the K interference signal components, frequency range matching is performed on the N spectral energy distribution information to determine the spectral region corresponding to each interference signal component. Energy change data within the corresponding region is then extracted to obtain the K interference spectral energy distribution information. The K interference spectral energy distribution information corresponds one-to-one with the K interference signal components and is used to describe the energy occupancy of different interference sources within the corresponding frequency range, including the current energy level of the interference signal, the energy distribution state within the frequency range, and the energy fluctuation trend over time.

[0076] Through the above processing, the interference identification results in the frequency dimension are further mapped to specific spectral energy states, enabling subsequent judgment of the actual impact of different interference sources by combining spatial direction information.

[0077] Based on this, the degree of interference in shortwave communication environments is determined not only by the location of the interfering frequency but also by the propagation direction of the interfering signal and the array's receiving response strength. Interfering signals within the same frequency range, originating from different spatial directions, may have different impacts on the target communication direction. Furthermore, even if interfering signals from different directions have the same frequency occupancy characteristics, their spatial response strength after reaching the array may differ. Specifically, the spectral energy distribution information describes the energy occupancy state of the interfering signal in the frequency dimension, including the positional relationship of the corresponding frequency region, the energy distribution, and the energy change trend over time. The spatial arrival direction information describes the incident direction of the interfering signal relative to the phased antenna array, used to determine the spatial source corresponding to different interfering signal components. The spatial response strength information describes the overall response level formed after the interfering signal in the corresponding spatial direction is received by the array, reflecting the strength of the impact of interfering signals from different spatial directions at the array receiver. Since the above three types of information reflect the frequency occupancy status, spatial source relationship and array reception influence of the interference signal respectively, a single dimension of information cannot fully describe the actual influence range of the interference signal. Therefore, it is necessary to jointly correlate the spectral energy distribution information, spatial direction of arrival information and spatial response intensity information to form an interference spatial distribution description result that simultaneously includes frequency, spatial direction and energy intensity relationship.

[0078] Therefore, this embodiment jointly correlates K interference spectral energy distribution information, K spatial direction of arrival information, and K spatial response intensity information. Specifically, for each interference signal component, according to its correspondence, the spectral energy distribution information, spatial direction of arrival information, and spatial response intensity information of that interference signal component are data-bound, so that each interference signal component forms a joint feature description including frequency state, spatial direction, and array response intensity. Furthermore, based on the spatial direction relationship and energy response level corresponding to different interference signal components, the energy contribution of each interference signal component at different spatial locations is comprehensively characterized to obtain the energy distribution relationship of the interference signal in the spatial range.

[0079] When the spectral energy of a certain interference signal component is high and its spatial response intensity is large, it indicates that the interference source has a strong spatial influence capability in the corresponding direction. When multiple interference signal components correspond to different spatial directions, a multi-directional interference energy distribution relationship is formed according to the energy state corresponding to each direction.

[0080] By fusing the frequency energy state, spatial direction of arrival, and spatial response intensity corresponding to K interference signal components, real-time interference energy distribution characteristics are obtained. These real-time interference energy distribution characteristics are used to describe the interference energy occupancy in different spatial directions in the current shortwave communication environment. They not only reflect the number and strength differences of interference sources but also the distribution relationship of interference energy in the spatial range, providing input basis for subsequent interference trend prediction, array weight optimization, and dynamic suppression pattern generation.

[0081] Ultimately, the real-time interference frequency characteristics, real-time interference spatial orientation characteristics, and real-time interference energy distribution characteristics together constitute the real-time interference status information.

[0082] In actual shortwave communication, the broadband spectrum scanning stage mainly separates the received signal based on the frequency occupancy relationship. The N signal components only represent signal components with independent response characteristics in different frequency regions. It is not possible to directly determine the service type corresponding to each signal component. Therefore, it is necessary to further combine the frequency stability, communication bandwidth characteristics and spatial response characteristics of the target communication signal to identify the target signal component corresponding to the target communication link from the N signal components.

[0083] Specifically, in this embodiment, frequency occupancy information, spectral energy change information, and spatial response information are acquired for each of the N signal components, and each signal component is matched and judged according to preset target communication signal identification conditions. The target communication signal typically has a continuously occupied communication frequency band, a relatively stable frequency center position, and spatial response characteristics related to the communication link direction, while interference signals typically exhibit abnormal frequency occupancy relationships, abnormal energy changes, spatial directions inconsistent with the target communication direction, or frequency states that do not conform to communication service rules.

[0084] Therefore, by comprehensively judging the frequency state, energy state, and spatial response state corresponding to the N signal components, the target signal component corresponding to the target communication service is determined from the N signal components. The purpose of identifying the target signal component is different from that of identifying interference signal components. It is mainly used to determine the target communication direction that needs to be maintained during subsequent array control, rather than participating in interference state statistics.

[0085] After determining the target signal component, since the aforementioned spatial feature processing has already calculated the corresponding N spatial direction-of-arrival (DOA) information for each of the N signal components, the target spatial DOA information corresponding to the target signal component is extracted from the N DOA information based on the one-to-one correspondence between the target signal component and the N signal components. The target spatial DOA information represents the incident angle and spatial position of the signal relative to the phased antenna array, and is determined by the phase difference variation of the target signal component on different array elements and the spatial geometric layout of the array elements.

[0086] Furthermore, the extracted target spatial arrival direction information is used as the target signal spatial orientation feature to describe the source direction of the target signal in the current communication link. The target signal spatial orientation feature corresponds to the aforementioned real-time interference spatial orientation feature, wherein the real-time interference spatial orientation feature is used to describe the spatial distribution of multiple interference signal components, while the target signal spatial orientation feature is used to maintain the direction of the target communication signal.

[0087] Step S300: Use the real-time interference state information as the state prediction input, and predict the future interference state evolution based on the pre-built interference trend prediction model to obtain the interference trend prediction result.

[0088] In one implementation, the real-time interference state information is used as the state prediction input, and the future interference state evolution is predicted based on a pre-built interference trend prediction model to obtain the interference trend prediction result. The method step S300 provided by the present invention includes:

[0089] Step S310: Retrospectively analyze historical interference state information to obtain interference state evolution characteristics.

[0090] Step S320: Introduce the ionospheric variation characteristics and multipath propagation characteristics of the shortwave propagation process, characterize the propagation environment impact of the interference state evolution characteristics, and obtain the environmental coupling interference characteristics.

[0091] Step S330: Establish a mapping relationship for the change of interference state based on the environmental coupling interference characteristics, and train the interference trend prediction model according to the mapping relationship for the change of interference state.

[0092] Step S340: Segment historical interference state segments of a preset time length from the historical interference state information, and concatenate them with the real-time interference state information in a time-correlated manner to form a predicted input interference state sequence.

[0093] Step S350: The interference trend prediction model predicts the future interference state evolution based on the predicted input interference state sequence to obtain the interference trend prediction result. The real-time interference state information includes real-time interference frequency characteristics, real-time interference spatial orientation characteristics, and real-time interference energy distribution characteristics.

[0094] In one implementation, historical interference state information is traced back to perform temporal correlation analysis of interference states to obtain interference state evolution characteristics. The method step S310 provided by this invention includes:

[0095] Step S311: Use a preset time sliding window to segment the historical interference state sequence of consecutive time periods from the historical interference state information.

[0096] Step S312: Perform time-dimensional correlation analysis on the historical interference state sequence, extract the interference frequency feature sequence, interference spatial orientation feature sequence, and interference energy distribution feature sequence, perform state change law analysis, obtain the interference frequency change law, spatial orientation change law, and interference energy change law, and constitute the interference state evolution characteristics.

[0097] In one implementation, ionospheric variation characteristics and multipath propagation characteristics of the shortwave propagation process are introduced to characterize the propagation environment impact of the interference state evolution characteristics, thereby obtaining environmental coupling interference characteristics. The method step S320 provided by this invention includes:

[0098] Step S321: Based on the ionospheric change characteristics, analyze the influence of propagation path changes on the spatial orientation change law and frequency change law to obtain interference frequency drift characteristics and interference spatial drift characteristics.

[0099] Step S322: Based on the multipath propagation characteristics, perform propagation superposition influence analysis on the energy change law to obtain the interference energy fluctuation characteristics.

[0100] Step S323: Fuse the interference frequency drift characteristics, interference spatial drift characteristics, and interference energy fluctuation characteristics to obtain the environmental coupling interference characteristics.

[0101] In one implementation, an interference state change mapping relationship is established based on the environmental coupling interference characteristics, and the interference trend prediction model is trained based on the interference state change mapping relationship. The method step S330 provided by this invention includes:

[0102] S331: Construct a feature sequence based on the environmental coupling interference features according to the time correlation to obtain an environmental coupling interference state sequence.

[0103] S332: Extract the interference state evolution relationship based on the environmental coupled interference state sequence, and establish the interference state mapping relationship between environmental coupled interference state changes and future interference state changes.

[0104] S333: Based on the aforementioned interference state mapping relationship, the parameters of the time series prediction model are trained and optimized to construct the interference trend prediction model.

[0105] Specifically, the historical interference state information is used to record the interference state changes corresponding to multiple historical time windows during shortwave communication, including historical interference frequency characteristics, historical interference spatial orientation characteristics, and historical interference energy distribution characteristics. To extract the dynamic correlation formed by the change of interference state over time, a preset time sliding window is used to segment the historical interference state information. The time sliding window represents a data interception range that moves continuously within the historical interference state information according to a fixed time length or a fixed number of samples; each window movement corresponds to acquiring interference state data within a continuous time range.

[0106] In the specific processing, historical interference state information is arranged according to time sequence. Within the coverage area of ​​each sliding window, the interference frequency state, interference spatial orientation state, and interference energy state for the corresponding time period are extracted and combined according to their chronological order to form a sequence of historical interference states corresponding to multiple consecutive time windows. The historical interference state sequence preserves the processes of interference frequency change, spatial orientation change, and energy change, and is used to characterize the evolution of historical interference states over time.

[0107] Based on the historical interference state sequence, time-dimensional correlation analysis is performed on the interference state data corresponding to different time windows. The interference state data is reorganized according to three characteristic dimensions: frequency, space, and energy, to form interference frequency characteristic sequence, interference spatial orientation characteristic sequence, and interference energy distribution characteristic sequence, respectively.

[0108] The interference frequency feature sequence describes the positional changes, frequency drift trends, and persistent occupancy relationships of interference frequency components within different time windows. By calculating the frequency offset relationship between adjacent time windows, the variation law of interference frequency is obtained. The interference spatial orientation feature sequence describes the change relationship of the spatial incident direction of the interference signal over time. By analyzing the spatial orientation changes corresponding to different time windows, the variation law of spatial orientation is obtained. The interference energy distribution feature sequence describes the enhancement, weakening, and distribution migration of interference signal energy within different time windows. By analyzing the amplitude and duration of energy changes, the variation law of interference energy is obtained.

[0109] The interference frequency variation pattern, spatial orientation variation pattern, and interference energy variation pattern are fused together over time to form the interference state evolution characteristics, which can simultaneously characterize the dynamic changes of historical interference states in the frequency domain, spatial domain, and energy domain.

[0110] After obtaining the aforementioned interference state evolution characteristics, ionospheric variation characteristics and multipath propagation characteristics during shortwave propagation are introduced to analyze the interference state changes caused by propagation environmental factors. The ionospheric variation characteristics are used to describe changes in ionospheric electron density, reflection height, and propagation path. The multipath propagation characteristics are used to describe the path delay differences, phase superposition relationships, and energy superposition changes that occur when the shortwave signal reaches the receiving end through multiple propagation paths.

[0111] Based on the ionospheric variation characteristics, a propagation path impact analysis is performed on the variation patterns of interference frequency and spatial orientation. The changes in ionospheric parameters are correlated with the changes in interference state to analyze the impact of propagation path changes on the interference frequency position and spatial incident direction. Specifically, based on the correspondence between the change in interference center frequency and ionospheric state within different time windows, the frequency position shift trend caused by changes in the propagation environment is extracted, obtaining the interference frequency drift characteristics; based on the relationship between the change in spatial incident direction and propagation path shift, the direction migration trend caused by changes in propagation path is extracted, obtaining the interference spatial drift characteristics.

[0112] Based on the multipath propagation characteristics, the propagation superposition effect analysis is performed on the interference energy change law. The energy change state in different time windows is correlated with the number of propagation paths, path delay difference and phase superposition relationship. The coherence enhancement, destructive attenuation and random energy fluctuation phenomena generated during multipath propagation are analyzed, and the energy change trend caused by the propagation superposition effect is extracted to obtain the interference energy fluctuation characteristics.

[0113] The interference frequency drift characteristics, interference spatial drift characteristics, and interference energy fluctuation characteristics are synchronously correlated and fused according to corresponding time windows to establish a correspondence between changes in the propagation environment and changes in the interference state, forming the environmentally coupled interference characteristics. These environmentally coupled interference characteristics include information on changes in the state of the interference source and the impact of changes in the shortwave propagation environment on the interference manifestation, and are used to describe the comprehensive changing relationship of the interference state under complex propagation environments.

[0114] When establishing the interference state change mapping relationship based on the environmental coupling interference characteristics, the historical environmental coupling interference characteristics are first constructed into a sequence according to the time correlation. According to the arrangement order of the environmental coupling interference characteristics corresponding to different historical time windows, the interference frequency drift, interference spatial drift and interference energy fluctuation are combined in time synchronization to form an environmental coupling interference state sequence.

[0115] Based on the continuous state change relationships in the environmental coupled interference state sequence, the state transition patterns between adjacent time windows are analyzed to extract the evolutionary relationships of interference states over time. Specifically, the environmental coupled interference states corresponding to multiple consecutive historical time windows are used as model training inputs, and the actual interference state changes corresponding to subsequent time windows are used as prediction targets. By analyzing the correlation between the direction, magnitude, and duration of historical interference state changes, a interference state mapping relationship is established between environmental coupled interference state changes and future interference state changes.

[0116] The interference trend prediction model is trained based on the interference state mapping relationship. The interference trend prediction model adopts a temporal prediction structure based on gated recurrent units, including an input layer, a temporal feature encoding layer, and a prediction output layer.

[0117] The input layer receives training input data consisting of historical environment-coupled interference state sequences from the training phase, and prediction input interference state sequences formed by transforming historical interference state fragments and real-time interference state information through environmental coupling features from the prediction phase. It also converts the interference frequency drift features, interference spatial drift features, and interference energy fluctuation features corresponding to each time window into a unified-dimensional state vector. The temporal feature encoding layer employs a gated recurrent unit structure, storing state information from historical time windows through hidden states and adjusting the influence ratio of state information on the prediction results at different time stages using update and reset gates, thereby extracting long-term change correlation features of the interference state. The prediction output layer outputs the interference state change results corresponding to future time windows based on the encoded temporal features, including future interference frequency drift trends, future interference spatial orientation change trends, and future interference energy change trends.

[0118] During model training, interference detection data from historical shortwave communication environments are acquired to construct training samples. The environmental coupling interference state sequence corresponding to multiple consecutive historical time windows is used as the model input, and the actual interference state change results detected in the corresponding future time windows are used as training labels. The prediction loss is calculated based on the error between the model prediction results and the actual labels, and the internal parameters of the model are adjusted by error backpropagation, so that the model gradually learns the mapping law from environmental coupling interference state to future interference state changes.

[0119] After the interference trend prediction model is trained, in order to predict the future trend of real-time interference status, it is necessary to combine the interference status change process within the historical time range with the real-time interference status information to construct a predictive input interference status sequence.

[0120] Specifically, the historical interference state information is segmented into state segments according to a preset time length to obtain continuous historical interference state segments adjacent to the real-time prediction time. The historical interference state segments are used to provide historical evolution information of the interference state over time, including the interference frequency state, interference spatial orientation state, and interference energy distribution state corresponding to multiple continuous time windows.

[0121] Subsequently, the historical interference state segments are spliced ​​together with the real-time interference state information acquired in real time, so that the interference state change process within the historical time range and the interference state detected in real time form a continuous time series. The real-time interference state information, as the latest state node in the time series, reflects the interference performance in the real-time communication environment, including real-time interference frequency characteristics, real-time interference spatial location characteristics, and real-time interference energy distribution characteristics.

[0122] During the splicing process, based on the temporal relationship between historical interference state fragments and real-time interference state information, the interference frequency characteristics, spatial orientation characteristics, and energy distribution characteristics corresponding to each time node are arranged according to a unified time index, ensuring that the resulting predicted input interference state sequence maintains the temporal continuity of interference state changes. This predicted input interference state sequence simultaneously includes historical interference state evolution information and real-time interference state information. Through a feature transformation method consistent with the model training phase, it forms input feature dimensions corresponding to the environment-coupled interference state sequence, ensuring consistency with the input structure of the interference trend prediction model and providing temporal prediction input for the interference trend prediction model.

[0123] The interference trend prediction model predicts the future evolution of interference states based on the predicted input interference state sequence. During the prediction process, the model first uses a temporal feature encoding structure to extract the correlation between historical and real-time interference states in the input sequence. Then, based on the interference state change mapping relationship obtained during the training phase, it infers the possible direction of change in the interference state within the future time window.

[0124] Specifically, the interference trend prediction model outputs the interference state change results corresponding to the future time window, including the future interference frequency drift trend, the future interference spatial orientation change trend, and the future interference energy change trend, thereby obtaining the interference trend prediction result. The interference trend prediction result is used to characterize the future evolution direction of the interference state during shortwave communication, providing a predictive basis for subsequent array weight adjustment and interference suppression direction control.

[0125] Step S400: Using the interference trend prediction result and the target signal spatial orientation characteristics as array control constraints, perform prediction drive beam weight optimization calculation to obtain the prediction drive array weight of the phased antenna array.

[0126] In one implementation, the interference trend prediction result and the spatial orientation characteristics of the target signal are used as array control constraints to perform prediction drive beam weight optimization calculation, thereby obtaining the prediction drive array weights of the phased antenna array. Step S400 of the method provided by this invention includes:

[0127] Step S410: Extract the predicted spatial distribution information of interference from the interference trend prediction results as a spatial constraint for the predicted interference.

[0128] Step S420: Determine the target signal direction maintenance constraint based on the target signal spatial orientation characteristics.

[0129] Step S430: Based on the predicted interference spatial constraint and the target signal direction preservation constraint, establish a joint beam optimization constraint relationship.

[0130] Step S440: Based on the joint beam optimization constraint relationship, optimize the amplitude and phase control parameters of the phased antenna array elements to obtain the predictive drive array weights.

[0131] In this embodiment, the interference trend prediction result reflects the direction of change of interference state within a future time window. The future interference spatial orientation change trend describes the migration relationship and spatial distribution changes of the incident direction of the interference signal over time, providing a predictive reference for subsequent spatial response adjustments of the array. The target signal spatial orientation feature describes the spatial orientation state corresponding to the desired received signal, enabling the array to maintain effective reception capability in the target signal direction during optimization.

[0132] This embodiment combines the interference trend prediction results obtained in step S300 above, and incorporates the future spatial orientation change trend of interference and the spatial orientation characteristics of the target signal into the phased antenna array beamforming process as a constraint basis for array spatial response optimization. By simultaneously incorporating the future spatial orientation change trend of interference and the spatial orientation information of the target signal, the array weight optimization process can take into account both the future interference suppression requirements and the target signal preservation requirements, realizing a shift from real-time interference state adjustment to prediction-driven beam control that combines interference evolution trend adjustment.

[0133] Specifically, this embodiment first establishes a predicted interference spatial constraint based on the future spatial orientation change trend of interference in the interference trend prediction results. The predicted interference spatial constraint is used to describe the possible spatial orientation changes of the interference signal within a future time range and the corresponding spatial response adjustment requirements, so that the array optimization process can consider the impact of interference orientation migration on the spatial response distribution in advance.

[0134] Since the future trend of interference space changes comes from the prediction results obtained in step S300 based on the historical interference state evolution law, this constraint not only reflects the currently detected interference space state, but also contains information on future directional changes caused by changes in the propagation environment and the continuous evolution of the interference state, thus providing a predictive basis for array space suppression.

[0135] After establishing the predicted interference spatial constraints, a target orientation preservation constraint is established based on the spatial orientation characteristics of the target signal. This target orientation preservation constraint limits the spatial response state corresponding to the target signal direction during array optimization, ensuring the array maintains the receiving gain in the target direction while adjusting its spatial suppression capability. Since phased array antennas generate different spatial orientation responses by adjusting the amplitude and phase relationships between their elements, changes in array weights affect not only the interference direction but also the target signal direction. Introducing the target orientation preservation constraint avoids a decrease in the target signal direction response due to excessive enhancement of interference suppression during optimization.

[0136] Furthermore, the spatial constraints of predicted interference and the target orientation maintenance constraints are jointly correlated to form a joint constraint relationship for array weight optimization. This joint constraint relationship describes the coordination between the future spatial state of interference and the target signal reception requirements, enabling the array optimization process to reduce the spatial response in the predicted interference direction while maintaining an effective response in the target signal direction. Through this joint constraint relationship, the array beamforming process can adjust the spatial pattern distribution according to future interference trends, rather than passively responding solely based on the current interference location.

[0137] Based on the aforementioned joint constraint relationship, the amplitude control parameters and phase control parameters of each element in the phased antenna array are optimized to obtain the predicted driving array weights. These amplitude and phase control parameters are used to adjust the amplitude-phase relationship between the output signals of each element, enabling the array to form a radiation pattern that meets predetermined spatial response requirements. During the optimization process, the spatial direction requiring reduced response is determined based on the predicted trend of interference spatial orientation changes. Combined with the target direction maintenance constraint, the amplitude and phase parameters of each element are adjusted so that the optimized array weights can suppress the response in future interference directions while maintaining stable reception in the target signal direction.

[0138] The final predicted array weights integrate the future spatial variation trend of interference and the target signal direction maintenance requirements, enabling the phased antenna array to adjust the spatial response distribution in advance. This gives the array the ability to predict and adapt to dynamic shortwave interference environments, providing a spatial control basis for subsequent signal reception optimization and interference suppression.

[0139] Step S500: Generate an array pattern based on the predicted driving array weights, and dynamically reconstruct the suppression region in the array pattern according to the predicted interference spatial distribution information to obtain a dynamic suppression pattern.

[0140] Specifically, based on the predicted driving array weights, the amplitude control parameters and phase control parameters corresponding to each array element are loaded into the corresponding control channel of the phased antenna array, causing the output signals of each array element to be spatially superimposed according to a preset amplitude-phase relationship. During the spatial superposition process, signals from different array elements arriving at different spatial positions will generate phase differences due to differences in propagation distances. Through joint control of the array element amplitude and phase parameters, the signals in the target direction are mutually enhanced, while signals in some interference directions are canceled out, thereby forming a spatial response distribution with direction selectivity. Based on this spatial response distribution, the array radiation pattern is obtained. The array radiation pattern contains response intensity information corresponding to different spatial angles, which is used to represent the changes in the receiving gain of signals in each direction under the current array weight control state.

[0141] Further, based on the predicted interference spatial distribution information obtained in step S300, the suppression region in the array pattern is dynamically reconstructed. The predicted interference spatial distribution information describes the potential spatial direction change trend of the interference signal within a future time window, including interference direction migration relationships, changes in the interference concentration area, and changes in the interference spatial distribution. Since the interference direction in the shortwave communication environment may dynamically change due to variations in ionospheric propagation conditions, multipath propagation effects, and changes in the state of the interference source, a fixed-direction suppression region may not be able to continuously cover future interference locations. Based on the predicted interference spatial distribution information, the response gain of the region corresponding to the future interference direction in the array pattern is readjusted, causing the original spatial suppression region to shift position, adjust range, or change suppression intensity according to the future interference change trend.

[0142] In the specific reconstruction process, the spatial direction corresponding to the predicted spatial distribution of interference is mapped to the spatial angle region of the array pattern, determining the directional range that future interference may affect, and adjusting the array response strength of the corresponding region based on this directional range. When the prediction result indicates that a certain interference direction will increase or migrate within a future time window, the spatial response relationship corresponding to the array weights is re-optimized to make the array pattern form a low-gain region in that predicted direction, thereby reducing the impact of the interference signal entering the receiving link in that direction. Simultaneously, during the suppression region adjustment process, the effective response corresponding to the target signal direction is maintained to avoid a decrease in target signal reception performance due to suppression region expansion or direction migration.

[0143] For example, in a shortwave communication process, if the interference signal detected in the current time window is mainly incident from the -35° spatial angle direction, after prediction in step S300, due to the historical evolution of interference states and the influence of changes in the propagation environment, it is determined that the interference signal will migrate towards the +20° direction in the future time window, forming a strong interference distribution in that direction. Based on this prediction result, the original suppression region in the array pattern is adjusted from the region corresponding to the current interference direction to the region corresponding to the predicted future interference direction, so that the array forms a low response region at +20°, while maintaining the stability of the main receiving region in the direction of the target signal, thereby achieving early suppression of the future changing interference direction.

[0144] After the above dynamic adjustment, the obtained dynamic suppression pattern can simultaneously reflect the target signal direction preservation requirement and the future interference direction suppression requirement, enabling the phased antenna array to actively adjust its spatial response according to the interference evolution trend, and providing a spatial selection basis for subsequent spatial filtering processing.

[0145] Step S600: Spatial filtering is performed on the multi-channel spatial received signal using the dynamic suppression pattern to obtain an anti-interference received signal.

[0146] Specifically, the multi-channel spatial received signal is input into a spatial filtering process. Based on the spatial response relationship corresponding to the dynamic suppression pattern, the signal data in each received channel are weighted and combined using array weights. The spatial filtering process refers to the process of weighted synthesis of the received signal using the differences in array spatial response. By adjusting the amplitude ratio and phase relationship of the signals from different channels during the synthesis process, the signal component from the target direction maintains a strong response at the output, while the signal component from the predicted interference direction is effectively weakened during the synthesis process because its corresponding suppression region has a lower spatial gain.

[0147] The response weights for each spatial direction are determined based on the dynamic suppression pattern, and these relationships are then converted into weighting parameters for the multi-channel received signals. For received components from the target signal's spatial direction, maintaining a high response gain in the target direction allows the target signal components in multiple channels to be superimposed in phase after amplitude-phase compensation, thus improving the target signal reception quality. For received components from the predicted interference's spatial direction, the dynamic suppression pattern sets a lower response gain in the corresponding region, resulting in a weakening effect on the interference signal after spatial weighting and reducing the impact of strong interference signals on subsequent demodulation processing.

[0148] Through the above spatial filtering process, the target signal component and the interference signal component in the multi-channel spatial received signal are separated in the spatial dimension, which reduces the energy ratio corresponding to the predicted interference direction of the output signal and improves the spatial discrimination capability between the target communication signal and the interference signal.

[0149] The anti-interference received signal obtained in this embodiment retains the target communication information while reducing the interference impact in a strong interference environment, providing high-quality input for subsequent communication signal demodulation, identification, and stable transmission.

[0150] The signal anti-interference processing in this embodiment uses the aforementioned interference trend prediction results to drive the array spatial response adjustment, so that the anti-interference process changes from passive suppression based on the current interference state to active spatial suppression combined with future interference change trends, thereby improving the reception reliability of the shortwave communication system in dynamic strong interference environment.

[0151] Step S700: Track the communication quality changes of the anti-interference received signal and perform signal anti-interference optimization based on the interference trend prediction model and the prediction driving array weight iterative update.

[0152] This embodiment continuously tracks signal quality changes during communication based on the obtained anti-interference received signal, and iteratively optimizes the aforementioned interference trend prediction model and prediction-driven array weights according to the communication quality feedback results, enabling the anti-interference control process to adapt to the dynamically changing shortwave communication environment. The communication quality changes characterize the stability of the received signal after spatial filtering, including signal effectiveness, interference residue effects, and changes in receiving performance. By continuously acquiring communication quality changes, it determines whether the current prediction results and array control effects meet communication requirements.

[0153] Specifically, based on the changes in communication quality corresponding to the anti-interference received signal, the future interference state prediction results output by the interference trend prediction model and the prediction driving array weights are adjusted accordingly. When the communication quality deteriorates or the interference suppression effect decreases, the model input data is updated using the new received state information, enabling the interference trend prediction model to relearn the interference state evolution relationship based on the latest communication environment changes. At the same time, the array weights are re-optimized based on the updated interference trend prediction results, making the spatial response of the phased antenna array more consistent with the current interference distribution changes.

[0154] Through the above iterative update process, interference prediction, beam control, and reception quality feedback form a closed-loop optimization relationship, thereby continuously improving the anti-interference capability in a strong interference shortwave communication environment. This achieves the effect of improving the interference suppression capability and anti-interference stability in a strong interference communication environment, while ensuring reliable reception of target signals and stable operation of the communication link.

[0155] Example 2: Based on the same inventive concept as the shortwave received signal anti-interference optimization method in the strong interference communication scenario described in the foregoing examples, this invention provides a shortwave received signal anti-interference optimization system in the strong interference communication scenario. See [link to example]. Figure 2 The system includes:

[0156] The multi-channel signal acquisition unit 1 is used to acquire multi-channel spatial received signals in the shortwave communication environment in real time through a phased antenna array.

[0157] The interference feature sensing unit 2 is used to obtain real-time interference status information and target signal spatial orientation features by performing broadband spectrum sensing and spatial feature analysis on the multi-channel spatial received signal.

[0158] Interference state prediction unit 3 is used to take the real-time interference state information as the state prediction input, and predict the future interference state evolution based on the pre-built interference trend prediction model to obtain the interference trend prediction result.

[0159] The beam weight optimization unit 4 is used to perform prediction-driven beam weight optimization calculations by taking the interference trend prediction results and the spatial orientation characteristics of the target signal as array control constraints, so as to obtain the prediction-driven array weights of the phased antenna array.

[0160] The pattern reconstruction unit 5 is used to generate an array pattern based on the predicted driving array weights, and to dynamically reconstruct the suppression region in the array pattern according to the predicted interference spatial distribution information to obtain a dynamic suppression pattern.

[0161] The spatial filtering processing unit 6 is used to perform spatial filtering processing on the multi-channel spatial received signal using the dynamic suppression pattern to obtain an anti-interference received signal.

[0162] The anti-interference optimization iteration unit 7 is used to track the communication quality changes of the anti-interference received signal and perform signal anti-interference optimization based on the interference trend prediction model and the prediction driving array weight iterative update.

[0163] In one implementation, the interference state prediction unit 3 is further configured to:

[0164] By retrospectively analyzing historical interference status information and combining it with real-time interference status information, temporal correlation analysis of interference status is performed to obtain interference status evolution characteristics. Ionospheric change characteristics and multipath propagation characteristics of shortwave propagation process are introduced to characterize the propagation environment impact of the interference status evolution characteristics and obtain environmental coupled interference characteristics. The environmental coupled interference characteristics are used as model input features to construct the interference trend prediction model.

[0165] In one implementation, the interference state prediction unit 3 is further configured to:

[0166] A preset time sliding window is used to segment a continuous time period of historical interference state sequence from the historical interference state information; the real-time interference state information is then concatenated to the historical interference state sequence to construct an interference state association sequence, wherein the real-time interference state information includes real-time interference frequency characteristics, real-time interference spatial orientation characteristics, and real-time interference energy distribution characteristics; interference frequency feature sequence, interference spatial orientation feature sequence, and interference energy distribution feature sequence are extracted from the interference state association sequence, and state change law analysis is performed to obtain the interference frequency change law, spatial orientation change law, and interference energy change law, which constitute the interference state evolution characteristics.

[0167] In one implementation, the interference state prediction unit 3 is further configured to:

[0168] Based on the ionospheric variation characteristics, the propagation path influence analysis of the spatial orientation variation law and frequency variation law is performed to obtain the interference frequency drift characteristics and interference spatial drift characteristics; based on the multipath propagation characteristics, the propagation superposition influence analysis of the energy variation law is performed to obtain the interference energy fluctuation characteristics; the interference frequency drift characteristics, interference spatial drift characteristics and interference energy fluctuation characteristics are fused to obtain the environmental coupling interference characteristics.

[0169] In one implementation, the interference state prediction unit 3 is further configured to:

[0170] The environmental coupling interference features are constructed according to the time correlation to obtain the environmental coupling interference state sequence; the interference state evolution relationship is extracted based on the environmental coupling interference state sequence to establish the interference state mapping relationship between the real-time environmental coupling interference state and the future interference state changes; the interference trend prediction model is constructed based on the interference state mapping relationship.

[0171] In one implementation, the beam weight optimization unit 4 is further configured to:

[0172] The spatial distribution information of predicted interference is extracted from the predicted interference trend results as a spatial constraint for predicted interference; the direction-keeping constraint of the target signal is determined according to the spatial orientation characteristics of the target signal; a joint beam optimization constraint relationship is established based on the spatial constraint of predicted interference and the direction-keeping constraint of the target signal; the element amplitude and phase control parameters of the phased antenna array are optimized according to the joint beam optimization constraint relationship to obtain the predicted driving array weights.

[0173] In one implementation, the interference feature sensing unit 2 is further configured to:

[0174] The multi-channel spatial received signal is subjected to time-frequency transformation to obtain multi-channel time-frequency domain signal data; broadband spectrum scanning is performed on the multi-channel time-frequency domain signal data to obtain N spectral energy distribution information of N signal components; interference features are extracted under interference frequency band positioning based on the N spectral energy distribution information to obtain Q interference frequency component information; frequency state characterization is performed based on the Q interference frequency component information to obtain the real-time interference frequency characteristics.

[0175] In one implementation, the interference feature sensing unit 2 is further configured to:

[0176] Based on the spatial response differences of the phased antenna array elements, spatial feature analysis is performed on the multi-channel spatial received signal to obtain N spatial direction-of-arrival information and N spatial response intensity information corresponding to the N signal components. According to the real-time interference frequency characteristics, K interference signal components are selected from the N signal components, and the real-time interference spatial orientation characteristics are determined based on the K spatial direction-of-arrival information corresponding to the K interference signal components. According to the frequency characteristics of the K interference signal components, K interference spectrum energy distribution information is selected from the N spectrum energy distribution information. Based on the K interference spectrum energy distribution information, the K spatial direction-of-arrival information, and the K spatial response intensity information, spatial energy distribution analysis is performed to obtain the real-time interference energy distribution characteristics.

[0177] In one implementation, the interference feature sensing unit 2 is further configured to:

[0178] Spatial feature identification is performed on the target signal component among the N signal components, and the target signal arrival direction information is extracted from the N spatial arrival direction information as the spatial orientation feature of the target signal.

[0179] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0180] The foregoing description of specific exemplary embodiments of the invention is for illustrative and explanatory purposes. These descriptions are not intended to limit the invention to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of the invention, as well as various different choices and variations. The scope of the invention is intended to be defined by the claims and their equivalents.

Claims

1. An optimization method for shortwave received signal anti-interference in strong interference communication scenarios, characterized in that, include: Real-time acquisition of multi-channel spatial received signals in the shortwave communication environment via a phased antenna array; By performing broadband spectrum sensing and spatial feature analysis on the multi-channel spatial received signal, real-time interference status information and target signal spatial orientation features are obtained. The real-time interference state information is used as the state prediction input, and the future interference state evolution is predicted based on the pre-built interference trend prediction model to obtain the interference trend prediction result. Using the interference trend prediction results and the spatial orientation characteristics of the target signal as array control constraints, the prediction drive beam weight optimization calculation is performed to obtain the prediction drive array weight of the phased antenna array. An array pattern is generated based on the predicted driving array weights, and the suppression region in the array pattern is dynamically reconstructed based on the predicted interference spatial distribution information to obtain a dynamic suppression pattern. The multi-channel spatial received signal is spatially filtered using the dynamic suppression pattern to obtain an anti-interference received signal. Track the communication quality changes of the anti-interference received signal, and perform signal anti-interference optimization based on the interference trend prediction model and the prediction-driven array weight iterative update.

2. The method for optimizing shortwave received signal anti-interference in strong interference communication scenarios as described in claim 1, characterized in that, Using the real-time interference state information as input for state prediction, and based on a pre-built interference trend prediction model, the future interference state evolution is predicted to obtain the interference trend prediction results, including: By retrospectively analyzing historical interference state information and performing temporal correlation analysis, the evolution characteristics of interference states can be obtained. By introducing the ionospheric variation characteristics and multipath propagation characteristics of the shortwave propagation process, the influence of the propagation environment on the evolution characteristics of the interference state is characterized, and the environmental coupling interference characteristics are obtained. Based on the environmental coupling interference characteristics, an interference state change mapping relationship is established, and the interference trend prediction model is trained based on the interference state change mapping relationship. Segment historical interference state segments of a preset time length from historical interference state information, and concatenate them with the real-time interference state information in a time-related manner to form a predicted input interference state sequence. The interference trend prediction model predicts the future interference state evolution based on the predicted input interference state sequence, and obtains the interference trend prediction result. The real-time interference state information includes real-time interference frequency characteristics, real-time interference spatial orientation characteristics, and real-time interference energy distribution characteristics.

3. The method for optimizing shortwave received signal anti-interference in strong interference communication scenarios as described in claim 2, characterized in that, By retrospectively analyzing historical interference state information and performing temporal correlation analysis, the evolution characteristics of interference states can be obtained, including: A preset time sliding window is used to segment the historical interference state sequence into consecutive time periods from the historical interference state information; A time-dimensional correlation analysis is performed on the historical interference state sequence to extract the interference frequency feature sequence, interference spatial orientation feature sequence, and interference energy distribution feature sequence. The state change pattern is analyzed to obtain the interference frequency change pattern, spatial orientation change pattern, and interference energy change pattern, which constitute the interference state evolution characteristics.

4. The method for optimizing shortwave received signal anti-interference in strong interference communication scenarios as described in claim 3, characterized in that, By introducing ionospheric variation characteristics and multipath propagation characteristics of the shortwave propagation process, the propagation environment impact of the interference state evolution characteristics is characterized to obtain environmental coupling interference characteristics, including: Based on the ionospheric change characteristics, the influence of propagation path changes on the spatial orientation and frequency change patterns is analyzed to obtain interference frequency drift characteristics and interference spatial drift characteristics; Based on the multipath propagation characteristics, the propagation superposition effect analysis of the energy change law is performed to obtain the interference energy fluctuation characteristics; By integrating the interference frequency drift characteristics, interference spatial drift characteristics, and interference energy fluctuation characteristics, the environmental coupling interference characteristics are obtained.

5. The method for optimizing shortwave received signal anti-interference in strong interference communication scenarios as described in claim 4, characterized in that, Based on the environmental coupling interference characteristics, an interference state change mapping relationship is established, and the interference trend prediction model is trained based on the interference state change mapping relationship, including: Based on the temporal correlation, feature sequences are constructed from the environmental coupling interference features to obtain environmental coupling interference state sequences; Based on the environmental coupled interference state sequence, the interference state evolution relationship is extracted, and the interference state mapping relationship between the environmental coupled interference state change and the future interference state change is established. Based on the aforementioned interference state mapping relationship, the parameters of the time series prediction model are trained and optimized to construct the interference trend prediction model.

6. The method for optimizing shortwave received signal anti-interference in strong interference communication scenarios as described in claim 1, characterized in that, Using the interference trend prediction results and the spatial orientation characteristics of the target signal as array control constraints, predictive drive beam weight optimization calculations are performed to obtain the predictive drive array weights of the phased antenna array, including: The spatial distribution information of predicted interference is extracted from the interference trend prediction results and used as the spatial constraint for predicted interference. The target signal direction retention constraint is determined based on the spatial orientation characteristics of the target signal. Based on the predicted interference spatial constraints and the target signal direction preservation constraints, a joint beam optimization constraint relationship is established. Based on the joint beam optimization constraint relationship, the element amplitude and phase control parameters of the phased antenna array are optimized and calculated to obtain the predictive drive array weights.

7. The method for optimizing shortwave received signal anti-interference in strong interference communication scenarios as described in claim 3, characterized in that, Also includes: The multi-channel spatial received signal is subjected to time-frequency transformation to obtain multi-channel time-frequency domain signal data; By performing broadband spectrum scanning on the multi-channel time-frequency domain signal data, N spectral energy distribution information of N signal components is obtained; Based on the N spectral energy distribution information, interference features are extracted under interference frequency band positioning to obtain Q interference frequency component information. Frequency state characterization is performed based on the Q interference frequency component information to obtain the real-time interference frequency characteristics.

8. The method for optimizing shortwave received signal anti-interference in strong interference communication scenarios as described in claim 7, characterized in that, Also includes: Based on the spatial response differences of the phased antenna array elements, spatial feature analysis is performed on the multi-channel spatial received signal to obtain N spatial direction of arrival information and N spatial response intensity information corresponding to the N signal components; Based on the real-time interference frequency characteristics, K interference signal components are selected from the N signal components, and the real-time interference spatial orientation characteristics are determined based on the K spatial arrival direction information corresponding to the K interference signal components. Based on the frequency characteristics of the K interference signal components, K interference spectrum energy distribution information is obtained by filtering from the N spectrum energy distribution information. Based on the K interference spectrum energy distribution information, K spatial direction of arrival information, and K spatial response intensity information, spatial energy distribution analysis is performed to obtain the real-time interference energy distribution characteristics.

9. The method for optimizing shortwave received signal anti-interference in strong interference communication scenarios as described in claim 8, characterized in that, Spatial feature identification is performed on the target signal component among the N signal components, and the target signal arrival direction information is extracted from the N spatial arrival direction information as the spatial orientation feature of the target signal.

10. A shortwave receiving signal anti-interference optimization system for strong interference communication scenarios, characterized in that, For implementing the method steps of any one of claims 1 to 9, including: A multi-channel signal acquisition unit is used to acquire multi-channel spatial received signals in the shortwave communication environment in real time through a phased antenna array. The interference feature sensing unit is used to obtain real-time interference status information and target signal spatial orientation features by performing broadband spectrum sensing and spatial feature analysis on the multi-channel spatial received signal. The interference state prediction unit is used to take the real-time interference state information as the state prediction input, and predict the future interference state evolution based on the pre-built interference trend prediction model to obtain the interference trend prediction result. The beam weight optimization unit is used to perform prediction-driven beam weight optimization calculations by taking the interference trend prediction results and the spatial orientation characteristics of the target signal as array control constraints, so as to obtain the prediction-driven array weights of the phased antenna array. The pattern reconstruction unit is used to generate an array pattern based on the predicted driving array weights, and to dynamically reconstruct the suppression region in the array pattern according to the predicted interference spatial distribution information to obtain a dynamic suppression pattern. The spatial filtering processing unit is used to perform spatial filtering processing on the multi-channel spatial received signal using the dynamic suppression pattern to obtain an anti-interference received signal. An anti-interference optimization iteration unit is used to track the communication quality changes of the anti-interference received signal and perform signal anti-interference optimization based on the interference trend prediction model and the prediction driving array weights iteratively updated.

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