A method for cooperative detection of air-sea distributed anti-jamming passive radar

Abstract

The application discloses a kind of based on air-sea distributed anti-interference passive radar cooperative detection method, constructs air-sea distributed radar cooperative detection network, including air multi-illumination source network, receiving network and synchronization network;Initial topological configuration design provides the infrastructure of air-sea distributed radar cooperative detection network;Adopt beam forming weight optimization to enhance the signal quality of detection network;Using node weighted selection strategy ensures the reasonable allocation of detection network resources;Based on topology optimization and dynamic deployment is to further optimize the adjustment of entire detection network, improve the anti-interference performance of detection network.The application not only improves the signal quality and anti-interference performance of radar system in multi-source, multi-path environment, but also enhances the expansibility and real-time performance of system in large-scale complex task, provides strong support for stable operation of radar system in diversified task environment.

Description

Technical Field

[0001] This invention belongs to the field of radar, specifically relating to a method for cooperative detection based on air-sea distributed anti-jamming passive radar. Background Technology

[0002] Current research in the radar field mainly focuses on single-source multiple-receiver (SPRN) systems. The main characteristic of SPRNs is that they use a single transmitter and multiple receivers for detection. While this technology performs well in some applications, its detection capabilities are often limited in complex environments. Distributed multi-source radar systems typically consist of multiple radar sites that can not only cooperate with each other but also dynamically adjust their operating modes as needed. They possess advantages such as strong anti-jamming capabilities, strong resistance to anti-radiation missiles, strong anti-stealth capabilities, and strong anti-low-altitude penetration capabilities. With continuous technological advancements, this field is expected to play an increasingly important role in military defense, aerospace, and intelligent transportation. However, it also faces many technical challenges, requiring continuous efforts through algorithmic innovation, hardware support, and system optimization to overcome existing limitations.

[0003] In distributed multi-source cooperative radar systems, the selection and optimization of radar illumination sources are key factors in improving system performance, detection accuracy, anti-jamming capabilities, and cost reduction. Correct selection of illumination sources and optimized resource allocation not only affect system efficiency but also determine the system's adaptability and flexibility in complex environments. This research focuses on multi-source distributed radar detection systems. Through multi-source optimization and evaluation, it establishes a high-precision, highly flexible distributed radar detection network, and breaks through the detection algorithm for fusion of cooperative and non-cooperative source signals, further improving detection accuracy and system anti-jamming performance. Summary of the Invention

[0004] Purpose of the invention: This invention proposes a method for cooperative detection based on air-sea distributed anti-jamming passive radar. Targeting distributed radar detection systems with multiple transmitters and receivers, it establishes a high-precision and highly flexible distributed radar detection network through multi-source optimization and evaluation. It also breaks through the detection algorithm of signal fusion between cooperative and non-cooperative sources, further improving detection accuracy and system anti-jamming performance.

[0005] Technical solution: The method for cooperative detection based on air-sea distributed anti-jamming passive radar according to the present invention specifically includes:

[0006] Construct an air-sea distributed radar cooperative detection network, including an airborne multi-source illumination network, a receiving network, and a synchronization network;

[0007] The initial topology design provides the basic architecture for a distributed air-sea radar cooperative detection network;

[0008] Beamforming weights are used to optimize and enhance the signal quality of the detection network;

[0009] A node weighted selection strategy is adopted to ensure the rational allocation of probe network resources;

[0010] Topology optimization and dynamic deployment further optimize and adjust the entire detection network to improve its anti-interference performance.

[0011] Furthermore, the airborne multi-source illumination network includes multiple illumination sources; the illumination sources provide multi-angle and multi-path signal illumination, and the transmitter nodes are flexibly selected and configured according to mission requirements. Through topology optimization and resource allocation, the optimal arrangement of multiple transmitters is achieved.

[0012] Furthermore, the receiving network includes multiple receivers responsible for signal reception and processing, signal fusion and target tracking, anti-interference and signal gain. The distribution of multiple receiving sources is optimized through topology configuration, which, together with the illumination source network, enhances the detection range and target recognition capability of the detection network.

[0013] The initial topology design implementation process is further described below:

[0014] The initial layout of the multi-irradiation source nodes is determined based on specific mission requirements; the position of each node is initially set based on the distribution of the mission target area and the channel environment.

[0015] Consider the overall coverage of the detection network, the directional properties of the main lobe, and the interference avoidance capability;

[0016] Set up multiple signal paths and backup nodes to provide redundancy backup in case of node failure or channel fading;

[0017] By monitoring the status of nodes and changes in the environment, the probe network generates feedback information, which is used as input for subsequent topology optimization, ensuring that the probe network can automatically adapt to and adjust the network topology in a constantly changing environment.

[0018] Further feedback information includes:

[0019] Detection performance evaluation for each node: detection range, signal strength;

[0020] Network coverage and redundancy design: Are there any blind spots or weak signal areas?

[0021] Environmental impact assessment results: the impact of interference sources and changes in target detection accuracy.

[0022] Furthermore, the weight optimization process for the adaptive beamforming is as follows:

[0023] To maximize the signal gain in the target direction and minimize sidelobe interference, beamforming pattern optimization is achieved by solving the following optimization objective:

[0024] max|w H a(θ0)| 2

[0025]

[0026] Where w is the weight vector, a(θ) is the steering vector of angle θ, and γ is the limit value of the sidelobe level;

[0027] An iterative optimization algorithm is used to dynamically adjust the weight vector, maximizing the main lobe gain while suppressing side lobe interference.

[0028] Furthermore, the implementation process of the node weighted selection strategy is as follows:

[0029] The detection network consists of N nodes, where the contribution of each node to the target direction is represented by the weight ω, and the interference generated by the detection network is represented by I. i express;

[0030] While ensuring the target direction gain G target Under the premise of minimizing the total power consumption and interference of unnecessary nodes:

[0031]

[0032] Among them, P i Let α represent the power of the i-th node. i and β i It is an adjustment coefficient used to balance the overall contribution of power consumption and interference effects to the detection network; G i This represents the signal gain of the i-th node;

[0033] Set each node as a binary selection variable x i The value can be 0 or 1, where x i =1 indicates that the node is enabled, x i =0 indicates that the node is disabled; the optimization objective can be reformulated as:

[0034]

[0035] Furthermore, the implementation process of the topology optimization and dynamic node deployment is as follows:

[0036] When the task scenario changes, the node deployment can be reconfigured by moving nodes or activating backup nodes;

[0037] An adaptive topology control algorithm is used to fine-tune the spatial positions of nodes, thereby adjusting the network topology to cope with environmental changes.

[0038] By utilizing multipath redundancy and alternative node configuration, the detection network can maintain good main lobe direction gain and anti-interference capability even if some nodes fail.

[0039] Furthermore, the synchronization network ensures time and frequency synchronization between the airborne multi-source illumination network and the receiving network; it uses a time difference measurement method to measure the time delay of signal transmission and adjusts the compensation strategy according to changes in link distance or path; and it introduces errors through a delay correction circuit to achieve dynamic compensation and maintain the synchronization of the slave station's time base with the master station.

[0040] Furthermore, the task requirements include coverage area, target direction, and location of interference sources.

[0041] Beneficial Effects: Compared with existing technologies, the present invention offers the following advantages: By dynamically adjusting the power allocation of the illumination source and the node activation status, the present invention prioritizes high-contribution, low-interference nodes for resource acquisition, reducing unnecessary power consumption and interference sources, and significantly enhancing the system's interference suppression capability. The weighted beamforming based on the present invention concentrates resources on key nodes, effectively improving the signal gain in the target direction while suppressing sidelobe interference, ensuring the radar system's detection accuracy in complex environments. Furthermore, through adaptive deployment of the topology configuration, the system can flexibly adjust according to different environmental conditions, thus maintaining efficient anti-interference capability when the distribution of interference sources changes, enhancing the system's environmental adaptability. Multi-path redundancy configuration further improves the system's robustness under harsh conditions, enabling the system to continue operating normally even when nodes fail or channels change. The present invention not only improves the signal quality and anti-interference performance of the radar system in multi-source, multi-path environments but also enhances the system's scalability and real-time performance in large-scale complex tasks, providing strong support for the stable operation of the radar system in diverse mission environments. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of a distributed radar collaborative detection network for air and sea. Detailed Implementation

[0043] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0044] This invention proposes a method for cooperative detection based on air-sea distributed anti-jamming passive radar, the specific implementation process of which is as follows:

[0045] Build as Figure 1 The air-sea distributed radar cooperative detection network shown includes an airborne multi-source illumination network (UAV early warning network), a receiving network, and a synchronization network.

[0046] Airborne multi-source illumination network: includes multiple illumination sources (usually radar signal transmitters); the illumination sources provide multi-angle, multi-path signal illumination, and the transmitter nodes can be flexibly selected and configured according to mission requirements. Through topology optimization and resource allocation, the optimal arrangement of multiple transmitters can be achieved.

[0047] The receiving network consists of multiple receivers (usually radar receivers) responsible for signal reception and processing, signal fusion and target tracking, anti-jamming and signal gain. The distribution of multiple receiver sources is optimized through topology configuration, and together with the illumination source network, it improves the detection range and target recognition capability of the radar system.

[0048] Synchronization network: Used to ensure time and frequency synchronization between multiple illumination sources and receiving sources.

[0049] In a detection network, the selection of illumination sources and receivers, as well as resource allocation, are crucial. Transmitting and receiving nodes in the network need to be adjusted based on real-time requirements, with node selection and topology optimization to ensure optimal configuration. Adaptive beamforming further enhances the system's robustness and anti-interference performance. The entire detection network is essentially a complete optimization loop. Initial topology design provides the basic architecture, beamforming weight optimization enhances signal quality, node weighting strategies ensure reasonable resource allocation, and topology optimization and dynamic deployment further refine the entire system. Through this loop, the system can continuously improve its performance, flexibility, and adaptability, maintaining high-efficiency detection capabilities under changing environments and complex mission requirements.

[0050] (1) Initial topology design.

[0051] The initial layout of the multi-irradiation source nodes is determined based on specific mission requirements (such as coverage area, target direction, and location of interference sources); the position of each node is initially set based on the distribution of the mission target area and the channel environment.

[0052] The initial layout should consider the overall system coverage, main lobe directional orientation, and interference avoidance capabilities;

[0053] Multiple signal paths and backup nodes are set up to provide redundancy in case of node failure or channel fading.

[0054] By monitoring the status of nodes and changes in the environment, the system will generate feedback information, mainly including the following aspects:

[0055] ① Evaluation of the detection performance of each node (e.g., detection range, signal strength, etc.).

[0056] ② Network coverage and redundancy design (whether there are blind spots or weak signal areas).

[0057] ③ The results of environmental impact assessments (such as the impact of interference sources, changes in target detection accuracy, etc.). This feedback information will be used as input for subsequent optimization to ensure that the system can automatically adapt and adjust the network topology in a constantly changing environment.

[0058] (2) Weight optimization of beamforming.

[0059] Optimization objective: Maximize signal gain in the target direction and minimize sidelobe interference. Beamforming pattern optimization is achieved by solving the following optimization objective:

[0060] max|w H a(θ0)| 2

[0061]

[0062] Where w is the weight vector, a(θ) is the steering vector of angle θ, and γ is the limit value of the sidelobe level.

[0063] Adaptive adjustment: The weight vector is dynamically adjusted using iterative optimization algorithms (such as convex optimization, particle swarm optimization, etc.) to maximize the main lobe gain while suppressing side lobe interference.

[0064] Algorithm implementation: Frequency offset and weight adjustment experiments are conducted in a simulation environment to obtain the optimal weight configuration scheme that meets the interference suppression conditions.

[0065] (3) Node weighted selection strategy.

[0066] Assume the system contains N nodes, where the contribution of each node to the target direction is represented by weight ω, and the disturbance it generates to the system is represented by I. i express.

[0067] While ensuring the target direction gain G target Under the premise of minimizing the total power consumption and interference of unnecessary nodes:

[0068]

[0069] Where: P i Let α represent the power of the i-th node. i and β i It is an adjustment coefficient used to balance the overall contribution of power consumption and disturbance effects to the system. i This represents the interference of the i-th node on the system. G i This represents the signal gain of the i-th node.

[0070] Set each node as a binary selection variable x i (Values ​​can be 0 or 1), where x i =1 indicates that the node is enabled, xi =0 indicates that the node is disabled. The optimization objective can be reformulated as:

[0071]

[0072] (4) Topology optimization and dynamic node deployment.

[0073] Dynamic deployment: When the task scenario changes (e.g., from an open field to a densely built-up area), the deployment of nodes is reconfigured. Nodes can be moved or standby nodes activated to adapt to the new environment and maintain system performance.

[0074] Adaptive topology adjustment: Based on the information recorded by the system, nodes are re-detected and feedback is received, and the spatial positions of nodes are fine-tuned. Feedback is used to adjust the deployment, and the network topology is adjusted to cope with environmental changes.

[0075] Robustness assurance: By utilizing multi-path redundancy and alternative node configuration, the system can still maintain good main lobe direction gain and anti-interference capability even if some nodes fail.

[0076] This technical solution can be effectively applied to the adaptive resource optimization deployment of multi-source illumination systems, providing stable and efficient signal coverage and interference suppression capabilities under complex channel environments and task requirements.

[0077] The synchronization network employs a master-slave distributed radio frequency transmission scheme to achieve high-precision time synchronization, significantly enhancing the system's autonomy and stability. It is particularly suitable for scenarios where GPS timing is limited or unstable. Compared to traditional GPS timing methods, this scheme avoids dependence on GPS signals through master-slave coordination and reduces synchronization errors caused by the ambiguity of wireless link phase pulses.

[0078] In the detection network, the rising edge phase difference of the signals transmitted by each slave station needs to be controlled within 5ns. This is achieved by precisely controlling the phase-locked local oscillator of each slave station. After receiving the clock signal from the master station, the slave station aligns its local oscillator with the master station's signal phase through a phase-locked loop (PLL), thus ensuring that the rising edges of all slave station signals are strictly synchronized. A 5MHz frequency signal is used as the slave station's time base. After receiving the 5MHz clock signal from the master station, the slave station uses a phase comparator for real-time error correction. This frequency base is locked to the master station's signal through a phase-locked loop, and the relative error can reach the sub-nanosecond level, effectively eliminating time base deviations on the link and achieving high-precision synchronization.

[0079] After receiving the master station's reference clock, each slave station performs precise measurement and compensation for signal transmission delay. Based on the calculated delay, high-precision synchronization between slave stations is maintained through the following steps: 1) Accurately measure the signal transmission time delay using a time difference measurement system (such as a Time-to-Digital Converter), and adjust the compensation strategy according to changes in link distance or path. 2) Through a delay correction circuit, the error is introduced into the control module to achieve dynamic compensation, thereby maintaining synchronization between the slave station's time base and the master station.

[0080] The above description provides a detailed account of the training data augmentation method for a traceability link recovery model based on unlabeled data, as described in this invention. However, it is clear that the specific implementation of this invention is not limited thereto. For those skilled in the art, various obvious modifications made to this invention without departing from the spirit and scope of the claims are within the protection scope of this invention.

Claims

1. A method for cooperative detection based on air-sea distributed anti-jamming passive radar, characterized in that, The implementation process is as follows: Construct an air-sea distributed radar cooperative detection network, including an airborne multi-source illumination network, a receiving network, and a synchronization network; The initial topology design provides the basic architecture for a distributed air-sea radar cooperative detection network; Beamforming weights are used to optimize and enhance the signal quality of the detection network; A node weighted selection strategy is adopted to ensure the rational allocation of probe network resources; Topology optimization and dynamic deployment further optimize and adjust the entire detection network to improve its anti-interference performance. The weight optimization process for the adaptive beamforming is as follows: To maximize the signal gain in the target direction and minimize sidelobe interference, beamforming pattern optimization is achieved by solving the following optimization objective: max|w H a(θ0)| 2 Where w is the weight vector, a(θ) is the steering vector of angle θ, and γ is the limit value of the sidelobe level; The weight vector is dynamically adjusted using an iterative optimization algorithm to maximize the main lobe gain while suppressing side lobe interference. The implementation process of the node weighted selection strategy is as follows: The detection network consists of N nodes, and the contribution of each node to the target direction is represented by a weight ω. i This indicates that interference generated by the detection network is represented by I. i express; While ensuring the target direction gain G target Under the premise of minimizing the total power consumption and interference of unnecessary nodes: Among them, P i Let α represent the power of the i-th node. i and β i It is an adjustment coefficient used to balance the overall contribution of power consumption and interference effects to the detection network; G i This represents the signal gain of the i-th node; Set each node as a binary selection variable x i The value can be 0 or 1, where x i =1 indicates that the node is enabled, x i =0 indicates that the node is disabled; the optimization objective can be reformulated as:

2. The method for cooperative detection based on air-sea distributed anti-jamming passive radar according to claim 1, characterized in that, The aerial multi-source illumination network includes multiple illumination sources; the illumination sources provide signal illumination from multiple angles and paths, and the transmitter nodes can be flexibly selected and configured according to mission requirements. Through topology optimization and resource allocation, the optimal arrangement of multiple transmitters can be achieved.

3. The method for cooperative detection based on air-sea distributed anti-jamming passive radar according to claim 1, characterized in that, The receiving network includes multiple receivers responsible for signal reception and processing, signal fusion and target tracking, anti-interference and signal gain. The distribution of multiple receiving sources is optimized through topology configuration, which, together with the illumination source network, improves the detection range and target recognition capability of the detection network.

4. The method for cooperative detection based on air-sea distributed anti-jamming passive radar according to claim 1, characterized in that, The initial topology design implementation process is as follows: The initial layout of the multi-irradiation source nodes is determined based on specific mission requirements; the position of each node is initially set based on the distribution of the mission target area and the channel environment. Consider the overall coverage of the detection network, the directional properties of the main lobe, and the interference avoidance capability; Set up multiple signal paths and backup nodes to provide redundancy backup in case of node failure or channel fading; By monitoring the status of nodes and changes in the environment, the probe network generates feedback information, which is used as input for subsequent topology optimization, ensuring that the probe network can automatically adapt to and adjust the network topology in a constantly changing environment.

5. The method for cooperative detection based on air-sea distributed anti-jamming passive radar according to claim 4, characterized in that, The feedback information includes: Detection performance evaluation for each node: detection range, signal strength; Network coverage and redundancy design: Are there any blind spots or weak signal areas? Environmental impact assessment results: the impact of interference sources and changes in target detection accuracy.

6. The method for cooperative detection based on air-sea distributed anti-jamming passive radar according to claim 1, characterized in that, The implementation process of topology optimization and dynamic node deployment is as follows: When the task scenario changes, the node deployment can be reconfigured by moving nodes or activating backup nodes; An adaptive topology control algorithm is used to fine-tune the spatial positions of nodes, thereby adjusting the network topology to cope with environmental changes. By utilizing multipath redundancy and alternative node configuration, the detection network can maintain good main lobe direction gain and anti-interference capability even if some nodes fail.

7. The method for cooperative detection based on air-sea distributed anti-jamming passive radar according to claim 1, characterized in that, The synchronization network ensures time and frequency synchronization between the airborne multi-source illumination network and the receiving network; it uses a time difference measurement method to measure the time delay of signal transmission and adjusts the compensation strategy according to changes in link distance or path; and it introduces errors through a delay correction circuit to achieve dynamic compensation and maintain the synchronization of the slave station's time base with the master station.

8. The method for cooperative detection based on air-sea distributed anti-jamming passive radar according to claim 2, characterized in that, The task requirements include coverage area, target direction, and location of interference sources.

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