Cooperative detection method based on air-sea distributed anti-interference passive radar

Abstract

The invention discloses a cooperative detection method based on an air-sea distributed anti-interference passive radar, and the method comprises the steps: constructing an air-sea distributed radar cooperative detection network which comprises an air multi-irradiation-source network, a receiving network and a synchronous network; the initial topological configuration design provides a basic framework of the air-sea distributed radar cooperative detection network; the signal quality of the detection network is enhanced through beam forming weight optimization; reasonable distribution of detection network resources is ensured by adopting a node weighted accepting and rejecting strategy; based on topological optimization and dynamic deployment, the whole detection network is further optimized and adjusted, and the anti-interference performance of the detection network is improved. According to the method, the signal quality and the anti-interference performance of the radar system in a multi-source and multi-path environment are improved, the expansibility and the real-time performance of the system in a large-scale complex task are enhanced, and powerful support is provided for stable operation of the radar system in a diversified task environment.

Description

Technical Field

[0001] The invention belongs to the field of radar, and in particular relates to a method for collaborative detection based on air-sea distributed anti-interference passive radars. Background Art

[0002] At present, research in the field of radar is mainly focused on the single-transmitter-multiple-receiver system. The main feature of the single-transmitter-multiple-receiver system is detection through one transmitting source and multiple receiving units. Although this technology has good performance in some applications, its detection capability is often limited in complex environments. Distributed multi-source radar systems are usually composed of multiple radar sites, which can not only cooperate with each other, but also dynamically adjust the working mode according to needs. They have the advantages of strong anti-interference ability, strong anti-anti-radiation missile ability, strong anti-stealth ability, and strong anti-low-altitude penetration ability. With the continuous development of technology, this field is expected to play an increasingly important role in military defense, aerospace, intelligent transportation and other fields. At the same time, it also faces many technical challenges and needs to continuously break through the existing limitations through efforts in algorithm innovation, hardware support and system optimization.

[0003] In a distributed multi-source cooperative detection radar system, the selection of radar illumination sources and resource optimization are key factors in improving system performance, detection accuracy, anti-interference capability, and cost reduction. Correctly selecting illumination sources and optimizing resource allocation not only affects the system's work efficiency, but also determines the system's adaptability and flexibility in complex environments. This study is aimed at a distributed radar detection system with multiple transmitters and receivers. Through multi-source optimization and evaluation, a high-precision, high-flexibility distributed radar detection network is established, and a breakthrough is made in the detection algorithm for cooperative and non-cooperative source signal fusion, further improving the detection accuracy and system anti-interference performance. Summary of the invention

[0004] Purpose of the invention: The present invention proposes a method for cooperative detection based on air-sea distributed anti-interference passive radars, which is aimed at a distributed radar detection system with multiple transmissions and multiple receptions. Through multi-source optimization and evaluation, a high-precision and high-flexibility distributed radar detection network is established, which breaks through the cooperative source and non-cooperative source signal fusion detection algorithm, and further improves the detection accuracy and system anti-interference performance.

[0005] Technical solution: The method of collaborative detection based on air-sea distributed anti-interference passive radar described in the present invention specifically includes:

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

[0007] The initial topology configuration design provides the infrastructure for the air-sea distributed radar collaborative detection network;

[0008] Use beamforming weight optimization to enhance the signal quality of the detection network;

[0009] Adopting node weighted selection strategy to ensure reasonable allocation of detection network resources;

[0010] Based on topology optimization and dynamic deployment, the entire detection network is further optimized and adjusted to improve the anti-interference performance of the detection network.

[0011] Furthermore, the aerial multi-illumination source network includes multiple illumination sources; the illumination sources provide multi-angle and multi-path signal illumination, flexibly select and configure emission source nodes according to mission requirements, and achieve optimal arrangement of multiple emission sources through topology optimization and resource allocation.

[0012] Furthermore, the receiving network includes multiple receivers, which are 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 topological configuration, and together with the illumination source network, the detection range and target recognition capability of the detection network are improved.

[0013] Further, the initial topology configuration design implementation process is as follows:

[0014] Determine the initial layout of multiple illumination source nodes according to specific mission requirements; preliminarily set the position of each node according to the distribution of the mission target area and the channel environment;

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

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

[0017] By monitoring the status of nodes and changes in the environment, the detection network will generate feedback information, which will be used as input for subsequent topology optimization, ensuring that the detection network can automatically adapt and adjust the network topology in a changing environment.

[0018] Further, the feedback information includes:

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

[0020] Network coverage and redundancy design: whether there are blind spots or areas with weak signals;

[0021] Results of environmental impact assessment: impact of interference sources, changes in target detection accuracy.

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

[0023] Maximize the signal gain in the target direction and minimize the sidelobe interference; optimize the beamforming pattern by solving the following optimization objectives:

[0024] max|w H a(θ0)| 2

[0025]

[0026] Wherein, 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] The weight vector is dynamically adjusted using an iterative optimization algorithm to maximize the main lobe gain while suppressing side lobe interference.

[0028] Furthermore, the node weighted selection strategy is implemented as follows:

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

[0030] To ensure 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 represents the power of the i-th node, α i and β i is the adjustment coefficient used to balance the overall contribution of power consumption and interference impact to the detection network; G i represents the signal gain of the i-th node;

[0033] Set each node to a binary choice variable x i , takes a value of 0 or 1, where x i =1 means the node is enabled, x i =0 means the node is disabled; the optimization objective can be re-expressed as:

[0034]

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

[0036] When the mission scenario changes, reconfigure the node deployment by moving nodes or activating standby nodes;

[0037] Use adaptive topology control algorithms to fine-tune the spatial positions of nodes and adjust network topology to cope with environmental changes;

[0038] Multipath redundancy and alternative node configuration are used to ensure that the detection network can maintain good main lobe directional gain and anti-interference capabilities even when some nodes fail.

[0039] Furthermore, the synchronization network ensures the time and frequency synchronization between the aerial multi-irradiation source network and the receiving network; measures the time delay of signal transmission using the time difference measurement method, and adjusts the compensation strategy according to the link distance or path changes; introduces errors through the delay correction circuit to achieve dynamic compensation, and maintains the synchronization of the slave station time base with the master station.

[0040] Furthermore, the mission requirements include coverage range, target direction, and interference source location.

[0041] Beneficial effects: Compared with the prior art, the present invention has the following beneficial effects: By dynamically adjusting the power allocation of the illumination source and the node activation status, the present invention enables high-contribution, low-interference nodes to obtain resources first, thereby reducing unnecessary power consumption and interference sources, and significantly enhancing the interference suppression capability of the system; the present invention concentrates resources on key nodes based on weight-optimized beamforming, effectively improving the signal gain in the target direction, while suppressing sidelobe interference, and ensuring the detection accuracy of the radar system in complex environments; in addition, through the adaptive deployment of the topological configuration, the system can be flexibly adjusted according to different environmental conditions, thereby maintaining efficient anti-interference capability when the distribution of interference sources changes, and enhancing the environmental adaptability of the system; the multi-path redundant configuration further improves the robustness of the system under harsh conditions, so that the system can still operate normally when a node fails or the channel changes; the present invention not only improves the signal quality and anti-interference performance of the radar system in a multi-source, multi-path environment, but also enhances the scalability and real-time performance of the system in large-scale complex tasks, providing strong support for the stable operation of the radar system in a diverse mission environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of the air-sea distributed radar collaborative detection network. DETAILED DESCRIPTION

[0043] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0044] The present invention proposes a method for collaborative detection based on air-sea distributed anti-interference passive radar, and the specific implementation process is as follows:

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

[0046] Aerial multi-illumination source network: includes multiple illumination sources (usually radar signal transmitters); the illumination sources provide multi-angle and multi-path signal illumination, flexibly select and configure the emission source nodes according to mission requirements, and achieve the optimal layout of multiple emission sources through topology optimization and resource allocation.

[0047] Receiving network: includes multiple receivers (usually radar receivers), which are responsible for signal reception and processing, signal fusion and target tracking, anti-interference and signal gain. Multiple receiving sources are distributed through topological configuration optimization, and together with the illumination source network, they improve the detection range and target recognition capability of the radar system.

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

[0049] In the detection network, the selection of illumination sources and receivers and the allocation of resources are crucial. The transmitting nodes and receiving nodes in the detection network need to adjust the selection of nodes and the optimization of the topology according to real-time needs to ensure the best configuration, and then improve the robustness and anti-interference performance of the system through adaptive beamforming. The entire detection network is actually a complete optimization cycle. The initial topology configuration design provides the infrastructure, beamforming weight optimization enhances signal quality, node weighted selection strategy ensures the reasonable allocation of resources, and topology optimization and dynamic deployment are further refined adjustments to the entire system. Through this cycle, the system can continuously improve its performance, flexibility and adaptability, and can always maintain efficient detection capabilities in changing environments and complex mission requirements.

[0050] (1) Initial topological configuration design.

[0051] Determine the initial layout of multiple illumination source nodes according to specific mission requirements (such as coverage range, target direction, interference source location, etc.); preliminarily set the location of each node according to the distribution of the mission target area and the channel environment;

[0052] The initial layout should consider the overall coverage of the system, the directivity of the main lobe and the interference avoidance capability;

[0053] Set up multiple signal paths and alternative nodes to provide redundant backup 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 Detection performance evaluation of each node (such as detection range, signal strength, etc.).

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

[0057] ③Evaluation results of environmental impacts (such as the impact of interference sources, changes in target detection accuracy, etc.). These 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 changing environment.

[0058] (2) Beamforming weight optimization.

[0059] Optimization goal: maximize the signal gain in the target direction and minimize the sidelobe interference. The beamforming pattern optimization is achieved by solving the following optimization goals:

[0060] max|w H a(θ0)| 2

[0061]

[0062] Wherein, 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: Use iterative optimization algorithms (such as convex optimization, particle swarm algorithm, etc.) to dynamically adjust the weight vector to maximize the main lobe gain while suppressing side lobe interference.

[0064] Algorithm implementation: Conduct frequency offset and weight adjustment experiments 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 that the system contains N nodes, the contribution of each node to the target direction is represented by weight ω, and the interference to the system is represented by I i express.

[0067] To ensure 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 represents the power of the i-th node, α i and β i is the adjustment factor used to balance the overall contribution of power consumption and interference impact to the system, I i is the interference of the ith node to the system. i Represents the signal gain of the i-th node.

[0070] Set each node to a binary choice variable x i (value is 0 or 1), where x i =1 means the node is enabled, xi = 0 means the node is disabled. The optimization objective can be re-expressed as:

[0071]

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

[0073] Dynamic deployment: When the mission scenario changes (such as moving from an open field to a densely built area), reconfigure the deployment of nodes. You can adapt to the new environment by moving nodes or activating spare nodes to maintain system performance.

[0074] Adaptive topology adjustment: Based on the information recorded by the system, re-detect nodes and feedback, fine-tune the spatial position of the nodes, adjust the deployment based on feedback, and respond to environmental changes by adjusting the network topology.

[0075] Robustness guarantee: Multipath redundancy and alternative node configuration are used to ensure that the system can maintain good main lobe directional gain and anti-interference capabilities even when some nodes fail.

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

[0077] The synchronization network adopts a master-slave distributed RF transmission scheme to achieve high-precision time synchronization, significantly enhancing the autonomy and stability of the system, and is particularly suitable for scenarios where there are restrictions or instability in GPS timing. Compared with the traditional GPS timing method, this scheme avoids dependence on GPS signals through master-slave coordination and reduces synchronization errors caused by the phase pulse ambiguity characteristics of the wireless link.

[0078] The rising edge phase difference of the signal transmitted by each slave station in the detection network needs to be controlled within 5ns, which is achieved by accurately controlling the phase lock of the local oscillator of the slave station. After the slave station receives the clock signal from the master station, it aligns its own local oscillator with the phase of the master station signal through the phase-locked loop (PLL), thereby ensuring that the rising edge of the signal of all slave stations is in a strictly synchronized state. A 5MHz frequency signal is used as the slave station time base. After the slave station receives the 5MHz clock signal from the master station, a phase comparator is used for real-time error correction. This frequency reference locks the master station signal through a phase-locked loop, and the relative error can reach the sub-nanosecond level, which can effectively eliminate the time base deviation on the link and achieve high-precision synchronization.

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

[0080] The above content has described in detail the method for enhancing the training data of the traceability link recovery model based on unlabeled data of the present invention, but it is obvious that the specific implementation form of the present invention is not limited thereto. For those skilled in the art, various obvious changes made to it without departing from the spirit and scope of the present invention are within the protection scope of the present invention.

Claims

1. A method for collaborative detection based on air-sea distributed anti-interference passive radar, characterized in that: The implementation process is as follows: Construct an air-sea distributed radar collaborative detection network, including an airborne multi-illumination source network, a receiving network, and a synchronization network; The initial topology configuration design provides the infrastructure for the air-sea distributed radar collaborative detection network; Use beamforming weight optimization to enhance the signal quality of the detection network; Adopting node weighted selection strategy to ensure reasonable allocation of detection network resources; Based on topology optimization and dynamic deployment, the entire detection network is further optimized and adjusted to improve the anti-interference performance of the detection network.

2. The method of collaborative detection based on air-sea distributed anti-interference passive radar according to claim 1 is characterized in that: The aerial multi-illumination source network includes multiple illumination sources; the illumination sources provide multi-angle and multi-path signal illumination, flexibly select and configure emission source nodes according to mission requirements, and achieve optimal arrangement of multiple emission sources through topology optimization and resource allocation.

3. The method of collaborative detection based on air-sea distributed anti-interference passive radar according to claim 1 is characterized in that: The receiving network includes multiple receivers, which are 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 topological configuration, and together with the illumination source network, they improve the detection range and target recognition capability of the detection network.

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

5. The method of collaborative detection based on air-sea distributed anti-interference passive radar according to claim 4 is characterized in that: The feedback information includes: Detection performance evaluation of each node: detection range, signal strength; Network coverage and redundancy design: whether there are blind spots or areas with weak signals; Results of environmental impact assessment: impact of interference sources, changes in target detection accuracy.

6. The method of cooperative detection based on air-sea distributed anti-interference passive radar according to claim 1 is characterized in that: The weight optimization implementation process of the adaptive beamforming is as follows: Maximize the signal gain in the target direction and minimize the sidelobe interference; optimize the beamforming pattern by solving the following optimization objectives: max|w H a(θ0)| 2 Wherein, 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.

7. The method of cooperative detection based on air-sea distributed anti-interference passive radar according to claim 1 is characterized in that: 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 weight ω i The interference to the detection network is represented by I i express; To ensure the target direction gain G target Under the premise of minimizing the total power consumption and interference of unnecessary nodes: Among them, P i represents the power of the i-th node, α i and β i is the adjustment coefficient used to balance the overall contribution of power consumption and interference impact to the detection network; G i represents the signal gain of the i-th node; Set each node to a binary choice variable x i , takes a value of 0 or 1, where x i =1 means the node is enabled, x i =0 means the node is disabled; the optimization objective can be re-expressed as:

8. The method of collaborative detection based on air-sea distributed anti-interference passive radar according to claim 1 is characterized in that: The topology optimization and dynamic node deployment implementation process is as follows: When the mission scenario changes, reconfigure the node deployment by moving nodes or activating standby nodes; Use adaptive topology control algorithms to fine-tune the spatial positions of nodes and adjust network topology to cope with environmental changes; Multipath redundancy and alternative node configuration are used to ensure that the detection network can maintain good main lobe directional gain and anti-interference capabilities even when some nodes fail.

9. The method of cooperative detection based on air-sea distributed anti-interference passive radar according to claim 1 is characterized in that: The synchronization network ensures the time and frequency synchronization between the aerial multi-irradiation source network and the receiving network; uses the time difference measurement method to measure the time delay of signal transmission, and adjusts the compensation strategy according to the link distance or path change; introduces errors through the delay correction circuit to achieve dynamic compensation, and maintains the synchronization of the slave station time base with the master station.

10. The method of cooperative detection based on air-sea distributed anti-interference passive radar according to claim 2 is characterized in that: It is characterized in that The mission requirements include coverage range, target direction, and interference source location.

Images