A ship stealth monitoring system based on multi-band radar networking technology

Abstract

The present invention relates to the technical field of intelligent ships. The present invention provides a ship stealth monitoring system based on multi-band radar networking technology, comprising a radar networking module, a data fusion module, a satellite reconnaissance module, and an early warning module. The radar networking module uses at least one radar band to collect shape data of a stealth ship to determine the stealth ship in a target monitoring area for monitoring. The satellite reconnaissance module is used to collect satellite images of the target monitoring area to determine the monitoring data of the target monitoring area. The data fusion module integrates the shape data of the stealth ship collected from the radar networking module and the monitoring data of the target monitoring area collected from the satellite reconnaissance module, and evaluates the target monitoring area to form an evaluation result to monitor the stealth ship. The early warning module interactively warns the manager of the evaluation result.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent ships, and in particular to a ship stealth monitoring system based on multi-band radar networking technology. Background Art

[0002] With the rapid development of modern military technology, stealth technology has become an important direction in ship design. Through special shape design and material application, stealth ships have greatly reduced the reflected signals of radar, sonar, infrared and other detection methods, making them have strong survivability in hostile environments.

[0003] For example, Chinese patent CN116973862A discloses a stealth radar that determines the target position based on a radar near-field positioning algorithm. The target detected by the detection signal is positioned through the radar near-field positioning algorithm, but lacks multi-band networking detection, resulting in reduced target detection accuracy or reduced detection precision.

[0004] In addition, the existing traditional detection methods have significantly reduced the detection effect of these stealth ships, which is specifically manifested in the following aspects: 1. Traditional radar systems usually use a single band for target detection. However, the design of stealth ships is often optimized for radars of specific bands, which significantly reduces their reflection cross-sectional area (RCS), making it difficult for single-band radars to effectively detect stealth targets; 2. Reflection blind spot problem: The shape design and material use of stealth ships make their reflected signals extremely weak at specific angles and frequency bands, forming detection blind spots. Due to the limitations of bands and viewing angles, traditional radars are difficult to fully cover these blind spots; 3. Data isolation and fusion difficulties: Even if multi-band radars are used, if the data of each radar system cannot be effectively fused, it is still difficult to form a comprehensive target recognition result. The isolation of data leads to the dispersion of detection information, which reduces the overall detection efficiency and accuracy; 4. Challenges of dynamic target tracking: Stealth ships have complex movement patterns in the ocean. Single radar systems have problems of slow response and insufficient accuracy in real-time dynamic tracking, making it difficult to effectively monitor rapidly changing targets; 5. Response to electronic countermeasures: Stealth ships may carry electronic countermeasures equipment to actively interfere with enemy radar detection signals. Traditional detection methods often seem powerless in the face of such active interference.

[0005] The present invention is made to solve the common problems in the field, such as poor detection capability, inability to conduct comprehensive detection, isolation of various detection data, inability to fuse data, low detection accuracy, low intelligence, and weak ability to resist active interference. Summary of the invention

[0006] The purpose of the present invention is to propose a ship stealth monitoring system based on multi-band radar networking technology in view of the current shortcomings.

[0007] In order to overcome the shortcomings of the prior art, the present invention adopts the following technical solutions:

[0008] A ship stealth monitoring system based on multi-band radar networking technology, the ship stealth monitoring system includes a radar networking module, a data fusion module, a satellite reconnaissance module, and an early warning module, the radar networking module uses at least one radar band to collect the shape data of a stealth ship to determine the stealth ship in a target monitoring area for monitoring, the satellite reconnaissance module is used to collect satellite images of the target monitoring area to determine the monitoring data of the target monitoring area, the data fusion module integrates the shape data of the stealth ship collected by the radar networking module and the monitoring data of the target monitoring area collected by the satellite reconnaissance module, and evaluates the target monitoring area to form an evaluation result, so as to monitor the stealth ship, and the early warning module interactively warns the manager of the evaluation result;

[0009] The radar networking module includes a radar node unit, a data transmission and communication unit, and a time synchronization unit. The time synchronization unit synchronizes the time of at least one radar band equipped with each radar node. The radar node unit performs communication connection with at least one radar band equipped with each radar node to form a distributed radar network. The data transmission and communication unit is used for data transmission and communication between at least one radar band equipped with each radar node.

[0010] The radar node unit communicates with at least one radar band equipped with each radar node in a polling manner, and triggers each radar node to detect the shape data of the stealth ship with at least one radar band.

[0011] Optionally, the polling method of the radar node unit includes the following steps:

[0012] S1, initialization phase:

[0013] The master control node starts, initializes all radar nodes, and establishes an initial communication connection. Each radar node registers with the master control node, reports its own status and capabilities, and all nodes are synchronized through the time synchronization unit;

[0014] The master control node refers to the core node responsible for managing and coordinating the entire radar network;

[0015] S2. Polling task allocation:

[0016] The master control node randomly selects several radar nodes for polling task allocation according to a preset algorithm.

[0017] According to the detection requirements of the current sea area and the node status, the master control node dynamically adjusts the polling tasks of each node and distributes specific detection tasks to the selected radar nodes. The detection tasks include the priority of the detection target, the polling frequency, and the data upload requirements;

[0018] S3, polling execution: the radar nodes assigned tasks perform detection tasks according to the instructions issued by the master control node and collect target data. After completing the detection task, each radar node uploads the collected data to the master control node according to the preset time interval or conditions. Each node regularly reports its own status to the master control node, including power, detection results, and fault conditions;

[0019] S4, master control node processing: the master control node receives the data uploaded by each radar node, and dynamically adjusts the polling strategy according to the real-time data and environmental changes. The master control node monitors the status of each node and handles it in time when an abnormal situation is found;

[0020] S5, Feedback and Optimization: The master control node evaluates the detection results of each radar node and sends feedback information to each node to guide the next round of detection tasks. Based on historical data and current detection results, the master control node continuously optimizes the random polling algorithm.

[0021] Optionally, the satellite reconnaissance module includes a satellite reconnaissance unit, a feedback unit, and a positioning request unit, the satellite reconnaissance unit acquires high-resolution visible light images and multispectral images of the target monitoring area, the feedback unit transmits the images and data acquired by the satellite back to the ground control center, and the positioning request unit feeds back precise positioning information of the target monitoring area to the satellite to call up real-time visible light images and multispectral images of the target monitoring area.

[0022] Optionally, the data fusion module includes a data processing unit and a fusion analysis unit, wherein the data processing unit processes the shape data of the stealth ship, and the high-resolution thermal visible light image and multi-spectral image of the target monitoring area to form a multi-dimensional data vector, and the fusion analysis unit fuses the multi-dimensional data vector processed by the data processing unit to evaluate the target monitoring area and form an evaluation result.

[0023] Optionally, the data processing unit processes the shape data of the stealth ship, and the high-resolution visible light image and multi-spectral image of the target monitoring area according to the following steps:

[0024] S11, collecting data of radar networking module;

[0025] S12, collecting data from satellite reconnaissance modules;

[0026] S13, performing data standardization on the data collected in step S11 and step S12;

[0027] S14. Combining the standardized feature data into a multi-dimensional data vector.

[0028] Optionally, the fusion analysis unit obtains a multidimensional data vector and calculates a risk index Worn according to the following formula:

[0029] ;

[0030] Where d(R, S) is the weighted distance between the multidimensional vector R and the multidimensional vector data S;

[0031] If the danger index Worn is greater than or equal to the monitoring threshold Danger set by the system, it indicates that there is a threat of ship stealth in the target area.

[0032] Optionally, the positioning request unit includes a positioning requester and a data receiver, the positioning requester sends a positioning request for the target monitoring area to the satellite, and the data receiver is used to receive real-time visible light images and multispectral images of the target area fed back by the satellite.

[0033] Optionally, before the positioning requester sends the positioning request of the target monitoring area to the satellite, it is necessary to pre-process the positioning data, and the pre-processing step includes:

[0034] S21, initialization and signal reception:

[0035] Start the GNSS receiver to receive navigation satellite signals, initialize the positioning requester, and prepare to receive and process positioning data;

[0036] S22. Calculate the exact position:

[0037] The GNSS receiver receives signals from navigation satellites to determine the real-time position of the target monitoring area, wherein the real-time position includes longitude and latitude;

[0038] S23. Calling satellite images:

[0039] Based on precise real-time information, the positioning requester sends a positioning request to the satellite image acquisition system, calls up high-resolution visible light images and multispectral images of the corresponding location, and prepares to transmit them back to the ground.

[0040] Optionally, the early warning module includes an early warning unit and an interactive unit, the early warning unit obtains the evaluation result and a monitoring threshold Danger set by the system, and compares them to determine an early warning prompt, and the interactive unit interactively prompts the early warning prompt to the manager.

[0041] Optionally, at least one radar band includes radars of low frequency, medium frequency and high frequency bands.

[0042] The beneficial effects achieved by the present invention are:

[0043] 1. Through the cooperation among the radar networking module, the data fusion module and the satellite reconnaissance module, the stealth of the ship can be monitored, ensuring that the entire system has the advantages of strong detection capability, good detection data fusion effect, high detection accuracy and high intelligence;

[0044] 2. Through the mutual cooperation between the early warning module and the data fusion module, the ship manager can dynamically monitor the stealth ship, ensuring that the entire system has the advantages of good interactive ability, strong intelligence and high detection and early warning capabilities.

[0045] 3. Through the radar networking module, the radars are networked to enhance the detection capability of stealth ships, ensuring that the entire system has the advantages of high detection accuracy, comprehensive detection range and strong detection capability;

[0046] 4. Through the mutual cooperation between the radar networking module and the satellite reconnaissance module, the monitoring capability of the stealth ship is further enhanced, ensuring that the entire system has the advantages of wide monitoring range, strong monitoring capability, good monitoring accuracy and high intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0048] Figure 1 It is an overall block diagram of the present invention.

[0049] Figure 2 It is a process schematic diagram of the data processing unit and the fusion analysis unit of the present invention.

[0050] Figure 3 The block diagram of radar data obtained by processing the data collected by the radar networking module by the data processing unit of the present invention.

[0051] Figure 4 The block diagram is a schematic diagram of the data processing unit of the present invention processing the data collected by the satellite reconnaissance module.

[0052] Figure 5 It is a schematic diagram of the evaluation process of the posture evaluation unit of the present invention. DETAILED DESCRIPTION

[0053] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. In addition, the drawings of the present invention are only simple schematic illustrations and are not depicted according to actual sizes. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention.

[0054] Embodiment 1: According to Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 As shown, this embodiment provides a ship stealth monitoring system based on multi-band radar networking technology, the ship stealth monitoring system includes a radar networking module, a data fusion module, a satellite reconnaissance module, and an early warning module, the radar networking module uses at least one radar band to collect the shape data of the stealth ship to determine the stealth ship in the target monitoring area for monitoring, the satellite reconnaissance module is used to collect satellite images of the target monitoring area to determine the monitoring data of the target monitoring area, the data fusion module integrates the shape data of the stealth ship collected from the radar networking module and the monitoring data of the target monitoring area collected by the satellite reconnaissance module, and evaluates the target monitoring area to form an evaluation result, so as to monitor the stealth ship, and the early warning module interactively warns the manager of the evaluation result;

[0055] The ship stealth monitoring system also includes a server and a central processor, wherein the server is respectively connected to the radar networking module, the data fusion module, the satellite reconnaissance module, and the early warning module, and stores the process data and intermediate data of the radar networking module, the data fusion module, the satellite reconnaissance module, and the early warning module in a database of the server for query or call;

[0056] The central processor is respectively connected to the radar networking module, the data fusion module, the satellite reconnaissance module, and the early warning module, and centrally controls the radar networking module, the data fusion module, the satellite reconnaissance module, and the early warning module based on the central processor, and stores the control data of the central processor in the database of the server, thereby improving the monitoring accuracy and monitoring efficiency of the entire system for ship stealth;

[0057] The radar networking module includes a radar node unit, a data transmission and communication unit, and a time synchronization unit. The time synchronization unit synchronizes the time of at least one radar band equipped with each radar node. The radar node unit performs communication connection with at least one radar band equipped with each radar node to form a distributed radar network. The data transmission and communication unit is used for data transmission and communication between at least one radar band equipped with each radar node.

[0058] The radar node unit communicates with at least one radar band equipped with each radar node in a polling manner, and triggers each radar node to detect the shape data of the stealth ship with at least one radar band;

[0059] The data transmission and communication unit includes a communicator and a connector, wherein the connector is used for each radar node to connect, and the communicator is used for communication between each connected radar node that establishes a connection relationship;

[0060] Wherein, the communication mode of the communicator includes wired transmission and wireless transmission;

[0061] The time synchronization unit includes a GNSS receiver, a time decoder, an internal clock, a pulse synchronization module, a network time protocol (NTP) client, a precision time protocol (PTP) hardware, a time distributor and a time calibrator. The GNSS receiver receives a time signal from a global navigation satellite system (such as GPS, GLONASS, Galileo or Beidou) to provide high-precision time and position information. The time decoder is used to decode the time signal output by the GNSS receiver and convert it into a time format available to the system. The internal clock provides a basic time and is calibrated by an external time source to maintain time accuracy. The pulse synchronization module (PPS module) generates a pulse signal per second (PPS) for high-precision time synchronization. The network time protocol (NTP) client obtains time data from the NTP server through the network to achieve time synchronization. The precision time protocol (PTP) hardware achieves sub-microsecond time synchronization through the PTP protocol (IEEE 1588). The time distributor is used to distribute the time signal to each module in the system to ensure that the time of all parts is consistent. The time calibrator is used to regularly calibrate the internal clock to ensure time accuracy.

[0062] The internal clock includes: a quartz crystal oscillator, a temperature compensated crystal oscillator (TCXO), and an atomic clock. The quartz crystal oscillator is used to provide a highly stable clock signal, the temperature compensated crystal oscillator (TCXO) is used to improve the temperature stability of the clock, and the atomic clock is used under extremely high precision requirements to improve the accuracy of the entire clock.

[0063] The synchronization process of the time synchronization unit is as follows:

[0064] S31, initialization: start the GNSS receiver, receive and decode the navigation satellite time signal;

[0065] Initialize the internal clock and perform the initial calibration via GNSS signals;

[0066] S32, time decoding and synchronization: the time decoder parses the GNSS time signal and converts it into the system standard time format. The internal clock receives the decoded time signal and performs synchronization;

[0067] S33, generate and distribute PPS signal: the pulse synchronizer generates PPS signal and distributes it to each submodule through the time distributor; wherein the pulse synchronization module includes a PPS signal generator and a PPS signal distributor, the PPS signal generator receives the time signal from the GNSS receiver or the clock and generates the PPS signal; the PPS signal distributor is used to distribute the PPS signal to each part of the system;

[0068] S34, Network time synchronization: NTP client obtains time data from NTP server through the network and synchronizes with the internal clock; PTP hardware realizes time synchronization through PTP protocol (IEEE 1588), and the PTP slave clock of each node is synchronized with the master clock;

[0069] S35, regular calibration: The clock calibration unit regularly obtains the latest time data from the GNSS receiver or NTP / PTP server to calibrate the internal clock. The calibration process is automatically completed by the software to ensure time accuracy;

[0070] In this embodiment, optionally, at least one radar band includes radars of low frequency, medium frequency and high frequency bands; wherein, the high frequency radar is mainly used to detect detailed features of the ship, has high-resolution imaging capability, and can accurately detect the shape and contour of the ship; the medium frequency radar has good penetration capability, can detect the characteristics of the stealth material to a certain extent, and preliminarily identify the shape and size of the target; the low frequency radar has strong penetration capability, and can detect the contour and structure of the stealth ship;

[0071] Optionally, the polling method of the radar node unit includes the following steps:

[0072] S1, initialization phase:

[0073] The master control node starts, initializes all radar nodes, and establishes an initial communication connection. Each radar node registers with the master control node, reports its own status and capabilities, and all nodes are synchronized through the time synchronization unit;

[0074] The master control node refers to the core node responsible for managing and coordinating the entire radar network;

[0075] S2. Polling task allocation:

[0076] The master control node randomly selects several radar nodes for polling task allocation according to a preset algorithm. According to the detection requirements of the current sea area and the node status, the master control node dynamically adjusts the polling task of each node (for example, increasing the polling frequency of high-frequency radar to deal with high-speed targets), and distributes specific detection tasks to the selected radar nodes;

[0077] Among them, in this embodiment, the detection task includes the priority of the detection target, the polling frequency, and the data upload requirement;

[0078] S3, polling execution: the radar nodes assigned tasks perform detection tasks according to the instructions issued by the master control node and collect target data. After completing the detection task, each radar node uploads the collected data to the master control node according to the preset time interval or conditions. Each node regularly reports its own status to the master control node, including power, detection results, and fault conditions;

[0079] S4, master control node processing: the master control node receives the data uploaded by each radar node, and dynamically adjusts the polling strategy (for example, adjusting the polling frequency, reallocating task nodes, etc.) according to the real-time data and environmental changes. The master control node monitors the status of each node and handles abnormal situations (such as node failure, signal interference, etc.) in a timely manner;

[0080] S5, feedback and optimization: the master control node evaluates the detection results of each radar node, and sends feedback information to each node to guide the next round of detection tasks. Based on historical data and current detection results, the master control node continuously optimizes the random polling algorithm;

[0081] The radar node unit polls each radar node using a random polling algorithm, specifically:

[0082] The specific steps of the random polling algorithm include:

[0083] S41, radar node initialization

[0084] Set the radar node set N = {N1, N2, N3, ..., N n’}, each node N i With initial weight ω i ; n' is the total number of nodes;

[0085] Initial weight ω i The system is set according to the capability and status factors of the radar node to ensure that high-capability nodes have a higher probability of being selected;

[0086] In this embodiment, the initial weight ω i Determine according to the following formula:

[0087] ;

[0088] In the formula, ability i is the capability factor, state i is the state factor, task i is the task factor, and its value is calculated according to the following formula:

[0089] ;

[0090] In the formula, E i is the normalized mission urgency of the i-th radar node, I i is the normalized mission importance of the i-th radar node;

[0091] For the state factor i Calculate according to the following formula:

[0092] ;

[0093] Where U i is the normalized CPU utilization of the ith radar node, M i is the normalized memory utilization of the i-th radar node;

[0094] For the ability factor i Calculate according to the following formula:

[0095] ;

[0096] In the formula, C i is the normalized computing power of the ith radar node, S i is the normalized storage capacity of the ith radar node, B i is the normalized communication bandwidth of the i-th radar node;

[0097] Among them, the standardized task importance of the radar node is i Calculated according to the following formula:

[0098] ;

[0099] In the formula, I t is the task importance of the radar node, and its value is obtained by using task management and scheduling tools such as Kubernetes, SLURM or Hadoop Yarn to obtain the task importance.min The minimum value corresponding to the importance set for the radar node, I max a maximum value corresponding to the importance set for the radar node;

[0100] The normalized mission urgency level E of the radar node i Calculate according to the following formula:

[0101] ;

[0102] In the formula, E t is the task urgency of the radar node, and its value is obtained by using task management and scheduling tools such as Kubernetes, SLURM or Hadoop Yarn to obtain the task urgency. min The minimum value of the mission urgency set for the radar node, E max a maximum value of the mission urgency set for the radar node;

[0103] The normalized memory utilization M of the radar node i Calculate according to the following formula:

[0104] ;

[0105] Where M t The memory utilization of the radar node is calculated by using dedicated performance monitoring software, such as Prometheus or Nagios, to monitor memory usage in real time. min The minimum memory utilization set for the radar node, M max A maximum memory utilization value set for the radar node;

[0106] The normalized CPU utilization U of the radar node i Calculate according to the following formula:

[0107] ;

[0108] Where U t The CPU utilization of the radar node is calculated by using dedicated performance monitoring software, such as Prometheus or Nagios, to monitor the CPU usage in real time. min The minimum CPU utilization value set for the radar node, U max The maximum CPU utilization value set for the radar node;

[0109] The standardized communication bandwidth B of the radar node i Calculate according to the following formula:

[0110] ;

[0111] In the formula, B t is the communication bandwidth of the radar node, and its value is obtained by using a network performance test tool, such as iperf, to measure the actual bandwidth. min The minimum value of the communication bandwidth set for the radar node, B max a maximum value of the communication bandwidth set for the radar node;

[0112] The standardized storage capacity S of the radar node i Calculate according to the following formula:

[0113] ;

[0114] In the formula, S t The storage capacity of the radar node, whose value is measured by running a storage benchmark tool such as IOzone or Fio to measure the storage read and write speed, S min The minimum storage capacity set for the radar node, S max a maximum storage capacity set for the radar node;

[0115] The standardized communication bandwidth C of the radar node i Calculate according to the following formula:

[0116] ;

[0117] In the formula, C t The computing power of the radar node is obtained by running a standard CPU benchmark tool, such as SPEC CPU Benchmark, to obtain the CPU performance score. min The minimum value of computing power set for the radar node, C max a maximum value of computing power set for the radar node;

[0118] Calculate the cumulative weight W of all nodes:

[0119] ;

[0120] In the formula, ω i is the initial weight of the i-th radar node;

[0121] S42, Node Selection

[0122] Roulette Wheel Selection is used to randomly select nodes for task allocation based on weights.

[0123] Here, a random number r in the range [0,W) is generated:

[0124] ;

[0125] Traverse the node set N, accumulate the node weights, and select the first node N whose cumulative weight is greater than or equal to r k , and the radar node N k Set to selected node;

[0126] S43. Task Allocation

[0127] Assign specific detection tasks based on the current status and detection requirements of the selected nodes;

[0128] Select radar node N k After that, tasks are assigned according to their current status and detection requirements;

[0129] In this embodiment, the task is assigned to the selected radar node according to the calculated task frequency Frequency:

[0130] The task frequency is calculated according to the following formula:

[0131] ;

[0132] In the formula, base is the basic update frequency set by the system, and its value is a fixed value, state i is the state factor, task i is the task factor;

[0133] In this embodiment, an example of a value of a basic update frequency base set by a system is provided, specifically:

[0134] 1) Set according to actual conditions, for example, determine the specific basic update frequency according to the data acquisition speed of the radar system;

[0135] 2) If the target in the monitoring area (such as a stealth ship) moves quickly, the system needs a higher update frequency to capture the real-time status of the target, thereby improving the tracking accuracy. Therefore, in the scenario of fast-moving targets, the base update frequency should be set higher;

[0136] 3) If the detection task itself requires high precision or real-time performance (for example, accurate tracking of high-speed moving targets), the value of base should be set higher to meet the task requirements;

[0137] 4) The base update frequency base should be set without affecting the stability of the system. Too high a frequency may cause system resource overload and affect the overall performance. Therefore, it is necessary to balance the response speed and stability of the system and determine a reasonable base update frequency based on the system's real-time requirements. If the system requires very high real-time performance (for example, real-time tracking of high-speed moving targets), the base needs to be set to a higher value.

[0138] In short, the basic update frequency base set by the system is usually determined based on many aspects such as the hardware performance of the radar system, the dynamics of the target scene, the complexity of the detection task, and the overall reliability requirements of the system. It needs to improve the real-time response capability of the system as much as possible under the premise of ensuring the stability and reliability of the system. A reasonably set base can ensure that the radar system can effectively perform tasks in various environments without missing detections due to too low an update frequency or causing system overload due to too high an update frequency. Therefore, the basic update frequency base of the system is determined by the system after rigorous analysis and experimental verification and preset through the human-computer interaction interface to ensure the best performance of the system in various tasks and environments;

[0139] S44, Dynamic Adjustment

[0140] Monitor node status in real time and dynamically adjust node weights based on detection data and environmental changes i ;

[0141] In this embodiment, the node weight ω' is dynamically adjusted. i Determine according to the following formula:

[0142] ;

[0143] In the formula, ω i is the initial weight, change is the adjustment factor, and its value is calculated according to the following formula:

[0144] ;

[0145] Where, P i is the detection performance factor of the i-th radar node, F i is the fault condition factor of the ith radar node, Eem i is the environmental change factor of the i-th radar node, and its value is calculated according to the following formula:

[0146] ;

[0147] In the formula, I i is the current electromagnetic interference intensity of the i-th radar node, I maxis the maximum interference intensity value of the i-th radar node determined by historical data;

[0148] For the detection performance factor P i , calculated according to the following formula:

[0149] ;

[0150] Where actual is the actual detection success rate of the ith radar node, and its value is obtained by using a dedicated detection management software or system to automatically record and count the detection results (wherein, the results of each detection recorded during node operation, including success and failure, can be obtained through logging), theory is the expected detection success rate set by the system;

[0151] For the fault condition factor F i , calculated according to the following formula:

[0152] ;

[0153] Where normal is the normal operation time of the ith radar node, and its value is obtained by using system monitoring tools (such as Nagios, Zabbix, and Prometheus) to record the node's operating status and failure time (i.e., by recording the node's operating status change log, including startup and failure time, in the log record), and error is the total operation time of the ith radar node, and its value is obtained by using system management tools (such as system event logs and monitoring software) to track the node's startup and shutdown events;

[0154] By networking each radar through the radar networking module, the detection capability of stealth ships is further enhanced, ensuring that the entire system has the advantages of high detection accuracy, comprehensive detection range and strong detection capability;

[0155] Optionally, the satellite reconnaissance module includes a satellite reconnaissance unit, a feedback unit, and a positioning request unit, wherein the satellite reconnaissance unit acquires high-resolution visible light images and multispectral images of the target monitoring area, the feedback unit transmits the images and data acquired by the satellite back to the ground control center, and the positioning request unit feeds back precise positioning information of the target monitoring area to the satellite to call the real-time visible light image and multispectral image of the target monitoring area;

[0156] The satellite reconnaissance unit includes a satellite, a high-resolution visible light camera, a multi-spectral camera, an infrared thermal imager and a memory. The satellite is equipped with the high-resolution visible light camera, the multi-spectral camera, the infrared thermal imager and the memory. The high-resolution visible light camera is used to take high-resolution visible light images of the target monitoring area to provide detailed visual information. The multi-spectral camera is used to take multi-spectral images of the target monitoring area to obtain information of different wavebands for analyzing target characteristics. The infrared thermal imager is used to detect and image the thermal radiation of the target monitoring area to obtain temperature distribution information. The memory is used to store the high-resolution visible light images of the high-resolution visible light camera, the multi-spectral images acquired by the multi-spectral camera, and the thermal radiation data acquired by the infrared imager.

[0157] The feedback unit includes a data transmission antenna, a radio frequency transceiver, a data encoder and modulator, a data compressor, and a communication controller. The data transmission antenna is responsible for sending and receiving signals for data transmission. The radio frequency transceiver is used for sending and receiving radio waves, converting digital signals into radio frequency signals, and performing reverse conversion (reverse conversion refers to the process of converting radio frequency signals back into digital signals). The data encoder and modulator encodes and modulates data for transmission on a wireless channel. The data compressor compresses images and data to reduce the amount of transmitted data and improve transmission efficiency. The communication controller manages the data transmission process, controls the sending and receiving of data, and processes the transmission protocol.

[0158] In the feedback unit, there is a clear data flow relationship between the components, forming a coordinated and consistent workflow; specifically, the communication controller is responsible for overall scheduling and management, first receiving and scheduling the data to be sent; the data is processed by the data compressor to reduce the data volume to improve transmission efficiency; the compressed data is passed to the data encoder and modulator, where the data is encoded to increase the reliability of transmission and modulated into a signal form suitable for wireless transmission;

[0159] Next, these modulated signals are transmitted to the RF transceiver; the RF transceiver converts the modulated signals into RF signals for wireless transmission through the data transmission antenna; at the same time, the RF transceiver also receives the external radio wave signals received from the data transmission antenna and converts them back into modulated signals, which are then passed back to the data encoder and modulator for demodulation and decoding, and restored to digital signals;

[0160] Throughout the entire process, the communication controller continuously monitors and manages each step of data transmission to ensure that data can be smoothly and reliably processed through multiple links such as compression, encoding, modulation, transmission, reception, demodulation and decoding, and finally reach the target node; this process fully reflects the close cooperation and interdependence between various components, forming a complete and closed data transmission link;

[0161] The RF transceiver is used to send and receive radio waves, convert digital signals into RF signals, and perform the opposite conversion. Specifically, this includes the following two main steps:

[0162] Conversion of digital signals to RF signals (modulation): This is the process of converting digital signals into RF signals that can be transmitted over wireless channels;

[0163] RF to digital conversion (demodulation): This is the process of converting the received RF signal back into a processable digital signal;

[0164] Optionally, the positioning request unit includes a positioning requester and a data receiver, the positioning requester sends a positioning request of the target monitoring area to the satellite, and the data receiver is used to receive a real-time visible light image and a multispectral image of the target area fed back by the satellite;

[0165] Optionally, before the positioning requester sends the positioning request of the target monitoring area to the satellite, it is necessary to pre-process the positioning data, and the pre-processing step includes:

[0166] S21, initialization and signal reception:

[0167] Start the GNSS receiver to receive navigation satellite signals, initialize the positioning requester, and prepare to receive and process positioning data;

[0168] S22. Calculate the exact position:

[0169] The GNSS receiver receives signals from navigation satellites to determine the real-time position of the target monitoring area, wherein the real-time position includes longitude and latitude;

[0170] S23. Calling satellite images:

[0171] Based on accurate real-time information, the positioning requester sends a positioning request to the satellite image acquisition system, calls up high-resolution visible light images and multispectral images of the corresponding location, and prepares to transmit them back to the ground;

[0172] When the satellite images are dispatched, the satellite images (including visible light images and multi-spectral images) are transmitted back to the ground through the feedback unit, and the stealth ships in the target area are monitored;

[0173] Through the cooperation between the radar networking module and the satellite reconnaissance module, the monitoring capability of stealth ships is further enhanced, ensuring that the entire system has the advantages of wide monitoring range, strong monitoring capability, good monitoring accuracy and high intelligence;

[0174] Optionally, the data fusion module includes a data processing unit and a fusion analysis unit, wherein the data processing unit processes the shape data of the stealth ship, and the high-resolution visible light image and multi-spectral image of the target monitoring area to form a multi-dimensional data vector, and the fusion analysis unit fuses the multi-dimensional data vector processed by the data processing unit to evaluate the target monitoring area to form an evaluation result;

[0175] Optionally, the data processing unit processes the shape data of the stealth ship, and the high-resolution visible light image and multi-spectral image of the target monitoring area according to the following steps:

[0176] S11, collecting radar data of the radar networking module;

[0177] The radar data collected by the radar networking module includes but is not limited to the following:

[0178] Radar cross section, shape profile data, indicating material properties;

[0179] The radar cross-section area measures the reflection characteristics of the target in different frequency bands by transmitting and receiving radar signals in multiple frequency bands, and collects RCS values ​​at different frequencies: RCS1, RCS2, and RCS3;

[0180] The radar cross section is calculated according to the following steps:

[0181] Radar signal transmission: transmit radar signals in multiple frequency bands;

[0182] Signal reception and processing: Receive radar signals reflected by the target and measure RCS values ​​in different frequency bands;

[0183] Data recording: record the RCS value in each frequency band;

[0184] The radar cross-sectional area index of the reflection area of ​​different bands is calculated according to the following formula:

[0185] ;

[0186] Where RCS(f) is the radar cross-sectional area index at frequency f, where RCS is a physical quantity used to measure the reflection characteristics of the target to the radar wave, E ref is the electric field strength reflected by the target, Einc is the incident electric field intensity, σ is the geometric cross-sectional area of ​​the target, and its value is calculated according to the following formula:

[0187] ;

[0188] Where P ref is the power reflected from the target received by the radar at frequency f, P inc is the radar transmission power at frequency f, R is the distance between the target and the radar, G t and G r are the gains of the transmitting and receiving antennas, respectively, and λ is the wavelength of the radar signal;

[0189] According to the above formula, calculate the radar cross-sections RCS1, RCS2, and RCS3 of three different bands;

[0190] The shape contour data uses high-resolution radar imaging technology to obtain the target's outline and extract shape features ShapeR1 and ShapeR2;

[0191] Specifically, the steps of extracting shape features ShapeR1 and ShapeR2 include:

[0192] S111. A high-resolution radar (such as synthetic aperture radar, SAR) will be used to generate a radar image of the target;

[0193] Use synthetic aperture radar (SAR) to generate high-resolution radar images of a target; SAR collects multiple radar echoes from a target area and processes them to create an image;

[0194] ;

[0195] Where I(x,y) is the radar image in the spatial domain, which is a graph of the radar reflection intensity or reflectivity at each location in the target area, and S(f x ,f y ) is the radar echo data in the frequency domain. The specific data is collected by radar. −1 is the inverse Fourier transform, is the inverse Fourier transform of the radar echo data in the frequency domain;

[0196] S112, extracting the outer contour of the target using an image processing algorithm (edge ​​detection algorithm);

[0197] Edge detection is used to extract the outer contour of the target; the Canny edge detection algorithm can be used:

[0198] ;

[0199] Where: E(x,y) is the binary image after edge detection, Canny(I(x,y)) is the edge image obtained by applying the Canny edge detection algorithm to the input image I(x,y);

[0200] S113, extracting shape features from the contour, wherein the shape features include: perimeter and area;

[0201] Perimeter:

[0202] Use a contour detection algorithm (such as findContours in OpenCV) to find the contour of the edge, then calculate the perimeter:

[0203] ;

[0204] Where n is the total number of points on the contour, (x i ,y i ) is the coordinate of the ith point, (x i−1 ,y i−1 ) is the coordinate of the i−1th point;

[0205] Area:

[0206] The area is calculated as follows:

[0207] ;

[0208] Where n is the total number of points on the contour, (x i ,y i ) is the coordinate of the ith point, (x i+11 ,y i+1 ) is the coordinate of the i+1th point, where the n+1th point coincides with the 1st point, that is: (x i+11 ,y i+1 )= (x1,y1);

[0209] The area of ​​the polygon is calculated by solving the difference product between the coordinates of each vertex and the coordinates of its adjacent vertices;

[0210] S114, taking the normalized perimeter and area as shape features ShapeR1 and ShapeR2, specifically:

[0211] Perimeter = ShapeR1;

[0212] Area = ShapeR2;

[0213] Surface material characteristics use polarization radar to analyze the polarization response characteristics of the material, and extract material characteristics through the polarization change of the reflected signal: Material1, Material2;

[0214] The material characteristics are obtained by processing according to the following steps, specifically:

[0215] 1) Transmitting polarized radar signals

[0216] Polarimetric radar systems can transmit radar signals with different polarization modes, such as horizontal polarization (H) and vertical polarization (V);

[0217] By transmitting these polarization signals, the reflection characteristics of the target material in different polarization modes can be obtained;

[0218] 2) Receive polarized radar signals

[0219] Receive the polarized radar signal reflected from the target and record the strength of the received horizontal and vertical polarization components;

[0220] 3) Calculate Stokes parameters

[0221] Stokes parameters are a set of parameters that describe the polarization state of electromagnetic waves and are often used to analyze polarization characteristics. Stokes parameters include:

[0222] S0: Total strength, satisfying:

[0223] ;

[0224] S1: The difference between the horizontal and vertical polarization components, satisfying:

[0225] ;

[0226] S2: The cosine phase difference of the orthogonal polarization components satisfies:

[0227] ;

[0228] S3: The sinusoidal phase difference of the orthogonal polarization components satisfies:

[0229] ;

[0230] In the above formula, I H and I V are the intensities of the horizontal and vertical polarization components, respectively. σ is the polarization phase difference, which is the phase difference between the two polarization modes (such as horizontal polarization and vertical polarization) of the radar signal after it is reflected by the target.

[0231] 4) In this embodiment, the total intensity S0 and the difference S1 between the horizontal and vertical polarization components are used as the material characteristics of Material1 and Material2, that is:

[0232] Material1 = S0;

[0233] Material2 = S1;

[0234] S12, collecting image data of satellite reconnaissance module;

[0235] The data collected by the satellite reconnaissance module include but are not limited to the following: high-resolution visible light images, multi-spectral images and thermal images;

[0236] The high-resolution visible light image obtains a high-resolution visible light image of the target monitoring area and extracts shape features of the visible light image: ShapeV1, ShapeV2, specifically:

[0237] Extract shape features of visible light images: ShapeV1, ShapeV2 according to the following steps:

[0238] Image acquisition: Use a high-resolution camera to obtain visible light images of the target monitoring area;

[0239] Image processing: pre-process the image, such as denoising, contrast enhancement, etc.;

[0240] Edge detection: Use an edge detection algorithm to extract the outer contour of the target. In this embodiment, the Canny edge detection algorithm is used to extract the edge of the target;

[0241] Feature extraction: extracting shape features from the contour. In this embodiment, the shape features are aspect ratio and circumscribed circle radius;

[0242] The circumscribed circle radius is calculated according to the following formula:

[0243] ;

[0244] Where C is the set of all points on the target contour {(x1, y1), (x2, y2), …, (x n ,y n )}, (x i ,y i ) is the i-th point on the target contour, (x c ,y c ) are the coordinates of the center of the circumscribed circle, and the center of the circumscribed circle is determined according to the following steps:

[0245] Initialize the minimum circumscribed circle: randomly select a point as the initial center of the circle with a radius of zero;

[0246] Iteratively update the center and radius: check whether all points are within the current circle;

[0247] If a point is not in the current circle, update the minimum circumscribed circle with the point as the center (using the point not in the current circle as the new center can ensure that after each iteration, the new circle can better approximate the minimum circumscribed circle that eventually contains all points. This method ensures that the circle finally calculated is the minimum circle that can cover all contour points);

[0248] Gradually expand the radius of the circle until all points are inside the circle;

[0249] Output result: The obtained center of the circle (x c ,y c ) and radius Radius are the minimum circumscribed circle of the target contour;

[0250] The aspect ratio of the shape feature is calculated according to the following formula:

[0251] ;

[0252] In the formula, Height is the length of the target contour, and Width is the width of the target contour;

[0253] Determine the width and height of the target contour by calculating the minimum enclosing rectangle of the contour point set;

[0254] Assume that the target contour point set is {(x1, y1), (x2, y2), …, (x n ,y n )}, then the width and height of the bounding box can be calculated by the following formula:

[0255] Width: Width=x max -x min ;

[0256] Height: Height=y max −y min ;

[0257] Where: x max and min are the maximum and minimum values ​​of the contour point set on the x-axis, and y max and min are the maximum and minimum values ​​of the contour point set on the y-axis respectively;

[0258] The aspect ratio and the circumscribed circle radius are used as shape features of visible light images: ShapeV1, ShapeV2;

[0259] Multispectral imaging obtains images of the target area in different spectral bands and extracts multispectral reflectance features: Spectral1, Spectral2, Spectral3. Specifically:

[0260] Extraction of multispectral reflectance features is determined according to the following steps:

[0261] Image acquisition: Use a multispectral camera to obtain images of the target area in different spectral bands;

[0262] Image processing: preprocess the images of each band;

[0263] Feature extraction: calculate the reflectance characteristics of different bands;

[0264] Where, for each band i, the reflectivity is calculated:

[0265] ;

[0266] In the formula, I i is the image pixel value of band i, I dark，i is the dark current correction value of the ith band, I white,i is the white reference correction value of the ith band;

[0267] Among them, the dark current correction value (dark current refers to the signal intensity output by the image sensor in the absence of light; due to the noise and characteristics of the sensor itself, the sensor will also generate current in a completely dark environment, which is the dark current) is carried out according to the following steps:

[0268] Shooting in total darkness: Place the image sensor in a completely dark environment and ensure that no light enters the sensor (preparation is to turn off all light sources and ensure that the sensor is in a completely dark environment);

[0269] Record output signal: record the output signal strength of the image sensor in a dark environment, usually taking the average value over multiple shots to reduce the influence of random noise (take multiple dark images (e.g. 10) using the image sensor), that is, take multiple dark images for each band in an environment without light;

[0270] Calculate the average value: The average signal intensity is the dark current (I dark，i );

[0271] Among them, the dark current correction value I of the i-th band dark，i The following formula is used for calculation:

[0272] ;

[0273] In the formula, I dark,ij is the average pixel value of the jth dark image in the ith band, and N is the number of images;

[0274] The white reference correction value refers to the signal intensity output by the image sensor when illuminated by a standard white reference with a known reflectivity close to 100%. This value is used to correct for the non-uniformity of the light source and the non-linear response of the sensor.

[0275] The whiteboard correction value is determined according to the following steps:

[0276] Standard white board illumination: Aim the image sensor at a standard white board and shoot under a uniform light source;

[0277] Record output signal: record the output signal intensity of the image sensor under the illumination of the whiteboard, usually taking the average value over multiple shots to ensure accuracy;

[0278] Calculate the average value: The average signal strength is the white plate correction value (I white,i );

[0279] Standard white board illumination: Aim the image sensor at a standard white board and shoot under a uniform light source;

[0280] Calculate the white correction value for each band:

[0281] ;

[0282] In the formula, I white，ij is the average pixel value of the jth whiteboard image in the i-th band, and N is the number of images;

[0283] According to the above method, the reflectance characteristics of the three spectral bands Spectral1, Spectral2, and Spectral3 are calculated, wherein the three spectral bands are all different;

[0284] Thermal imaging obtains infrared thermal imaging data of the target area and extracts thermal features: Thermal1, Thermal2. Specifically:

[0285] Thermal imaging obtains infrared thermal imaging data of the target area according to the following steps:

[0286] Image acquisition: Use an infrared thermal imager to obtain thermal imaging data of the target area;

[0287] Image processing: pre-processing of thermal imaging data;

[0288] Feature extraction: Calculate the features of thermal imaging data. In this embodiment, the average temperature and temperature distribution are used as thermal features Thermal1 and Thermal2.

[0289] ;

[0290] Where T is the temperature value matrix in the thermal imaging data, mean(T) is the mean value of the temperature value, and std(T) is the standard deviation of the temperature value;

[0291] The mean temperature value mean(T) is calculated according to the following formula:

[0292] ;

[0293] Where, T ij is the temperature value of the i-th row and j-th column in the temperature matrix, M is the number of rows in the temperature matrix, and D is the number of columns in the temperature matrix;

[0294] The standard deviation of temperature values ​​std(T) is calculated according to the following formula:

[0295] ;

[0296] Where, T ij is the temperature value of the i-th row and j-th column in the temperature matrix, M is the number of rows in the temperature matrix, and D is the number of columns in the temperature matrix;

[0297] S13, performing data standardization on the radar data and image data collected in step S11 and step S12;

[0298] In this embodiment, the radar data includes: RCS1, RCS2, RCS3, ShapeR1, ShapeR2, Material1, Material2;

[0299] The image data includes: ShapeV1, ShapeV2, Spectral1, Spectral2, Spectral3, Thermal1, Thermal2;

[0300] The standardization process for the radar cross-sections RCS1, RCS2, and RCS3 of three different bands includes:

[0301] ;

[0302] Where RCS1' is the normalized radar cross section, RCS1 is the radar cross section of the first radar band, and RCS min is the minimum value of the radar cross-section of the first radar band in the historical data, RCS maxis the maximum value of the radar cross-section of the first radar band in the historical data;

[0303] Similarly, RCS2' and RCS3' refer to the calculation method of RCS1', ​​which will not be described here one by one;

[0304] In addition, the extracted shape features ShapeR1 and ShapeR2 are standardized (the perimeter Perimeter is regarded as the shape feature ShapeR1, and the area Area' is regarded as the shape feature ShapeR2);

[0305] ;

[0306] Where Perimeter' is the normalized perimeter, Perimeter is the perimeter extracted in real time, and Perimeter min The minimum perimeter extracted from historical data, Perimeter max is the maximum circumference extracted from historical data;

[0307] ;

[0308] In the formula, Area' is the standardized area, Area is the area extracted in real time, and Area min Area is the minimum area extracted from historical data. max It is the maximum area extracted from historical data;

[0309] Treat the normalized perimeter Perimeter' as ShapeR1'. Similarly, treat the normalized area Area' as ShapeR2'.

[0310] The material features of Material1 and Material2 are standardized according to the following formula. In this embodiment, the total intensity S0 and the difference S1 between the horizontal and vertical polarization components are used as the material features of Material1 and Material2), specifically:

[0311] ;

[0312] Where S0 is the total intensity of the polarization state of the electromagnetic wave, S 0，min is the minimum total intensity value recorded in the historical data, S 0，max is the maximum total intensity recorded in the historical data;

[0313] Similarly, ;

[0314] Where S1 is the difference between the horizontal and vertical polarization components of the electromagnetic wave, S 1，minis the minimum total intensity value recorded in the historical data, S 1，max is the maximum total intensity recorded in the historical data;

[0315] For the shape features of visible light images: ShapeV1, ShapeV2 (where the aspect ratio and the circumscribed circle radius are used as the shape features of the visible light images: ShapeV1, ShapeV2):

[0316] ;

[0317] In the formula, ShapeV1 is the aspect ratio of the visible light image, ShapeV 1，min is the minimum aspect ratio of the visible light image in the historical data, ShapeV 1，max is the maximum value of the aspect ratio of the visible light image in the historical data;

[0318] ;

[0319] In the formula, ShapeV1 is the radius of the circumscribed circle of the visible light image, ShapeV 1，min is the minimum value of the circumscribed circle radius of the visible light image in the historical data, ShapeV 1，max is the maximum value of the circumscribed circle radius of the visible light image in the historical data;

[0320] For multispectral reflectance features: Spectral1, Spectral2, Spectral3 (using Spectral1' as an example):

[0321] ;

[0322] Where Spectral1 is the multispectral reflectance characteristic value of the first band, Spectral 1，min is the minimum value of the multispectral reflectance characteristic value of the first band in the historical data. 1，max is the maximum value of the multispectral reflectance characteristic value of the first band in the historical data;

[0323] Similarly, the multispectral reflectance characteristic value Spectral2' of the second band and the multispectral reflectance characteristic value Spectral3' of the third band can refer to the calculation method of Spectral1 (i.e., the above formula), which will not be repeated here;

[0324] For thermal features Thermal1 and Thermal2, the normalization process is calculated according to the following formula (with average temperature and temperature distribution as thermal features Thermal1 and Thermal2):

[0325] ;

[0326] Where, mean(T) is the average temperature value, mean(T) min is the minimum value of the average temperature in the historical data, mean(T) max is the maximum value of the average temperature in the historical data;

[0327] Similarly, ;

[0328] Where std(T) is the standard deviation of the temperature value, std(T) min is the minimum value of the temperature standard deviation in the historical data, std(T) max is the maximum value of the standard deviation in historical data;

[0329] Other radar data and image data are calculated by referring to the above standardized formulas, which will not be described here one by one;

[0330] In this embodiment, the standardized radar data includes: RCS1', ​​RCS2', RCS3', ShapeR1', ShapeR2', Material1', Material2';

[0331] The standardized image data includes: ShapeV1', ShapeV2', Spectral1', Spectral2', Spectral3', Thermal1', Thermal2';

[0332] S14, combining the standardized feature data into a multi-dimensional data vector;

[0333] R=[RCS1',RCS2',RCS3',ShapeR1',ShapeR2',Material1',Material2',ShapeV1'];

[0334] S=[ShapeV2', Spectral1', Spectral2', Spectral3', Thermal1', Thermal2'];

[0335] Among them, R and S represent the multidimensional data vectors obtained from the radar networking module and the satellite reconnaissance module respectively. The data of the i-th dimension of the multidimensional data vector R of the radar networking module is R i , the data of the i-th dimension of the multidimensional data vector S obtained by the satellite reconnaissance module is S i ;

[0336] The multidimensional data vector R of the radar networking module and the multidimensional data vector S obtained by the satellite reconnaissance module calculate the weighted distance d (R, S) between the multidimensional data vector R of the networking module and the multidimensional data vector S obtained by the satellite reconnaissance module:

[0337] ;

[0338] In the formula, R i is the data of the i-th dimension of the multidimensional data vector R of the radar networking module, S i is the data of the i-th dimension of the multidimensional data vector S acquired by the satellite reconnaissance module; m is the total dimension of the multidimensional data vector;

[0339] Optionally, the fusion analysis unit obtains a multidimensional data vector and calculates a risk index Worn according to the following formula:

[0340] ;

[0341] Where d(R, S) is the weighted distance between the multidimensional vector R and the multidimensional vector data S;

[0342] If the danger index Worn is greater than or equal to the monitoring threshold Danger set by the system, it indicates that there is a threat of ship stealth in the target area;

[0343] If the danger index Worn is less than the monitoring threshold Danger set by the system, it indicates that the threat of ship stealth in the target area is low;

[0344] The monitoring threshold Danger set by the system is set by the system or the administrator according to the actual situation. This is a technical means well known to those skilled in the art. Those skilled in the art can refer to relevant technical manuals to learn about the technology, so it will not be described in detail in this embodiment.

[0345] In this embodiment, through the cooperation between the radar networking module, the data fusion module, and the satellite reconnaissance module, the stealth of the ship can be monitored, ensuring that the entire system has the advantages of strong detection capability, good detection data fusion effect, high detection accuracy and high intelligence;

[0346] Optionally, the early warning module includes an early warning unit and an interactive unit, the early warning unit obtains the evaluation result and the monitoring threshold Danger set by the system, and compares them to determine the early warning prompt, and the interactive unit interactively prompts the early warning prompt to the manager;

[0347] The early warning unit includes a data receiver and a comparator. The data receiver receives the evaluation result and the monitoring threshold Danger set by the system. The comparator compares the received evaluation result and the monitoring threshold Danger set by the system and generates a corresponding early warning signal.

[0348] In this embodiment, different warning signal prompts are provided:

[0349] If the evaluation result < 0.5 * Danger, the warning green signal is triggered (normal);

[0350] Among them, the green warning signal indicates that the ship is in normal condition and no action is needed, that is, the assessment result is far below the monitoring threshold, indicating that the current ship operation risk is good;

[0351] If: 0.5 * Danger ≤ Evaluation Result < Danger, a yellow signal (warning) is triggered;

[0352] Among them, the yellow warning signal indicates a ship status warning, please pay attention to monitoring, that is, the assessment result is close to the monitoring threshold, you need to be vigilant and continue to monitor the ship status;

[0353] If the evaluation result ≥ Danger is met, a red signal (serious warning) is triggered;

[0354] Among them, the red warning signal indicates that the ship's status is seriously warned and immediate action is required, that is, the assessment result exceeds or equals the monitoring threshold, indicating that the ship is at serious risk and immediate measures need to be taken to deal with it;

[0355] The interactive unit includes an interactive display and a communicator, wherein the interactive display is for the manager or the operator to view, and the communicator is used to obtain the warning level of the warning unit and transmit it to the interactive display for the manager or the operator to view;

[0356] Through the mutual cooperation between the early warning module and the data fusion module, the ship manager can dynamically monitor the stealth ship, ensuring that the entire system has the advantages of good interactive ability, strong intelligence and high detection and early warning capabilities.

[0357] Embodiment 2: This embodiment should be understood to include all the features of any of the above embodiments, and further improve upon them. Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5As shown, the ship stealth monitoring system also includes a trend tracking module, which evaluates the posture of the stealth ship according to the position data collected by the radar networking module, and issues an early warning prompt to the manager or operator according to the evaluation result;

[0358] The trend tracking module includes a posture evaluation unit and a danger trigger unit, wherein the posture evaluation unit evaluates the posture index of the stealth ship according to the position of the stealth ship, and the danger trigger unit warns the movement trend of the stealth ship according to the posture index;

[0359] The posture evaluation unit calculates the posture evaluation index PI according to the following steps:

[0360] S51. Data Collection

[0361] Collect the bow and stern position data of the stealth ship from the radar networking module;

[0362] The bow position is p bow =(x bow ,y bow );

[0363] The stern position is p stern =(x stern ,y stern );

[0364] The location of this ship or monitoring point: p own =(x own ,y own );

[0365] S52. Calculate the ship's heading vector

[0366] The ship's direction vector (Direction Vector, d) can be calculated from the positions of the bow and stern:

[0367] ;

[0368] S53. Calculate the angle between the orientation vector and the relative vector

[0369] Calculate the angle θ between the facing vector d and the relative vector r:

[0370] ;

[0371] Where d⋅r is the dot product of the facing vector and the relative vector, is the magnitude of the orientation vector, is the magnitude of the relative vector;

[0372] The dot product d⋅r of the facing vector and the relative vector is calculated according to the following formula: ;

[0373] The magnitude of the orientation vector Calculate according to the following formula: ;

[0374] Relative vector magnitude Calculated according to the following formula: ;

[0375] Among them, the components of the relative vector r are: ;

[0376] S54, calculating a velocity vector V, wherein the velocity vector represents the velocity of the ship, and is calculated according to the following formula:

[0377] ;

[0378] In the formula, x bow,current and bow,current are the horizontal and vertical position coordinates of the bow of the stealth ship at the current time point; bow,previous and bow,previous are the horizontal and vertical position coordinates of the bow of the stealth ship at the previous time point, respectively, and Δt is the time interval between two adjacent time points;

[0379] S55, calculate the angle β between the velocity vector v and the relative vector r:

[0380] ;

[0381] Where V⋅r is the dot product of the velocity vector and the relative vector, is the magnitude of the velocity vector, is the magnitude of the relative vector;

[0382] Among them, the dot product V⋅r of the velocity vector and the relative vector is calculated according to the following formula:

[0383] ;

[0384] The magnitude of the velocity vector Calculated according to the following formula:

[0385] ;

[0386] The danger trigger unit obtains the angle θ between the orientation vector d and the relative vector r, and the angle β between the velocity vector v and the relative vector r. If the angle θ is satisfied: cos(θ)≈1, it means that the stealth ship is moving toward the ship (or detecting the point);

[0387] If cos(θ)≈−1 is satisfied, it means that the stealth ship is far away from the ship (or detection point);

[0388] If cos(θ)≈0 and cos(β)≈1, it means that the stealth ship and the host ship (or detection point) are traveling in parallel and in the same direction;

[0389] If cos(θ)≈0 and cos(β)≈−1, it means that the stealth ship and the host ship (or detection point) are moving in parallel and in opposite directions;

[0390] The danger triggering unit prompts the operator or administrator with a warning prompt of the corresponding situation through the interactive unit;

[0391] In this embodiment, the posture assessment unit and the danger trigger unit cooperate with each other, so that the movement trend of the stealth ship can be accurately monitored, ensuring that the entire system has the advantages of strong detection capability, reliable detection accuracy, high intelligence, and good interaction capability;

[0392] At the same time, through the mutual cooperation between the trend tracking module and the radar networking module, the posture state of the stealth ship can be monitored, ensuring that the entire system has the advantages of strong monitoring capability, comprehensive detection range, reliable detection accuracy and high intelligence.

[0393] The contents disclosed above are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made using the contents of the present invention specification and drawings are included in the protection scope of the present invention. In addition, the elements therein can be updated as technology develops.

Claims

1. A ship stealth monitoring system based on multi-band radar networking technology, characterized in that: The ship stealth monitoring system includes a radar networking module, a data fusion module, a satellite reconnaissance module, and an early warning module. The radar networking module uses at least one radar band to collect the shape data of the stealth ship to monitor the stealth ship in the target monitoring area. The satellite reconnaissance module is used to collect satellite images of the target monitoring area to determine the monitoring data of the target monitoring area. The data fusion module integrates the shape data of the stealth ship collected by the radar networking module and the monitoring data of the target monitoring area collected by the satellite reconnaissance module, and evaluates the target monitoring area to form an evaluation result to monitor the stealth ship. The early warning module interactively warns the manager of the evaluation result. The radar networking module includes a radar node unit, a data transmission and communication unit, and a time synchronization unit. The time synchronization unit synchronizes the time of at least one radar band equipped with each radar node. The radar node unit performs communication connection with at least one radar band equipped with each radar node to form a distributed radar network. The data transmission and communication unit is used for data transmission and communication between at least one radar band equipped with each radar node. The radar node unit communicates with at least one radar band equipped with each radar node in a polling manner, and triggers each radar node to detect the shape data of the stealth ship with at least one radar band; The polling method of the radar node unit includes the following steps: S1, initialization phase: The master control node starts, initializes all radar nodes, and establishes an initial communication connection. Each radar node registers with the master control node, reports its own status and capabilities, and all nodes are synchronized through the time synchronization unit; The master control node refers to the core node responsible for managing and coordinating the entire radar network; S2. Polling task allocation: The master control node randomly selects several radar nodes for polling task allocation according to a preset algorithm. According to the detection requirements of the current sea area and the node status, the master control node dynamically adjusts the polling tasks of each node and distributes specific detection tasks to the selected radar nodes. The detection tasks include the priority of the detection target, the polling frequency, and the data upload requirements; S3, polling execution: the radar nodes assigned tasks perform detection tasks according to the instructions issued by the master control node and collect target data. After completing the detection task, each radar node uploads the collected data to the master control node according to the preset time interval or conditions. Each node regularly reports its own status to the master control node, including power, detection results, and fault conditions; S4, master control node processing: the master control node receives the data uploaded by each radar node, and dynamically adjusts the polling strategy according to the real-time data and environmental changes. The master control node monitors the status of each node and handles it in time when an abnormal situation is found; S5, Feedback and Optimization: The master control node evaluates the detection results of each radar node and sends feedback information to each node to guide the next round of detection tasks. Based on historical data and current detection results, the master control node continuously optimizes the random polling algorithm.

2. According to claim 1, the ship stealth monitoring system based on multi-band radar networking technology is characterized in that: The satellite reconnaissance module includes a satellite reconnaissance unit, a feedback unit, and a positioning request unit. The satellite reconnaissance unit obtains high-resolution visible light images and multi-spectral images of the target monitoring area. The feedback unit transmits the images and data obtained by the satellite back to the ground control center. The positioning request unit feeds back precise positioning information of the target monitoring area to the satellite to call the real-time visible light images and multi-spectral images of the target monitoring area.

3. The ship stealth monitoring system based on multi-band radar networking technology according to claim 2 is characterized in that: The data fusion module includes a data processing unit and a fusion analysis unit. The data processing unit processes the shape data of the stealth ship, and the high-resolution visible light image and multi-spectral image of the target monitoring area to form a multi-dimensional data vector. The fusion analysis unit fuses the multi-dimensional data vector processed by the data processing unit to evaluate the target monitoring area and form an evaluation result.

4. The ship stealth monitoring system based on multi-band radar networking technology according to claim 3 is characterized in that: The data processing unit processes the shape data of the stealth ship, and the high-resolution visible light image and multi-spectral image of the target monitoring area according to the following steps: S11, collecting data of radar networking module; S12, collecting data from satellite reconnaissance modules; S13, performing data standardization on the data collected in step S11 and step S12; S14. Combining the standardized feature data into a multi-dimensional data vector.

5. The ship stealth monitoring system based on multi-band radar networking technology according to claim 4 is characterized in that: The fusion analysis unit obtains a multidimensional data vector and calculates the risk index Worn according to the following formula: ； Where d(R, S) is the weighted distance between multidimensional vector R and multidimensional vector S; If the danger index Worn is greater than or equal to the monitoring threshold Danger set by the system, it indicates that there is a threat of ship stealth in the target area.

6. The ship stealth monitoring system based on multi-band radar networking technology according to claim 5 is characterized in that: The positioning request unit includes a positioning requester and a data receiver. The positioning requester sends a positioning request for the target monitoring area to the satellite, and the data receiver is used to receive real-time visible light images and multi-spectral images of the target area fed back by the satellite.

7. The ship stealth monitoring system based on multi-band radar networking technology according to claim 6 is characterized in that: Before the positioning requester sends the positioning request of the target monitoring area to the satellite, it is necessary to pre-process the positioning data, and the pre-processing steps include: S21, initialization and signal reception Start the GNSS receiver to receive navigation satellite signals, initialize the positioning requester, and prepare to receive and process positioning data; S22. Calculate the exact position The GNSS receiver receives signals from navigation satellites to determine the real-time position of the target monitoring area, wherein the real-time position includes longitude and latitude; S23. Calling satellite images Based on precise real-time information, the positioning requester sends a positioning request to the satellite image acquisition system, calls up high-resolution visible light images and multispectral images of the corresponding location, and prepares to transmit them back to the ground.

8. The ship stealth monitoring system based on multi-band radar networking technology according to claim 7 is characterized in that: The early warning module includes an early warning unit and an interactive unit. The early warning unit obtains the evaluation result and the monitoring threshold Danger set by the system, and compares them to determine the early warning prompt. The interactive unit interactively prompts the early warning prompt to the manager.

9. The ship stealth monitoring system based on multi-band radar networking technology according to claim 8 is characterized in that: The at least one radar band includes radars of low frequency, medium frequency and high frequency bands.

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