Intelligent target distribution method and system for collaborative interception

Abstract

The invention discloses an intelligent target distribution method and system for collaborative interception, particularly relates to the field of radar networking, is used for solving the problem of correction of phase noise accumulation and array error propagation, and comprises the following steps: establishing a high-dimensional node phase distribution model, determining a noise mode and extracting key features; in combination with calculation of a phase drift index and a signal distortion index and construction of a multi-dimensional noise matrix, fine quantization of node errors is realized; on the basis, the array surface information is corrected in real time by using a dynamic interpolation and phase offset correction technology, node cooperation is optimized in combination with an energy measurement algorithm, and the stability and precision of the array surface information are improved; and finally, through a multi-dimensional decision matrix, target features and node states are comprehensively analyzed, and intelligent allocation and optimization of resources are realized. Therefore, the observation precision and the cooperation efficiency of radar networking are remarkably improved, and reliable technical support is provided for high-precision target monitoring and resource optimization allocation in a complex environment.

Description

Technical Field

[0001] The present invention relates to the field of radar networking, and more specifically, to an intelligent target allocation method and system for collaborative interception. Background Art

[0002] In the distributed phased array radar networking scenario, multiple antenna units collaborate to perform search and tracking tasks over a large range, achieving wider coverage and finer angle measurement accuracy through mutual signal transmission and joint processing, which is suitable for intelligent target allocation solutions for collaborative interception. Large-scale networking has a better detection advantage when dealing with high-speed or stealth targets, but the phase noise between multiple nodes often accumulates in the synthetic array, and the array error expands accordingly, causing deviations in angle measurement and target judgment.

[0003] However, when high-density radar nodes jointly perform the intelligent target allocation task for collaborative interception, the local oscillator noise of some RF links is gradually amplified after transmission and superposition between multiple nodes, and the local array surface shows slight distortion and causes the main lobe or side lobe to shift, thereby weakening the accuracy of target positioning and threat assessment. This has a significant impact on the intelligent target allocation method and system for collaborative interception, and has become a technical difficulty that needs to be solved urgently. In order to solve the above-mentioned technical problems, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an intelligent target allocation method and system for collaborative interception, which defines the noise pattern and extracts key features by constructing a high-dimensional node phase distribution model; combines the calculation of phase drift index and signal distortion index, and the construction of a multi-dimensional noise matrix to achieve refined quantification of node errors; on this basis, uses dynamic interpolation and phase offset correction technology to perform real-time correction on array information, and combines an energy measurement algorithm to optimize node collaboration, thereby improving the stability and accuracy of array information; finally, through a multi-dimensional decision matrix, comprehensively analyzes target characteristics and node status, and realizes intelligent allocation and optimization of resources; thereby significantly improving the observation accuracy and collaborative efficiency of radar networking, and providing reliable technical support for high-precision target monitoring and resource optimization allocation in complex environments, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: An intelligent target allocation method for collaborative interception includes the following steps: S1: Real-time acquisition of RF link phase data of each radar node, combined with historical observation data, to build a high-dimensional node phase distribution model. By using a multi-objective clustering analysis algorithm, the phase data is divided into different noise pattern groups, the main phase noise source is located, and the key parameters affecting the array synthesis are screened out to form a correction feature vector. S2: Based on the real-time echo signal, the phase drift index and signal distortion index between nodes are calculated. The noise characteristic spectrum is extracted through joint analysis in the time domain and frequency domain, and a second-order noise matrix is ​​constructed to quantify the drift and distortion degree. The error type in the echo signal is comprehensively evaluated by combining these two indices through weighted correlation analysis. S3: Using the error evaluation results, the array error is corrected by dynamic interpolation and phase offset correction methods, and the local residual error is identified by combining the energy measurement algorithm to generate a real-time array optimization factor, and the phase adjustment parameters of each radar node are globally updated; S4: Analyze the target characteristics of the corrected observation data, combine its movement trajectory and threat level, use a multi-dimensional decision matrix to complete resource allocation, and generate an optimized interception strategy by establishing a dynamic priority allocation model and combining the resource status of each node with the target attributes.

[0006] In a preferred embodiment, step S1 includes the following contents: S1.1, before collecting phase data, select the following two limiting conditions: Only phase data with a signal-to-noise ratio greater than a low-quality standard value is collected, and only phase data within the radar beam scanning range within the rated area of ​​the main lobe is collected; S1.2, then collect the RF link phase data of each radar node in real time ,in Indicates the radar node number, Indicates time; for each node, the phase data in the main lobe area with a signal-to-noise ratio higher than the threshold is recorded, and static offset elimination is performed: by The phase data within the time is averaged to extract the phase noise component : ;in, represents the phase noise component, For Node Phase data of S1.3, after eliminating the static offset, multi-order derivative calculations are performed to extract dynamic characteristic parameters, which are defined as the dynamic offset rate : ;in, Represent the first-order, second-order and third-order derivatives, respectively, and are used to characterize linear, quadratic nonlinear and high-order nonlinear characteristics; generate a feature vector for each node ; S1.4, the eigenvectors of all nodes are combined into a feature matrix , and perform multi-objective cluster analysis to classify different noise pattern groups ,in Number the mode group; S1.5, based on the clustering results, construct a node phase distribution model , with the center vector of each mode group express: ;node The feature vector of is finally expressed as the model mapping result: ;in, For Node The noise pattern group number to which it belongs; by subtracting the pattern center vector, the corrected eigenvector of each node is obtained .

[0007] In a preferred embodiment, step S2 includes the following contents: S2.1, obtain real-time echo signal from each radar node ,in Indicates the node number, represents time; and the corrected eigenvector Correlate the echo signals in the same observation period Sampling is performed within and a synchronous mapping is established with the correction feature vector; S2.2, quantizes the dual variation of phase and amplitude with time and frequency, and defines the second-order noise matrix To characterize the nodes The noise distribution of the echo signal in multiple dimensions; the calculation process of the matrix elements is defined as follows: ;in, Representation Node Real-time echo signal; Indicates frequency variable, value range , The highest frequency required for analysis; Indicates the time offset, the value range is , is the maximum time shift for the second-order analysis; represents an imaginary unit; It is a mapping function defined based on the correction feature vector and the time offset, which is used to inject the noise pattern information into the second-order matrix.

[0008] In a preferred embodiment, step S2 further includes the following contents: S2.3, characterizes the maximum amplitude and instantaneous jump characteristics of phase change between nodes, and defines the phase drift index : ;in represents the imaginary part of a complex number. To search for nodes The maximum phase fluctuation at a given time shift and frequency.

[0009] In a preferred embodiment, step S2 further includes the following contents: S2.4, evaluate the combined effects of amplitude variation, bandwidth expansion, and spectrum distortion, and define the signal distortion index : ;in represents the modulus of the second-order noise matrix on the complex plane; S2.5, after completing the calculation of the phase drift index and the signal distortion index, combined with the determination of the node noise pattern group, the node A multidimensional exponential correlation function is used to comprehensively judge the error sources. Maps two indices to a single evaluation value : .

[0010] In a preferred embodiment, step S3 includes the following contents: S3.1, based on the assessed value , for each radar node Develop local interpolation strategy; define nodes Local interpolation basis functions Indicates that it is in the angular domain The correction trend within the range is expressed as: ;in, represents the interpolation sensitivity coefficient associated with the correction eigenvector, which is used to control the node Interpolation process for angle increment The intensity of the response; S3.2, to compensate for the phase mismatch of the node in the time domain, define the bias correction function , so that it comprehensively reflects the node The interaction of the phase shift index with the local interpolation basis function: ;in, is the phase gain coefficient; S3.3, perform energy measurement calculation on the node phase information after bias correction in the echo signal domain to detect residual error; define node The local energy measure function ,in Represents discrete moments in the observation period: .

[0011] In a preferred embodiment, step S3 further includes the following contents: S3.4, based on the interpolation correction and energy measurement results, generates real-time array optimization factors for each node , defined as follows: ;in, Representation Node At the moment The initial value of the energy measurement; after collecting the real-time array optimization factors of all nodes, the global optimization vector is constructed , and according to the node number Update the phase adjustment parameter table to maintain the uniformity within the network during the observation period; S3.5, when the global optimization vector shows a convergence trend after multiple rounds of calculations, that is, the correction effect of each node tends to a stable standard, it enters the maintenance state, monitors small changes and performs low-frequency interpolation updates based on the fine-tuning results of the local energy measurement; if the global optimization vector continues to increase abnormally above the standard at a certain node, it means that there is a re-accumulated mismatch in phase or amplitude at this node, and the bias correction function or local interpolation basis function needs to be deeply iterated again.

[0012] In a preferred embodiment, step S4 includes the following contents: S4.1, based on the corrected echo information, obtain the target in the observation period The position information within; each observation target is recorded as , which at the time The state is defined as , including two-dimensional or three-dimensional spatial coordinates and velocity components; based on the residual noise level after multi-frame echo fusion and phase correction, a multi-hypothesis tracking structure is used to Perform continuous estimation to obtain the target's motion trajectory; S4.2, using the threat level function for the extracted target trajectory information To comprehensively measure the goals potential impact on the intercept mission; S4.3, based on the existing node status and target characteristics, quantify the comprehensive benefits of resources; record the node The real-time optimization factor of the array is , in order to achieve resource adaptation between targets and nodes, a multi-dimensional decision matrix is ​​defined , the matrix elements Indicates the target Assign to Node The comprehensive benefits include: is the number of targets observed; is the total number of nodes.

[0013] In a preferred embodiment, step S4 further includes the following contents: S4.4, based on the multidimensional decision matrix, adopts a dynamic priority allocation model to allocate all Perform global processing, in each target In the corresponding row, select one or more optimal nodes Interception resources are allocated; once the optimal allocation result is determined, an interception strategy list is generated based on the corresponding result, including the priority interception node number corresponding to each target, as well as the initial time and path prediction parameters for starting the interception; finally, the interception strategy is sent to the resource scheduling and execution module through the command link to implement the final coordinated interception.

[0014] An intelligent target allocation system for collaborative interception, comprising: a data mapping module, an error analysis module, a front correction module, a feature analysis module and a resource allocation module; Data mapping module: collects the RF link phase data of each radar node in real time, and builds a high-dimensional node phase distribution model based on historical observation data. It divides the noise pattern groups through multi-target clustering analysis, extracts key parameters to form correction feature vectors, and outputs the correction feature vectors as input to the error analysis module. Error analysis module: Based on the correction feature vector and real-time echo signal, the phase drift index and signal distortion index are calculated, the noise characteristic spectrum is extracted through time-frequency joint analysis, the second-order noise matrix is ​​constructed to quantify the drift and distortion degree, and the error type in the echo signal is comprehensively evaluated. The output phase drift index and signal distortion index are transmitted to the array correction module; Face correction module: using the error analysis results, the face error is corrected by dynamic interpolation and phase offset correction methods, the energy measurement algorithm is combined to identify local residual errors, the real-time face optimization factor is generated, and the phase adjustment parameters of each radar node are globally updated. The corrected observation data and face optimization factor are transmitted to the feature analysis module; Feature analysis module: Analyze the target features of the observation data after the formation correction, extract the target's motion trajectory and key characteristics, combine the threat level analysis, provide target priority and status information for resource allocation, and pass the analysis results to the resource allocation module; Resource allocation module: Based on the target characteristics and threat level provided by the feature analysis module, combined with the resource status of each node and the dynamic priority allocation model, a multi-dimensional decision matrix is ​​used to optimize the resource allocation plan and generate the final interception strategy.

[0015] The technical effects and advantages of the intelligent target allocation method and system for collaborative interception of the present invention are as follows: The present invention proposes a complete technical solution to the problems of phase noise accumulation and array error propagation by efficiently integrating multi-source data of distributed phased array radar networking. By constructing a high-dimensional node phase distribution model, the noise pattern distribution is clarified and key features are extracted, laying the foundation for subsequent error analysis. The precise calculation of phase drift index and signal distortion index, combined with the construction of a multi-dimensional noise matrix, achieves fine quantification of node errors. On this basis, the array information is corrected in real time through dynamic interpolation and phase offset correction technology, and the collaborative relationship between nodes is optimized in combination with an energy measurement algorithm to ensure the stability and accuracy of the array information. Finally, by constructing a multi-dimensional decision matrix and comprehensively analyzing target characteristics and node status, the intelligent allocation and optimization of resources are achieved. This significantly improves the observation accuracy and collaborative efficiency of radar networking, and provides strong technical support for high-precision target monitoring and resource optimization allocation in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart of an intelligent target allocation method for collaborative interception according to the present invention; Figure 2 It is a structural schematic diagram of an intelligent target allocation system for collaborative interception according to the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: Figure 1 The present invention provides an intelligent target allocation method for collaborative interception, comprising the steps of: S1: Real-time acquisition of RF link phase data of each radar node, combined with historical observation data, to build a high-dimensional node phase distribution model. By using a multi-objective clustering analysis algorithm, the phase data is divided into different noise pattern groups, the main phase noise source is located, and the key parameters affecting the array synthesis are screened out to form a correction feature vector. S2: Based on the real-time echo signal, the phase drift index and signal distortion index between nodes are calculated. The noise characteristic spectrum is extracted through joint analysis in the time domain and frequency domain, and a second-order noise matrix is ​​constructed to quantify the drift and distortion degree. The error type in the echo signal is comprehensively evaluated by combining these two indices through weighted correlation analysis. S3: Using the error evaluation results, the array error is corrected by dynamic interpolation and phase offset correction methods, and the local residual error is identified by combining the energy measurement algorithm to generate a real-time array optimization factor, and the phase adjustment parameters of each radar node are globally updated; S4: Analyze the target characteristics of the corrected observation data, combine its movement trajectory and threat level, use a multi-dimensional decision matrix to complete resource allocation, and generate an optimized interception strategy by establishing a dynamic priority allocation model and combining the resource status of each node with the target attributes.

[0019] In distributed radar networking, the RF link phase noise of each node presents complex dynamic characteristics over time and environmental changes, which directly affects the stability of array synthesis and the accuracy of target detection. In the prior art, the data acquisition range is wide and the quality is uneven, which makes it difficult for noise analysis to focus on key features. At the same time, the mixing of data with low signal-to-noise ratio and non-main lobe areas further reduces the reliability of the model. This step strictly constrains the data acquisition conditions, uses the signal-to-noise ratio and beam range to filter out high-quality phase data, and constructs a node phase distribution model through dynamic feature extraction and cluster analysis, providing accurate input for subsequent error evaluation and correction.

[0020] Step S1 includes the following contents: S1.1, before phase data acquisition, the system needs to ensure that the acquisition conditions meet the predetermined quality standards to improve the effectiveness of subsequent analysis. Select the following two limiting conditions: Signal-to-noise ratio restriction: Only collect phase data with a signal-to-noise ratio greater than the low-quality standard value. By setting a higher low-quality standard value, it is ensured that the collected phase data mainly comes from high-quality signals, reducing the interference of noise on phase feature extraction.

[0021] Radar beam scan range limitation: Only phase data within the rated area of ​​the main lobe is collected. This limitation ensures that the data mainly reflects the phase characteristics of the main lobe area, excluding multipath interference and false echoes that may exist in the side lobe area.

[0022] S1.2, under the premise of meeting the above acquisition conditions, real-time acquisition of RF link phase data of each radar node ,in Indicates the radar node number, For each node, the phase data in the main lobe area with a signal-to-noise ratio higher than the threshold is recorded and preprocessed as follows: Static offset elimination: By adjusting the acquisition cycle The phase data within the time is averaged to extract the phase noise component : ;in, represents the phase noise component, For Node This step is to remove the static offset and extract the pure dynamic noise characteristics.

[0023] S1.3, after eliminating the static offset, multi-order derivative calculations are performed to extract dynamic characteristic parameters, which are defined as the dynamic offset rate : ;in, Represent the first, second and third order derivatives respectively, which are used to characterize linear, quadratic nonlinear and high-order nonlinear characteristics. Generate a feature vector for each node , to capture the multi-dimensional dynamic characteristics of noise.

[0024] S1.4, the eigenvectors of all nodes are combined into a feature matrix , and perform multi-objective cluster analysis to classify different noise pattern groups ,in The clustering process is based on the following dynamic distance function definition: ;in, is the weight factor of different order derivatives, set as , , , in order to enhance the influence of high-order derivative features on distance calculation. This weight setting reflects the importance of high-order derivatives in distinguishing noise features.

[0025] S1.5, based on the clustering results, construct a node phase distribution model , with the center vector of each mode group express: ;node The feature vector of is finally expressed as the model mapping result: ;in, For Node The noise pattern group number to which it belongs. By subtracting the pattern center vector, the corrected eigenvector of each node is obtained. , used for subsequent phase error evaluation and correction.

[0026] Step S1 ensures that the collected phase data is of high quality and representativeness by limiting the signal-to-noise ratio and the radar beam scanning range. Phase data that meets the conditions is collected in real time, and a high-dimensional node phase distribution model is constructed through static offset elimination, multi-order derivative feature extraction, and multi-target clustering analysis. This model not only accurately reflects the phase noise characteristics of each node, but also provides accurate parameter input for subsequent phase drift and error index calculations, array information correction, and intelligent target allocation decisions through feature vector correction processing. Through strict data acquisition condition limitations, this step effectively improves the system's ability to quantify and analyze noise distribution, laying a solid foundation for the efficient operation of the intelligent target allocation system.

[0027] In step S1, noise pattern analysis and key parameter screening are completed based on the correction feature vector, and the main characteristic distribution of each radar node under different noise patterns is clarified. In order to achieve accurate correction of the array error, it is necessary to further analyze the real-time echo information in depth, and quantitatively characterize the phase mismatch degree and echo distortion between each node by constructing the phase drift index and signal distortion index. Step S2 combines the real-time echo signal based on the correction feature vector, constructs a second-order noise matrix and performs multi-dimensional integration and mapping, and outputs key indicators that can comprehensively characterize the error source, providing the necessary error evaluation input for the next step (step S3).

[0028] Step S2 includes the following contents: S2.1, real-time echo acquisition and node data mapping: Get real-time echo signals from each radar node ,in Indicates the node number, Indicates time. To facilitate the correction of the feature vector of the node obtained in step S1 To correlate, the echo signals need to be observed in the same period of time. The sampling is performed within and a synchronous mapping is established with the correction feature vector. The meaning of synchronous mapping is that under the same time reference and node label, the echo data and the correction feature vector of the corresponding node are kept one-to-one corresponding, so that the noise pattern information of each node can be accurately used in the process of constructing the second-order noise matrix.

[0029] S2.2, second-order noise matrix construction: To quantify the dual variation of phase and amplitude with time and frequency, define the second-order noise matrix To characterize the nodes The noise distribution of the echo signal in multiple dimensions. The calculation process of the matrix elements is defined as follows: ;in, Representation Node Real-time echo signal; Indicates frequency variable, value range , The highest frequency required for analysis; Indicates the time offset, the value range is , is the maximum time shift for the second-order analysis; represents an imaginary unit; is a mapping function defined based on the correction feature vector and the time offset, which is used to inject the noise pattern information into the second-order matrix. The function can be defined as: ;in Represents the order calibration coefficient, which is used to amplify or attenuate the influence of different derivative terms on noise characterization; Representation Node In the correction feature vector A quantity.

[0030] S2.3, Phase drift index definition and calculation: In order to characterize the maximum amplitude and instantaneous jump characteristics of phase change between nodes, the phase drift index is defined as : ;in represents the imaginary part of a complex number. To search for nodes The maximum phase fluctuation value at a given time shift and frequency. The phase drift index reflects the phase mismatch degree of the node at the most extreme time-frequency combination. The larger the value, the more significant the echo phase shift.

[0031] S2.4, Signal distortion index definition and calculation: To evaluate the combined effects of amplitude variation, bandwidth expansion, and spectral distortion, the signal distortion index is defined : ;in Represents the modulus of the second-order noise matrix on the complex plane. This double integral is used to capture the energy concentration and distortion of the node within the complete time shift and frequency range. The higher the integral result, the more likely it is that the echo signal will be significantly distorted in terms of amplitude or bandwidth.

[0032] S2.5, Comprehensive evaluation of error types: After the calculation of the phase drift index and the signal distortion index is completed, the node can be A multidimensional exponential correlation function is used to comprehensively judge the error sources. Maps two indices to a single evaluation value : ; Evaluation value The larger the value, the more nodes The phase shift and signal distortion are serious, and the subsequent step (step S3) needs to be calibrated.

[0033] Step S2 first takes the correction feature vector output by step S1 as the basis, and maps the node feature vector to the second-order noise matrix of the real-time echo signal to obtain the basic data that describes the phase and amplitude distortion in multiple dimensions. Then, by defining the phase drift index and signal distortion index, combined with multiple integral and mapping function calculations, the error distribution of each node in the time-frequency range is fully reflected. Finally, the results of these two indexes are mapped to evaluation values ​​again, providing accurate and rich error information input for the composite array information correction and dynamic optimization link of the next step (step S3). Through this step, it is possible to determine which nodes have significant mismatches in both phase and amplitude, so as to focus on compensation and interpolation in the subsequent correction process, thereby ensuring the ultimate robustness and accuracy of the collaborative interception system.

[0034] In step S1, the correction feature vector is screened out through multi-target cluster analysis, and the noise pattern characteristics of each radar node are clarified; in step S2, the phase drift index and signal distortion index are defined by using the mapping relationship between the echo signal and the second-order noise matrix, and the evaluation value is further generated. Therefore, before entering step S3, there is a quantifiable error evaluation basis for judging the severity of the node in terms of phase mismatch and amplitude distortion. Step S3 aims to correct and optimize the existing evaluation results, correct the array error through dynamic interpolation and phase offset, and perform global updates in combination with energy measurement to ensure that the subsequent stages can perform target feature analysis and intelligent allocation decisions based on accurate array information.

[0035] Step S3 includes the following contents: S3.1, firstly, the evaluation value obtained in step S2 , for each radar node Develop a local interpolation strategy. Define nodes Local interpolation basis functions Indicates that it is in the angular domain The correction trend within the range is expressed as: ;in, represents the interpolation sensitivity coefficient associated with the correction eigenvector, which is used to control the node Interpolation process for angle increment The response strength of , reflecting the comprehensive trade-off between the node in terms of phase and signal distortion. The basis function is numerically adjustable and diverse. The larger the evaluation value, the wider the range of oscillation and cumulative impact of the interpolation function.

[0036] S3.2, in order to finely compensate the phase mismatch of the node in the time domain, define the bias correction function , so that it comprehensively reflects the node The interaction of the phase shift index with the local interpolation basis function: ;in, is the phase gain factor, which is used to balance the phase drift accumulated over time. The relationship between the interpolation basis function and the function returns the maximum value in the angular domain to ensure that the node It can also be fully compensated at the most unfavorable angle, thereby improving the array's adaptability to extreme mismatches.

[0037] S3.3, for the node phase information after offset correction, further energy measurement calculation is performed in the echo signal domain to detect the residual error. Define node The local energy measure function ,in Represents discrete moments in the observation period: ; This function will bias the correction amount Superimposed on echo signal The phase of the superimposed signal is observed to show the cumulative characteristics of the signal modulus over time. If the energy measurement function is If the global range shows a steady growth, it indicates that the correction effect is ideal; if it shows irregular fluctuations or local bulges, it means that the residual error is still significant. For the period of significant fluctuations, further interpolation iterations are performed in the local area to compensate for high-frequency distortion.

[0038] S3.4, based on the above interpolation correction and energy measurement results, a real-time array optimization factor is generated for each node , defined as follows: ;in, Representation Node At the moment The real-time front optimization factor combines the square term of the evaluation value with the growth characteristics of the energy measure after correction to characterize the global improvement effect of the node during the correction process.

[0039] After collecting the real-time optimization factors of all nodes, a global optimization vector is constructed. , and according to the node number Update the phase adjustment parameter table to maintain the uniformity within the network during the observation period. This global update process distributes the corrected bias and optimization factor to each node synchronously through the communication link, so that it has a more accurate initial phase configuration in the subsequent period.

[0040] S3.5, when the global optimization vector shows a convergence trend after multiple rounds of calculation, that is, the correction effect of each node tends to a stable standard, the system can enter the maintenance state, monitor small changes and perform low-frequency interpolation updates based on the fine-tuning results of the local energy measurement; if the global optimization vector continues to abnormally increase to a certain node, it means that the node has a re-accumulated mismatch in phase or amplitude, and the bias correction function or local interpolation basis function needs to be deeply iterated again. This mechanism forms a closed loop to ensure real-time tracking and correction capabilities for sudden noise or fast target motion.

[0041] Step S3 uses the phase drift index, signal distortion index and evaluation value of step S2 as input, defines interpolation and bias correction functions in the angular domain and time domain, identifies residual errors through energy measurement functions, and generates real-time array optimization factors to complete the global phase adjustment parameter update. This process plays a key role in distributed radar networking. Through the dynamic feedback mechanism, it ensures that the overall array continues to maintain high accuracy and stability in high-speed or unstable environments, and provides reliable and detailed array observation data support for subsequent target feature analysis and intelligent allocation decisions (step S4).

[0042] In step S1, based on multi-target clustering analysis and the construction of correction feature vectors, the noise patterns of each radar node are identified and screened; in step S2, the phase drift index, signal distortion index and evaluation value are defined, providing a quantitative basis for subsequent correction; in step S3, the interpolation and bias correction functions are combined with energy measurement to generate real-time array optimization factors, and the phase adjustment parameters of each node are synchronously updated to ensure the accuracy and stability of array synthesis. At this point, the system has a relatively accurate observation data foundation and can present the temporal and spatial distribution and motion characteristics of potential targets more realistically. Based on this, step S4 completes the target feature analysis and resource allocation decision-making through a multi-dimensional decision matrix, providing final instructions for the implementation of the interception strategy and the execution of the collaborative interception task.

[0043] Step S4 includes the following contents: S4.1, according to the corrected echo information output in step S3, obtain the target in the observation period Reliable position information within. Each observation target is recorded as , which at the time The state can be defined as , including 2D or 3D spatial coordinates and velocity components. Based on the residual noise level after multi-frame echo fusion and phase correction, a multi-hypothesis tracking structure is used to Perform continuous estimation to obtain the target's trajectory: ; Analyze the relative acceleration and direction change rate of each trajectory to determine whether the target is maneuvering at high speed or has stealth characteristics, laying the data foundation for the threat judgment below.

[0044] S4.2, define the threat level function for the extracted target trajectory information To comprehensively measure the goals The potential impact on the interception mission is calculated, for example, by: ;in, For the goal The characteristic gain coefficient is used to amplify the distance and maneuverability relationship between the target and the central protection interval; Indicates the target At the moment Position modulus relative to a reference origin (usually a command center or critical area); It is a velocity direction mapping function, which is used to measure the contribution of target acceleration and heading change to potential threats; The introduction of can improve the resolution of medium and long-distance targets and avoid excessive bias caused by linear amplification.

[0045] Through the above integration and mapping, the larger the value of the threat level function is, the higher the threat level of the corresponding target is.

[0046] S4.3, based on the existing node status and target characteristics, it is necessary to further quantify resource availability and comprehensive benefits. The real-time optimization factor of the array is The larger the value, the greater the node In the above correction, more accurate guarantee is obtained, and better interception potential is also achieved. In order to achieve resource adaptation between the target and the node, a multi-dimensional decision matrix is ​​defined , the matrix elements Indicates the target Assign to Node The overall benefit is, for example, calculated as follows: ;in, is the number of observable targets; is the total number of nodes; For the goal Threat level function value of ; Used to highlight the benefits of high-precision nodes after correction; As an additional feasibility evaluation function, comprehensively consider the node The current dispatchable resources (such as fuel balance) and the target At the terminal moment Spatial location If the node resources are insufficient or the target location is too far away, the function value will decrease.

[0047] S4.4, based on the multidimensional decision matrix, adopts a dynamic priority allocation model to allocate all For example, it can be considered as a global In the corresponding row, select one or more optimal nodes To ensure the overall optimization effect of allocation, the following objective functional is defined: : ;in, Represents an indicator variable. If the target Assign to Node ,but , otherwise it is 0. By searching in the feasible solution space and maximizing the objective functional value, the global optimal objective-node allocation solution can be obtained.

[0048] Once the optimal allocation result If confirmed, an interception strategy list is generated based on the result, including the priority interception node number corresponding to each target, as well as the initial time and path prediction parameters for starting the interception, etc. Finally, the interception strategy is sent to the resource scheduling and execution module through the command link to implement the final coordinated interception.

[0049] Step S4 completes the design of the entire process from target feature analysis to resource allocation decision by placing the corrected observation data and real-time array optimization factors output from step S3 into a multi-dimensional decision matrix, combining the target threat level function with the dynamic priority allocation model. This process effectively combines the high-precision observation capability of radar networking with the dynamic availability of interception resources, and forms a multi-adaptive allocation scheme for high-speed targets. Through the final optimal solution search, the system can reasonably allocate limited node resources, improve interception efficiency and success rate, and seamlessly connect the decision results to the back-end resource scheduling link through the command link, ensuring that the global needs for collaborative interception can be efficiently realized.

[0050] Embodiment 2: Figure 2 The invention provides an intelligent target allocation system for collaborative interception, comprising: a data mapping module, an error analysis module, a front correction module, a feature analysis module and a resource allocation module; Data mapping module: collects the RF link phase data of each radar node in real time, and builds a high-dimensional node phase distribution model based on historical observation data. It divides the noise pattern groups through multi-target clustering analysis, extracts key parameters to form correction feature vectors, and outputs the correction feature vectors as input to the error analysis module. Error analysis module: Based on the correction feature vector and real-time echo signal, the phase drift index and signal distortion index are calculated, the noise characteristic spectrum is extracted through time-frequency joint analysis, the second-order noise matrix is ​​constructed to quantify the drift and distortion degree, and the error type in the echo signal is comprehensively evaluated. The output phase drift index and signal distortion index are transmitted to the array correction module; Face correction module: using the error analysis results, the face error is corrected by dynamic interpolation and phase offset correction methods, the energy measurement algorithm is combined to identify local residual errors, the real-time face optimization factor is generated, and the phase adjustment parameters of each radar node are globally updated. The corrected observation data and face optimization factor are transmitted to the feature analysis module; Feature analysis module: Analyze the target features of the observation data after the formation correction, extract the target's motion trajectory and key characteristics, combine the threat level analysis, provide target priority and status information for resource allocation, and pass the analysis results to the resource allocation module; Resource allocation module: Based on the target characteristics and threat level provided by the feature analysis module, combined with the resource status of each node and the dynamic priority allocation model, a multi-dimensional decision matrix is ​​used to optimize the resource allocation plan and generate the final interception strategy.

[0051] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0052] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0053] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0054] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An intelligent target allocation method for collaborative interception, characterized in that: Includes steps: S1: Real-time acquisition of RF link phase data of each radar node, combined with historical observation data, to build a high-dimensional node phase distribution model. By using a multi-objective clustering analysis algorithm, the phase data is divided into different noise pattern groups, the main phase noise source is located, and the key parameters affecting the array synthesis are screened out to form a correction feature vector. S2: Based on the real-time echo signal, the phase drift index and signal distortion index between nodes are calculated. The noise characteristic spectrum is extracted through joint analysis in the time domain and frequency domain, and a second-order noise matrix is ​​constructed to quantify the drift and distortion degree. The error type in the echo signal is comprehensively evaluated by combining these two indices through weighted correlation analysis. S3: Using the error evaluation results, the array error is corrected by dynamic interpolation and phase offset correction methods, and the local residual error is identified by combining the energy measurement algorithm to generate a real-time array optimization factor, and the phase adjustment parameters of each radar node are globally updated; S4: Analyze the target characteristics of the corrected observation data, combine its movement trajectory and threat level, use a multi-dimensional decision matrix to complete resource allocation, and generate an optimized interception strategy by establishing a dynamic priority allocation model and combining the resource status of each node with the target attributes.

2. The intelligent target allocation method for collaborative interception according to claim 1 is characterized by: Step S1 includes the following contents: S1.1, before collecting phase data, select the following two limiting conditions: Only phase data with a signal-to-noise ratio greater than a low-quality standard value is collected, and only phase data within the radar beam scanning range within the rated area of ​​the main lobe is collected; S1.2, then collect the RF link phase data of each radar node in real time ,in Indicates the radar node number, Indicates time; for each node, the phase data in the main lobe area with a signal-to-noise ratio higher than the threshold is recorded, and static offset elimination is performed: by The phase data within the time is averaged to extract the phase noise component : ;in, represents the phase noise component, For Node Phase data of S1.3, after eliminating the static offset, multi-order derivative calculations are performed to extract dynamic characteristic parameters, which are defined as the dynamic offset rate : ;in, Represent the first-order, second-order and third-order derivatives, respectively, and are used to characterize linear, quadratic nonlinear and high-order nonlinear characteristics; generate a feature vector for each node ; S1.4, the eigenvectors of all nodes are combined into a feature matrix , and perform multi-objective cluster analysis to classify different noise pattern groups ,in Number the mode group; S1.5, based on the clustering results, construct a node phase distribution model , with the center vector of each mode group express: ;node The feature vector of is finally expressed as the model mapping result: ;in, For Node The noise pattern group number to which it belongs; by subtracting the pattern center vector, the corrected eigenvector of each node is obtained .

3. The intelligent target allocation method for collaborative interception according to claim 2 is characterized in that: Step S2 includes the following contents: S2.1, obtain real-time echo signal from each radar node ,in Indicates the node number, represents time; and the corrected eigenvector Correlate the echo signals in the same observation period Sampling is performed within and a synchronous mapping is established with the correction feature vector; S2.2, quantizes the dual variation of phase and amplitude with time and frequency, and defines the second-order noise matrix To characterize the nodes The noise distribution of the echo signal in multiple dimensions; the calculation process of the matrix elements is defined as follows: ;in, Representation Node Real-time echo signal; Indicates frequency variable, value range , The highest frequency required for analysis; Indicates the time offset, the value range is , is the maximum time shift for the second-order analysis; represents an imaginary unit; It is a mapping function defined based on the correction feature vector and the time offset, which is used to inject the noise pattern information into the second-order matrix.

4. The intelligent target allocation method for collaborative interception according to claim 3 is characterized by: Step S2 also includes the following contents: S2.3, characterizes the maximum amplitude and instantaneous jump characteristics of phase change between nodes, and defines the phase drift index : ;in represents the imaginary part of a complex number. To search for nodes The maximum phase fluctuation at a given time shift and frequency.

5. The intelligent target allocation method for collaborative interception according to claim 4 is characterized in that: Step S2 also includes the following contents: S2.4, evaluate the combined effects of amplitude variation, bandwidth expansion and spectrum distortion, and define the signal distortion index : ;in represents the modulus of the second-order noise matrix on the complex plane; S2.5, after completing the calculation of the phase drift index and the signal distortion index, combined with the determination of the node noise pattern group, the node A multidimensional exponential correlation function is used to comprehensively judge the error sources. Maps two indices to a single evaluation value : .

6. The intelligent target allocation method for collaborative interception according to claim 5 is characterized by: Step S3 includes the following contents: S3.1, based on the assessed value , for each radar node Develop local interpolation strategy; define nodes Local interpolation basis functions Indicates that it is in the angular domain The correction trend within the range is expressed as: ;in, represents the interpolation sensitivity coefficient associated with the correction eigenvector, which is used to control the node Interpolation process for angle increment The intensity of the response; S3.2, to compensate for the phase mismatch of the node in the time domain, define the bias correction function , so that it comprehensively reflects the node The interaction of the phase shift index with the local interpolation basis function: ;in, is the phase gain coefficient; S3.3, perform energy measurement calculation on the node phase information after bias correction in the echo signal domain to detect residual error; define node The local energy measure function ,in Represents discrete moments in the observation period: .

7. The intelligent target allocation method for collaborative interception according to claim 6 is characterized by: Step S3 also includes the following: S3.4, based on the interpolation correction and energy measurement results, generates real-time array optimization factors for each node , defined as follows: ;in, Representation Node At the moment The initial value of the energy measurement; after collecting the real-time array optimization factors of all nodes, the global optimization vector is constructed , and according to the node number Update the phase adjustment parameter table to maintain the uniformity within the network during the observation period; S3.5, when the global optimization vector shows a convergence trend after multiple rounds of calculations, that is, the correction effect of each node tends to a stable standard, it enters the maintenance state, monitors small changes and performs low-frequency interpolation updates based on the fine-tuning results of the local energy measurement; if the global optimization vector continues to increase abnormally above the standard at a certain node, it means that there is a re-accumulated mismatch in phase or amplitude at this node, and the bias correction function or local interpolation basis function needs to be deeply iterated again.

8. The intelligent target allocation method for collaborative interception according to claim 7 is characterized by: Step S4 includes the following contents: S4.1, based on the corrected echo information, obtain the target in the observation period The position information within; each observation target is recorded as , which at the time The state is defined as , including two-dimensional or three-dimensional spatial coordinates and velocity components; Based on the residual noise level after multi-frame echo fusion and phase correction, a multi-hypothesis tracking structure is used to Perform continuous estimation to obtain the target's motion trajectory; S4.2, using the threat level function for the extracted target trajectory information To comprehensively measure the goals potential impact on the intercept mission; S4.3, based on the existing node status and target characteristics, quantify the comprehensive benefits of resources; record the node The real-time optimization factor of the array is , in order to achieve resource adaptation between targets and nodes, a multi-dimensional decision matrix is ​​defined , the matrix elements Indicates the target Assign to Node The comprehensive benefits include: is the number of targets observed; is the total number of nodes.

9. The intelligent target allocation method for collaborative interception according to claim 8 is characterized by: Step S4 also includes the following contents: S4.4, based on the multidimensional decision matrix, adopts a dynamic priority allocation model to allocate all Perform global processing, in each target In the corresponding row, select one or more optimal nodes Perform interception resource allocation; Once the optimal allocation result is determined, an interception strategy list is generated based on the corresponding result, including the priority interception node number corresponding to each target, as well as the initial time and path prediction parameters for starting the interception; finally, the interception strategy is sent to the resource scheduling and execution module through the command link to implement the final coordinated interception.

10. An intelligent target allocation system for collaborative interception, used to implement an intelligent target allocation method for collaborative interception according to any one of claims 1 to 9, characterized in that: include: Data mapping module, error analysis module, array correction module, feature analysis module and resource allocation module; Data mapping module: collects the RF link phase data of each radar node in real time, and builds a high-dimensional node phase distribution model based on historical observation data. It divides the noise pattern groups through multi-objective clustering analysis, extracts key parameters to form correction feature vectors, and outputs the correction feature vectors as input to the error analysis module. Error analysis module: Based on the correction feature vector and real-time echo signal, the phase drift index and signal distortion index are calculated, the noise characteristic spectrum is extracted through time-frequency joint analysis, the second-order noise matrix is ​​constructed to quantify the drift and distortion degree, and the error type in the echo signal is comprehensively evaluated. The output phase drift index and signal distortion index are transmitted to the array correction module; Face correction module: using the error analysis results, the face error is corrected by dynamic interpolation and phase offset correction methods, the energy measurement algorithm is combined to identify local residual errors, the real-time face optimization factor is generated, and the phase adjustment parameters of each radar node are globally updated. The corrected observation data and face optimization factor are transmitted to the feature analysis module; Feature analysis module: Analyze the target features of the observation data after the formation correction, extract the target's motion trajectory and key characteristics, combine the threat level analysis, provide target priority and status information for resource allocation, and pass the analysis results to the resource allocation module; Resource allocation module: Based on the target characteristics and threat level provided by the feature analysis module, combined with the resource status of each node and the dynamic priority allocation model, a multi-dimensional decision matrix is ​​used to optimize the resource allocation plan and generate the final interception strategy.

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