207 results about "Passive detection" patented technology

A network mapping behavior anomaly detection method and system based on machine learning is provided. The method includes: collecting dual-source traffic data, generating a structured log data set through dual-source log fusion engine; performing subgraph matching calculation to obtain a mapping behavior deviation degree; generating communication data containing a watermark identifier in a session corresponding communication path; verifying whether attack events carry the watermark identifier; generating a network mapping behavior anomaly detection report. According to the disclosure, an adaptive attack behavior model is constructed through a multi-modal feature vector based on structured logs and a graph protocol mapping rule base, so that the cognitive robustness to protocol camouflage and path drift is fundamentally enhanced, a real-time verification chain of detection results is built, and traditional passive detection is transformed into self-proof active defense through cross verification of watermark carrying state and behavior trajectory.

The invention relates to the technical field of image processing, and discloses a backlight effect image edge enhancement method based on intelligent identification, which comprises the following steps of: judging a field environment type; performing global optimization on the original image based on a set environment perception type enhancement mechanism according to the judged field environment type, and outputting a global pre-processing image; constructing a backlight area segmentation model for the globally preprocessed image; outputting a local enhanced image; designing a structure perception type local adaptive threshold algorithm for the local enhanced image, and outputting a binary image keeping structural continuity; extracting an edge image of the binarized image through an edge detection algorithm, and optimizing a topological structure of a contour in the binarized image; and comparing with a wood template processing standard feature library, outputting an edge quality evaluation result and feeding back to a processing control system. Defect detection and machining control depth linkage is achieved, passive detection is changed into active optimization, and the production efficiency and the yield are improved.

The invention belongs to the technical field of distributed optical fiber sensing and low-altitude security and protection, and particularly relates to an unmanned aerial vehicle long-distance tracking method based on distributed optical fiber vibration sensing. The invention relates to an unmanned aerial vehicle vibration / electromagnetic fusion multi-mode identification method, a special installation formation design, a dispersion compensation and amplitude phase error compensation design, an unmanned aerial vehicle target vibration / electromagnetic fusion multi-mode identification design, a detection capability calculation method of an unmanned aerial vehicle vibration sound pressure-space attenuation law-distributed optical fiber processing equipment detection capability coupling model, a long-distance sensing optical cable and an electromagnetic monitoring node. According to the tracking method, a special optical fiber installation mode, dispersion compensation and phase error compensation of an original phase signal, a vibration / electromagnetic fusion multi-mode identification method, an unmanned aerial vehicle vibration sound pressure-space attenuation model-distributed optical fiber processing equipment detection capability corresponding method and other innovative work are adopted; a mature distributed optical fiber vibration sensing system is utilized to realize long-distance passive detection and target tracking of the low-slow-small unmanned aerial vehicle.

The invention relates to the technical field of energy-saving door and window machining, and particularly discloses an energy-saving door and window machining manipulator device which comprises a manipulator, a first guide rod, a recording mechanism and a clamping jaw. The recording mechanism is arranged at the top end of the right side of the mechanical arm, and the clamping jaw is arranged at the right end of the recording mechanism. The device effectively overcomes the defect that a traditional detection method cannot quantitatively evaluate the wear process of the sliding rail, can accurately record the change rule of the friction characteristic of the sliding rail under different opening and closing times, visually reflects the wear critical point through the fluctuation characteristic of the corrugated line, and can accurately predict the failure threshold value of a hardware system. Test data not only reveals the corresponding relation between sliding rail abrasion and opening and closing times, but also can provide a scientific basis for optimizing hardware material pairing and improving a lubricating scheme, the durability design level of energy-saving door and window products is remarkably improved, the technology span from passive detection to active prediction is achieved, and the method is suitable for popularization and application. And a durability test result has a higher engineering guidance value.

According to the intelligent frequency spectrum prediction system and method with the dynamic anti-interference function, through the lightweight AI prediction model deployed in the embedded terminal, prospective accurate prediction of the future frequency spectrum hole probability is achieved, and the time delay defect of traditional passive detection is overcome; through an interference identification and classification module, fine identification of an interference source is realized, and a basis is provided for a differential anti-interference strategy; dynamic global optimization of communication parameters is realized through fusion of a utility function decision mechanism of a prediction result, interference information and historical quality and GFSK / QPSK adaptive modulation switching, and optimal balance is obtained between noise immunity and spectrum efficiency; through maintenance of the pre-switching channel pool and a microsecond-level rapid switching mechanism, challenges brought by strong interference burst and high-speed moving scenes are effectively dealt with, and continuity and high reliability of communication are ensured.

The invention relates to the technical field of communication terminals, in particular to an ultra-long-distance aviation airborne 5G communication terminal, which comprises an opportunity signal passive detection module for monitoring multi-source opportunity signals in parallel by adopting a broadband multi-channel radio frequency front end, and performing time delay conjugate multiplication and integral operation on reference channel signals and detection channel signals, so as to obtain an opportunity signal passive detection module; and extracting a cross-correlation peak value. According to the method, the multi-dimensional demand vector is formed by analyzing the mirror image metadata of the virtual machine and the configuration parameters, and the multi-dimensional demand vector is compared with the capability vector of the heterogeneous micro-host item by item, so that the scheduling precision can be improved on the two aspects of architecture consistency and resource adaptability. In the matching logic, matching pairs which do not meet conditions are endowed with negative infinite values, invalid scheduling paths are eliminated, candidate matching pairs meeting the conditions are prioritized by calculating resource matching degrees and affinity scores and integrating the adaptation level of network bandwidth and storage performance, and therefore resource utilization efficiency and scheduling reasonability are guaranteed.

The invention relates to an intelligent analysis method and system for PCBA production data, and relates to the technical field of PCBA data analysis, and the method comprises the steps: collecting real-time production data in a PCBA production process, associating production batches, and forming a real-time production data sequence; constructing a parameter-defect knowledge graph based on historical production data; after a production defect signal is received, taking a production batch as an index, extracting corresponding real-time production data, taking a corresponding defect type node as a starting point for reverse reasoning, and calculating the contribution degree of each upstream node to the defect type node; and positioning a root cause node, determining a key process parameter node in combination with the type, and outputting a process adjustment suggestion. The method solves the problems that traditional PCBA defect analysis depends on experience judgment and lacks causal correlation analysis among different production key elements in production data, so that root cause positioning is fuzzy, the efficiency is low, passive detection is mostly carried out after defects occur, prevention is difficult to carry out in advance, and then the production quality control effect is affected.

The invention belongs to the technical field of distributed multi-sensor passive detection multi-target tracking. The invention provides a factor graph optimization passive detection multi-target tracking method based on a Gaussian mixture model. According to the embodiment of the invention, the multi-target batch number is distributed by constructing the distributed passive sensor cooperative coordinate system and combining the multi-target identity judgment result between the two sensors; calculating direction finding lines based on two-dimensional observation of a sensor, combining the direction finding lines of the same batch number, obtaining a multi-target position estimation point set through a least square method, and obtaining a multi-target coarse positioning point through weighted fusion; modeling by adopting a Gaussian mixture model, fusing measurement distribution characteristics, solving parameters through an expectation maximization algorithm, and completing solvable conversion of an optimization problem; a factor graph optimization model containing multiple factors is constructed, state estimation is achieved through sliding window optimization, and track association and state updating are completed in combination with the Mahalanobis distance and the Hungary algorithm; and the passive detection multi-target tracking performance of the distributed sensor is effectively improved.

The invention belongs to the technical field of radio countermeasure, and particularly relates to a distributed unmanned aerial vehicle active defense system which comprises an intelligent stationing dynamic optimization module, a multi-mode cognitive interference module, a high-precision three-level clock synchronization module and an elastic ad hoc network communication module which are used for supporting the overall function of the system. The distributed hardware equipment and the edge intelligent decision-making terminal are used for implementing a detection countering function at distributed layout positions; during specific operation and use, a distributed unmanned aerial vehicle active defense system can be provided for an important target with a large area and multiple important parts; the application problems that the arrangement position cannot be accurately selected, a large coverage blind area exists, the synchronous time service precision is low, communication networking is unreliable, serious self-interference is generated on electromagnetic and time service equipment of a partner, and the unmanned aerial vehicle countering effect is poor can be solved. And rapid and automatic passive detection and accurate interference of important targets with a large area and multiple important parts on the low-slow-small unmanned aerial vehicle are realized.

The invention relates to the technical field of drainage pipeline detection, and discloses a drainage pipeline structural defect detection system and method, a medium and a product, a controller actively constructs and generates a coded infrasonic wave signal, and it is ensured that the signal has stable frequency and amplitude characteristics; the infrasonic sound production module performs infrasonic wave emission and processing by using a coded infrasonic wave signal, and enables a low-frequency mechanical vibration signal to be stably propagated along a pipe wall, so that energy attenuation is reduced, and a long-distance pipe section which is difficult to touch in traditional passive detection is covered; the infrasound signal receiving module is used for detecting low-frequency mechanical vibration signals and converting analog signals into initial digital signals, and interference and distortion in the transmission process are reduced. Furthermore, by combining a Fourier transform method and a correlation coefficient analysis method, the problem of high misjudgment rate of a traditional single algorithm is avoided, accurate recognition of the structural defects of the pipeline is ensured, and the requirement for accuracy of structural defect detection of the drainage pipeline under complex working conditions is met.

The invention provides a full-automatic probe position inspection system for a probe card, and the system comprises a morphology collection module which is used for carrying out the three-dimensional morphology scanning of a probe group of the probe card, and obtaining the three-dimensional point cloud data of the probe group; the probe position analysis module is connected to the morphology acquisition module, comprises a machine learning model and is used for processing the three-dimensional point cloud data and generating probe position deviation data and probe health state scores; the compensation execution module is connected to the probe position analysis module and is used for scoring based on the probe position deviation data and the probe health state; calculating an optimal contact position, and driving the carrying platform to move to the position; and the verification module is used for verifying the accuracy of the needle position after the contact is executed at the optimal contact position. Passive detection, predictive maintenance and process closed-loop optimization are crossed, and the test yield, the equipment service life and the manufacturing process level are comprehensively improved through data driving.

The invention discloses a passive fault positioning system and method based on SRv6 data plane detection, and the method comprises the steps: deploying a data collection module at an SRv6 network node, passively collecting a passing data message, and adding detection information into the data message; analyzing an SRH extension head in the message to extract detection information, and filtering and processing the detection information to provide formatted data; summarizing detection information in the monitoring data of different nodes in different time periods, and performing association and fusion to form a to-be-analyzed data packet; according to the detection information in the to-be-analyzed data packet, performing fault positioning analysis by using a fault analysis method to lock a fault position; and performing root cause tracing based on the fault positioning position to obtain a fault cause, outputting a fault positioning result and linking a fault processing flow. According to the method, the existing information of the SRv6 data plane is multiplexed to realize non-overhead passive detection, and high-precision and real-time fault positioning of the SRv6 network is realized.

The embodiment of the invention provides a network surveying and mapping behavior anomaly detection method and system based on machine learning, and belongs to the technical field of network surveying and mapping behavior anomaly detection. The method comprises the steps of collecting double-source flow data, and generating a structured log data set through a double-source log fusion engine; performing sub-graph matching calculation to obtain a surveying and mapping behavior deviation degree; generating communication data containing the watermark identifier in a communication path corresponding to the session; and verifying whether an attack event carries the watermark identifier, and generating a network surveying and mapping behavior anomaly detection report. According to the method, the self-adaptive attack behavior model is constructed through the multi-modal feature vector based on the structured log and the graph protocol mapping rule base, the cognitive robustness of protocol camouflage and path drifting is fundamentally enhanced, a detection result real-time verification chain is established, and through cross verification of a watermark carrying state and a behavior track, the detection accuracy is improved. And traditional passive detection is converted into self-proof active defense.

The invention discloses an air outlet anti-leakage and anti-reverse-installation detection method based on image detection, and belongs to the technical field of optical detection. The method comprises the following steps: determining a data acquisition range of image detection in an air outlet area, and delimiting a detection area according to structural parameters of the air outlet; by dividing conventional and error-prone areas, adopting a differential detection strategy and combining a three-dimensional point cloud comparison technology, the unqualified product rate and the detection time consumption of air outlet installation are effectively controlled, the labor cost and the repair rate are reduced, a threshold value is dynamically optimized based on historical data, the recognition precision of a complex area is improved, misjudgment of a difficult point area is solved, and the detection efficiency is improved. Double improvement of detection precision and efficiency is realized; meanwhile, through a three-level risk signal mechanism, conversion from passive detection to active early warning is realized, the risk response time is shortened, the product quality stability is guaranteed, the downtime is reduced, the overall efficiency of a production line is improved, and collaborative optimization of risk management and control and production continuity is realized.

The invention discloses an online quality detection system based on artificial intelligence. The online quality detection system comprises a generative full-dimensional perception and cognition twinborn reproduction layer, an interpretable causal atlas and man-machine enhancement cognition layer, an alliance collaboration and credible value tracing layer and an autonomous evolution and scientific discovery layer. Through the online quality detection system based on artificial intelligence, the problems of passive detection, surface recognition, insufficient adaptability to dynamic changes and the like of a current online quality detection system can be solved, and the intelligent level of the online quality detection system is remarkably improved.

The invention belongs to the technical field of medical care informatics, and discloses a remote central monitoring system for community medical treatment, which comprises a data acquisition and integration module, a physiological state analysis engine and a monitoring information presentation module, and can construct a state-dependent baseline model cluster based on the activity state information of a user, according to the real-time activity state of the user in the monitoring period, the corresponding sub-model is dynamically selected to serve as a judgment reference, and then a continuously-changing physiological stability index is generated by comparing the multi-dimensional statistical characteristics of the real-time physiological data with the deviation degree of the reference; according to the method, by establishing an individualized physiological steady state judgment benchmark which is adaptively adjusted along with the activity situation, the capability of a monitoring system for continuously and quantitatively evaluating the overall stability of a human body as a complex system is improved, and the transformation from passive detection of discrete clinical events to state insight of a risk evolution process is realized.

The invention discloses a PCDN active detection method based on fusion of user behaviors and traffic features, which belongs to the technical field of active detection, and comprises the following steps: 1.1, building a simulation environment, collecting data, deploying a controllable PCDN service simulation environment comprising mainstream PCDN platform node equipment, a user terminal and detection equipment, the method comprises the following steps: non-inductively collecting network traffic and application layer behavior data through a bypass-deployed deep packet inspection engine and traffic mirroring equipment; according to the method, the specific user behavior characteristics of PCDN platform registration, node testing, settlement query and the like which are strongly associated with commercial settlement are extracted, and flow characteristic fusion judgment is combined, so that identification can be completed at the stage that the user registers and deploys PCDN nodes, and PCDN behaviors are found 7-10 days earlier than that of a traditional flow detection method; the problem of passive detection lagging is solved, and network risks caused by PCDN commercial behaviors in a home broadband can be monitored more timely.

The application discloses a mining behavior identification method combining active and passive detection technologies, comprising flow side identification, wherein the flow side identification comprises passive detection and active detection, and specifically comprises the following contents: obtaining packet features of mining pool miner communication through passive detection; constructing a packet to send to the mining pool, and analyzing and judging returned data; obtaining IP addresses of known mining pools through the passive detection method, obtaining the mining pool server fingerprint by using JARM, searching for IP addresses of the same fingerprint by using a space survey product, actively sending a request by constructing a data packet, analyzing a response result, and judging the belonging mining pool of the IP. The application combines the active and passive detection technologies, can independently detect possible mining machines and mining pools and mining behaviors while monitoring the flow features of the mining behaviors and the device resource occupation caused by the behaviors, and blocks the high-threat mining behaviors hidden deeply.

The invention relates to the technical field of equipment collaborative scheduling, and discloses a multi-equipment collaborative scheduling processing method and system for mobile transparent display equipment, and the method comprises the steps: collecting the real-time state data of a plurality of mobile transparent display equipment in an exhibition hall, and calculating the task compatibility data between the real-time state data and an exhibition task; deadlock risk prediction is carried out based on the real-time state data and the task compatibility data, and an inter-device space conflict probability and a task execution deadlock risk value are obtained; and performing cooperative control on the plurality of mobile transparent display devices based on the space conflict probability between the devices and the task execution deadlock risk value, and outputting a device movement instruction and a transparency coordination instruction. Continuous and stable operation of the multiple mobile transparent display devices in the exhibition hall is ensured, the optimal visual display effect is ensured, and meanwhile energy waste is avoided.

The application discloses a near-field strong interference suppression method based on spherical wave deconvolution beam forming positioning, which comprises the following steps: performing fine grid division on a near-field scanning area of a linear array; performing spherical wave focusing beam forming on the near-field scanning area and two-dimensional deconvolution beam forming about distance-angle under the condition of unknown sound source position; calculating the distribution entropy characteristics of the deconvolution beam forming result in the angle dimension, and using a preset threshold to correlate the interference positioning of a target; constructing an array flow pattern matrix A of each near-field target l and generating a covariance matrix A; calculating the orthogonal complement space projection matrix of the covariance matrix A, and applying the orthogonal complement space projection matrix to the array element domain covariance matrix C to perform near-field interference suppression; using a far-field plane wave model to perform deconvolution beam forming calculation on the covariance matrix for suppressing near-field interference, so as to obtain passive detection results of the near-field strong interference suppression. The application can realize near-field interference positioning and interference suppression under the condition that the interference position is unknown.

The application discloses an industrial label printing self-adaptive optimization method based on multi-dimensional defect quantification. The method realizes intelligent management and control of label printing quality through task data preparation, image acquisition and parallel diagnosis, defect quantification and shunt decision, quality trend analysis and self-adaptive tuning, and predictive maintenance warning. The core of the method is to construct a defect severity index quantification model, combine the improved YOLOv8 algorithm to realize accurate classification and root diagnosis of defects, and realize real-time fine tuning of printing parameters through a fuzzy PID self-tuning tuning engine, and match a cache level real-time bit-level check to ensure data accuracy. The application breaks through the limitations of traditional passive detection, realizes active optimization and predictive maintenance, greatly improves production efficiency and reduces the rate of defective products, ensures stable label printing quality and accurate data, and is suitable for high-speed industrial label printing scenes.

The utility model discloses a temperature measuring device for an enclosed bus, which comprises a directional antenna, a data concentrator, a transmission module and one or more RFID (Radio Frequency Identification)-based temperature sensors, and the temperature sensors are arranged on a metal flexible connector of the enclosed bus; the directional antenna is installed on the side wall of a closed mother cylinder of the closed bus. The temperature sensor is connected with the data concentrator through the directional antenna, the data concentrator is connected with the transmission module, and the transmission module is connected with the upper computer. The temperature of the metal soft connection part of the enclosed bus is acquired through the RFID-based temperature sensor, and the detected temperature is transmitted to the data concentrator through the directional antenna and is uploaded to the upper computer through the transmission module, so that the temperature measurement of the enclosed bus is realized; wireless passive detection can be realized through the temperature sensor based on the RIFD; detection data can be displayed online in real time through the background of the upper computer.

The invention discloses a chip Trojan horse detection and shielding system and method based on multiple confusion links, and relates to the technical field of integrated circuit safety. According to the method, a plurality of confusion links which are large in structural difference, identical in function and similar in time delay are designed for sensitive critical paths in a chip. A write-only unreadable register is adopted to store link selection information, so that an attacker is prevented from stealing or tampering a currently activated path by means of scanning a chain, debugging an interface and the like; a dual-stage operation mechanism is adopted, a test stage is combined with high-precision delay measurement and anomaly analysis to accurately identify a path in which Trojan is implanted, and a formal stage is dynamically switched to a Trojan-free path based on safety link information stored in a register, so that a chip is ensured to automatically avoid malicious logic during operation, and active defense instead of passive detection is realized. The method is superior to a traditional hardware Trojan horse protection scheme in detection precision, operation safety and implementation cost, and an efficient, reliable and easy-to-integrate solution is provided for chip safety.

According to one aspect of the present disclosure, a computer-implemented method for determining a configuration of a projectile launching device is provided, wherein the method comprises receiving first data from a passive sensor; determining a first location of a detected object based on the first data; determining a second location based on the first location; and determining a configuration of the projectile launching device to fire a projectile such that it collides with the detected object at the second location.

The invention discloses a combined train obstacle detection system and method, and belongs to the technical field of train operation safety, the detection system comprises an active detection module, a passive detection module, a processor module and a communication module, the active detection module comprises a distance detection assembly and a form detection assembly; the distance detection assembly and the form detection assembly are arranged at the head position of the train and are respectively used for detecting form information and distance information of an object; the passive detection module comprises a pressure detection assembly arranged at the bottom of the train and used for detecting pressure information when the train makes contact with an obstacle. The processor module is in communication connection with the distance detection assembly, the shape detection assembly and the pressure detection assembly, and judges and generates different signals according to detected information; and the communication module transmits the information received by the processor module and the generated signal to a cab and a train control center. According to the combined train obstacle detection system, early warning response and alarm response of different levels of obstacles can be achieved.

The application relates to an ionosphere virtual height estimation method applied to a short-wave active-passive integrated radar, which comprises the following steps: 1: an integrated detection system receives active radar detection echoes and passive radar detection echoes, and a main-passive detection channel is respectively subjected to distance, speed and azimuth sorting to obtain an active radar point track measurement set and a passive radar point track measurement set; 2: a group of points is respectively taken out from the two measurement sets, and the ionosphere virtual height is calculated by assuming the points as matched points; 3: the active radar measurement and the passive radar measurement are converted to the northeast sky coordinate system according to the calculated ionosphere virtual height; 4: a threshold method is used for global matching on the converted measurement sets to obtain a matching result, and a cost function value is calculated according to the matching result; 5: the two measurement sets are respectively traversed, and the steps 2, 3 and 4 are repeated, and the ionosphere virtual height corresponding to the minimum cost function is taken as the ionosphere virtual height in a detection environment. The application is used in the field of high-frequency over-the-horizon radar signal processing.

The present application relates to the technical field of image processing, and discloses a backlight effect image edge enhancement method based on intelligent identification, which comprises the following steps: determining the type of the on-site environment; performing global optimization on the original image based on a set environment perception type enhancement mechanism according to the determined type of the on-site environment to output a global pretreatment image; constructing a backlight area segmentation model for the global pretreatment image; outputting a local enhancement image; designing a structure perception type local adaptive threshold algorithm for the local enhancement image to output a binary image that maintains the continuity of the structure; extracting an edge map of the binary image through an edge detection algorithm to optimize the topological structure of the contour in the binary image; comparing with a wooden template processing standard feature library to output an edge quality evaluation result and feed back to a processing control system. The present application realizes the deep linkage of defect detection and processing control, changes from passive detection to active optimization, and improves the production efficiency and the yield.

The invention discloses a plastic pipe extrusion on-line ovality detection and correction method and device, and belongs to the technical field of plastic pipe forming. Comprising the following steps: S1, pipe surface data measurement: driving a turntable to rotate around a pipe, and synchronously measuring distance and temperature through a displacement sensor and a temperature sensor on the turntable; s2, ellipticity calculation: calculating the real-time ellipticity and the distribution orientation of the pipe based on the distance data; s3, temperature partition calculation: calculating the average temperature and the temperature difference of each fan-shaped partition of the pipe based on the temperature data; and S4, ellipticity closed-loop regulation and control: associating the distribution orientation of the ellipticity with the temperature parameters of the corresponding subareas, and differentially adjusting the cooling parameters of the corresponding subareas on the setting machine so as to actively correct the ellipticity. According to the method, the ellipticity and the circumferential temperature are measured synchronously on line, and partition regulation and control are implemented based on'temperature-deformation 'association, so that closed-loop quality control from passive detection to active prevention is realized.

The invention discloses a titanium alloy welding fault monitoring method, device, system, equipment and product, and relates to the technical field of welding monitoring. The method comprises the following steps: firstly, receiving visual acquisition data from a visual sensor, spectrum acquisition data from a spectrum sensor, power supply acquisition data from a power supply sensor and temperature acquisition data from an infrared temperature sensor in real time, and then carrying out time synchronization and multi-dimensional feature extraction processing on the collected multi-source data; the method comprises the following steps: obtaining sequence data of a multi-dimensional feature vector currently used for reflecting a titanium alloy welding health instantaneous state, finally importing the sequence data into a titanium alloy welding fault recognition model obtained by pre-training based on a time sequence classification model to obtain a current fault recognition result, and outputting a current titanium alloy welding decision according to the recognition result. In this way, comprehensive perception of the titanium alloy welding process and early-stage accurate prediction of faults such as arc stopping can be achieved, passive detection is changed into active intervention, and the welding stability and the yield are effectively guaranteed.

The invention relates to an unknown unmanned aerial vehicle signal detection method and device, computer equipment and a storage medium, and the method comprises the steps: generating a slow time delay correlation coefficient graph according to IQ data obtained from an unmanned aerial vehicle signal, carrying out the judgment of a periodic repetition signal according to the features of the periodic repetition signal presented in the slow time delay correlation coefficient graph, and carrying out the detection of the unknown unmanned aerial vehicle signal. If the periodic repetition signal exists, estimating the starting and ending time and the repetition period of the periodic repetition signal, performing rearrangement and phase alignment on the periodic repetition signal according to the starting and ending time and the repetition period to obtain an alignment signal matrix, and obtaining the signal fingerprint of the unknown unmanned aerial vehicle signal according to the average characteristic of the alignment signal matrix. Therefore, unknown unmanned aerial vehicle signal detection is realized. By adopting the method, the signal of the unmanned aerial vehicle of an unknown model can be quickly detected and estimated, and then passive detection is realized.