38 results about "Passive probing" patented technology

The invention provides a communication and sensing integration method and system, electronic equipment and a storage medium. The method comprises the following steps: under the condition that a target object needs to be sensed, detecting the target object based on a passive detection technology to obtain a detection distance of the target object; and determining a target signal transmission mode adapted to the detection distance for sensing. And according to the target signal transmission mode, performing communication processing based on the target antenna in the communication time slot, and performing sensing processing based on the target antenna in the sensing time slot. Based on the scheme of the application, communication and sensing functions can be realized only by deploying a set of target antennas, so that spectrum and hardware resources are saved. In addition, in the sensing process, the signal transmission mode is adapted according to the detection distance of the target object determined by the passive detection technology, and the sensing precision for the target object can be improved. Meanwhile, the passive detection technology can assist in locking the target object in the sensing process, and the blind area possibly caused by switching the signal transmission mode of the target antenna is made up.

The invention discloses a passive detection resource parameter dynamic optimization system and method based on frequency spectrum monitoring, and the method comprises the steps: carrying out the control of a frequency spectrum monitoring resource residence parameter and a passive detection resource residence parameter based on task data, radar receiving signals and frequency spectrum monitoring data, and carrying out the receiving and processing of a radio frequency signal through a passive radar system, and dynamically optimizing passive detection resource residence parameters periodically based on a frequency spectrum monitoring result in combination with a frequency spectrum knowledge base. According to the scheme, the passive detection resource parameters are dynamically optimized, the search resource parameters can be customized according to the external environment, and the probability that the search resides to intercept the target is improved; and meanwhile, when the searched and intercepted target is tracked, the tracking resource parameters and the tracking target parameters are dynamically adapted, so that the target tracking stability is improved.

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 embodiment of the invention discloses a construction method, device and equipment of an intrusion detection model. When the method provided by the embodiment of the invention is executed, firstly, a distributed architecture capable of realizing parallel computing of the intrusion detection task of the power internet of things can be established; and constructing a self-adaptive neural network model, and deploying the self-adaptive neural network model to the computing node of the distributed architecture to obtain a target node. And then active and passive combined network detection and data sensing tasks are distributed to the target node, so that context sensing data of all devices in the electric power Internet of Things are obtained. And inputting the context sensing data into the adaptive neural network model to obtain an intrusion detection model. According to the method, the distributed architecture is constructed, the computing resources of multiple computing nodes are fully utilized, the adaptive neural network model is constructed, and the mode of combining active detection and passive detection is combined, so that the network activity and data traffic in the power Internet of Things are comprehensively monitored and perceived, and the coverage range and accuracy of intrusion detection are improved.

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 application provides a grey fault detection and positioning method and system based on hybrid in-band network telemetry, and relates to the field of fault detection.The method comprises the following steps: a server collects hop-by-hop telemetry information of a passive INT detection packet, performs a first detection on whether a fault exists, and sends a second detection instruction of a fault path to a controller of a virtual SDN network; the controller sends an active INT detection packet to the server, and performs a second detection on the path with the fault in the first detection; a source server re-routes data flow of the path information with the real fault; the controller sets priorities for all the path information with the real fault, compares the paths according to the priorities, and obtains a fault position; the controller feeds back the fault position to the server, and the server finds all the paths related to the fault position and ages in advance.The application integrates the active in-band network telemetry and the passive in-band network telemetry, makes up for the deficiency of a single telemetry method, and improves the efficiency and reliability of the network telemetry.

The present invention discloses a system and method for dynamically optimizing passive detection resource parameters based on spectrum monitoring, which respectively controls spectrum monitoring resource resident parameters and passive detection resource resident parameters based on mission data, radar reception signals and spectrum monitoring data, and periodically optimizes passive detection resource resident parameters based on spectrum monitoring results and in combination with a spectrum knowledge base after receiving and processing radio frequency signals by a passive radar system. The dynamic optimization of passive detection resource parameters in the scheme of the present invention can customize search resource parameters according to the external environment to improve the probability of search and retention of intercepted targets; at the same time, when tracking the searched and intercepted targets, the tracking resource parameters and the tracking target parameters are dynamically adapted to improve the stability of target tracking.

This invention belongs to the field of radio countermeasures technology, and specifically relates to a distributed active defense system for unmanned aerial vehicles (UAVs). It includes an intelligent deployment dynamic optimization module, a multimodal cognitive jamming module, a high-precision three-level clock synchronization module, and a flexible self-organizing network communication module for overall system function support; distributed hardware devices and edge intelligent decision-making terminals for distributed deployment locations to implement detection and countermeasure functions; in practical operation, it can provide a distributed active defense system for UAVs targeting large areas and multiple important locations, avoiding application problems such as inaccurate deployment location selection, large coverage blind spots, low synchronization accuracy, unreliable communication networking, severe self-interference with the electromagnetic and timing equipment of partners, and poor UAV countermeasure effects. It achieves rapid, automatic, passive detection and precise jamming of "low, slow, and small" UAVs targeting large areas and multiple important locations.

The invention discloses a space-based optical active and passive task planning method for space situation awareness, and belongs to the field of space situation awareness. The method comprises the following steps: establishing an active and passive detection mode, and using a large-view-field passive sensor and a small-view-field active sensor to realize timely discovery of a new target and accurate perception of a catalogued target state; constructing a single-sensor large-scale target track state determination method, and determining a new target state and a cataloged target state by using a multi-target tracking filter and an initial track determination method; meanwhile, a new target label matching method is constructed and is used for identifying the corresponding relation of new targets among different sensors; afterwards, a multi-sensor information fusion method based on label matching is combined, unification of multi-target information among multiple sensors is achieved, and stable and consistent multi-target state estimation is obtained; and finally, according to a multi-target state estimation result, comprehensively evaluating the urgency of additional observation of a new target and the degree of uncertainty of a cataloged target state, planning a sensor to preferentially observe a target with relatively large uncertainty, and improving the utilization efficiency of observation resources.

The invention provides a full-view monitoring system and method for an application, and relates to the technical field of application monitoring, and the method comprises the steps: a passive detection module is integrated in a business application, receives monitoring data, and regularly sends the monitoring data to a business collection and analysis module according to the setting of a data collection and analysis module; the active detection module receives and simulates a real service message to send a request to a service application according to the setting of the data collection and analysis module, and periodically sends request information and response data to the data collection and analysis module; the data collection and analysis module receives the monitoring data sent by the passive detection module and the active detection module, sends the monitoring data to the service application state evaluation model to obtain an evaluation result, judges a running state and pushes abnormal service application information to the alarm module; and the alarm module sends the abnormal service application information to operation and maintenance personnel and carries out service application fault self-recovery operation. According to the method, active and passive detection, comprehensive evaluation and real-time alarm of the application running state are realized, and higher-dimension availability of a key system is realized.

A charged small target detection and positioning method based on double-sphere differential array, when the flying small target carries electric charge due to friction, contact separation, air action or environmental electric field induction during movement, different induced charge changes are generated on the detection electrode and reference electrode of each double-sphere differential electrostatic sensor when passing through the detection area, after current-voltage conversion, differential amplification, filtering and synchronous acquisition, multi-channel differential electrostatic response signals are obtained. Based on the calibration correction mirror model, combined with multiple sensor observations, a variable-width sliding window electrostatic distortion weighted nonlinear target detection algorithm is used to solve the target position and charged quantity, and through sliding window trajectory fitting, the target velocity, incident direction and motion trajectory are obtained, realizing the non-contact passive detection and positioning of the charged small target. The sensor array can use a system composed of a double-sphere differential electrostatic sensor array, a front-end signal conditioning unit, a synchronous acquisition unit, a data transmission unit and a parameter solving unit.

The invention discloses an asset detection method and system based on cyberspace surveying and mapping, and the method comprises the steps: transmitting a detection signal to a network, the detection signal being used for detecting network assets; receiving a first message returned via the network, the first message being a response to the detection signal and indicating the detected first network asset information; monitoring traffic data corresponding to the network; determining second network asset information based on the flow data and the first network asset information; performing vulnerability scanning according to the second network asset information by using a cyberspace surveying and mapping technology to generate risk assessment information; and sending a second message to the display terminal, the second message carrying the second network asset information and the risk assessment information, so that network assets that cannot be found by passive detection can be detected in an active detection mode, and comprehensive and accurate network asset information is formed in combination with the flow data, thereby realizing comprehensive risk assessment, and improving the risk assessment efficiency. Therefore, the network security is improved.

This application belongs to the field of multi-target state estimation technology. This application provides a method for joint optimization of the states of multiple passive sensors and multiple targets based on a two-factor graph. The embodiments of this disclosure first construct a passive detection multi-target measurement solution layer to obtain coarse positioning points of multiple targets as state measurements; then, a joint optimization framework is constructed containing a multi-target management layer, a multi-target state optimization layer, and a multi-sensor state optimization layer. The non-Gaussian distribution characteristics of the fused measurements and the non-Gaussian distribution characteristics of the fused measurement reprojection error are modeled using a three-dimensional Gaussian mixture model and a two-dimensional Gaussian mixture model, respectively. Then, a two-factor graph is established based on the three-dimensional and two-dimensional Gaussian mixture models; finally, the sensor attitude state and target state are jointly optimized and estimated using sliding window nonlinear least squares, and the optimization results are fed back to the multi-target management layer to form a closed-loop update.

The application provides a network traffic aggregation statistics-based asset fingerprint extraction method and system. The method comprises: obtaining a first asset fingerprint feature set of a target network asset device by passive detection, the set comprising uplink and downlink traffic of each network protocol of the target network asset device within a predetermined time period; obtaining a second asset fingerprint feature set of the target network asset device by active detection, the set comprising each inherent attribute feature of the target network asset device; performing weighted calculation on each fingerprint feature of the two asset fingerprint feature sets based on a predetermined weight algorithm; and constructing an asset fingerprint of the target network asset device according to the two asset fingerprint feature sets obtained by the weighted calculation. The system comprises various functional modules corresponding to the above steps. According to the application, the problem that the network asset fingerprint constructed by the existing network asset fingerprint extraction method cannot comprehensively reflect the characteristic attributes of the network asset is solved.

The invention discloses a decomposition deconvolution beam forming method based on subspace clustering, and belongs to the field of direction of arrival estimation in passive detection. According to the invention, the problem of poor target discovery capability of the existing deconvolution beam forming algorithm is solved. According to the spectral clustering-based subspace decomposition method provided by the invention, the covariance matrix can be adaptively decomposed into the strong signal subspace and the weak signal subspace according to the size of the characteristic value without estimating the number of information sources, so that the problem of array gain reduction of a traditional deconvolution beam forming algorithm in a strong interference scene is solved; and the target discovery capability is improved. According to the DOA estimation method provided by the invention, null constraint deconvolution beam forming and a signal-to-noise ratio-based fusion algorithm are introduced, so that energy leaked to weak signal subspace components by interference can be effectively suppressed under non-ideal conditions, the influence of strong interference on a weak target can be effectively suppressed, and the robustness of the algorithm and the target discovery capability are improved. The method can be applied to direction-of-arrival estimation in passive detection.

The invention provides a passive detection analog simulation system based on an underwater platform, and belongs to the technical field of analog simulation. The objective of the invention is to solve the problem that an existing analog simulation platform for underwater target tracking lacks the feasibility and reliability influence of a verification target resolving algorithm. The system comprises a laying module, a motion module, a data import module, an interface display module, a network communication module, a process control module, a data recording module, an electronic chart module and an index evaluation module, can meet the requirements of a verification algorithm, simulates the motion trails of an underwater platform and a target, fully verifies the feasibility and reliability of the algorithm, and improves the verification efficiency. And meanwhile, the influence of different maneuvering strategies on a target resolving algorithm can be verified.

The invention discloses an air traffic warning and collision avoidance system and method based on multi-source data fusion, and particularly relates to the technical field of aviation equipment, and the air traffic warning and collision avoidance system comprises a traffic monitoring module which is used for obtaining passive detection target data; the meteorological radar module is used for obtaining active detection target data; and the data fusion module is used for fusing the passive detection target data and the active detection target data to obtain a fused target track. The airborne active phased array meteorological radar transmits and receives radar electromagnetic waves to actively acquire air target data, active detection target data can be stably acquired in a complex electromagnetic environment, the data fusion module performs multi-source fusion on the active detection target data and passive detection target data, and the data fusion module performs multi-source fusion on the active detection target data and the passive detection target data. Data errors or data loss caused by failure of single data can be avoided, the accuracy and integrity of the data can be improved, and the monitoring, warning and anti-collision capabilities of the local machine to the aerial target can be effectively improved.

The application discloses a detection method, an electronic device and a system. By listening to the service message transceiving event and transceiving result between devices, the device online state machine is driven to migrate, and the device state change can be quickly perceived. The method increases the active detection mechanism, sets up a new device state - silent state, and a silent detection interface, supports the application to determine the UI presentation and business processing strategy of various states of the device. When the passive detection times out, the online state will change to the silent state. In the silent state, the local device can actively broadcast the detection message as needed to confirm whether the opposite device is online, solve the problem that the device state cannot be updated in time and is prone to error, effectively reduce the device offline delay, resource power consumption, and transmission bandwidth occupation, avoid the defect that the device is frequently online and offline due to temporary disconnection, provide a more intuitive and easy-to-understand application interaction interface for the user, improve the user experience, and further improve the communication efficiency.

The invention discloses a passive detection station distribution method and system based on a preselected area, a medium and a product, and the method comprises the steps: selecting the position of a master station S0 as an original point O to establish a coordinate system O-XYZ, determining a point A and a point B which are farthest from each other on the boundary of the maximum activity area range of a detection target, and determining the preselected area of a circle and the center of the preselected area as a point C; determining an included angle difference between the projection points of the point A and the point C on the XOZ plane and the X axis as a detection area angle theta; if the angle theta of the detection area is smaller than or equal to the preset threshold value theta max, three auxiliary stations distributed in an inverted triangle mode are selected or arranged for the main station S0, otherwise, three auxiliary stations distributed in a star mode are selected or arranged for work for the main station S0. The invention aims at ensuring that a potential detected target area is always in a high-precision positioning range, simultaneously realizing non-delay dynamic switching of two station distribution modes, and supporting detection and positioning of a high-value target with electromagnetic radiation information.

The application discloses a target tracking method and device of a multi-base external radiation source radar, mainly applied to the field of the multi-base external radiation source radar, and aims to effectively fuse multi-base measurement information and improve target positioning and tracking precision. The method firstly obtains an effective measurement data set of a target, and establishes a multi-base passive detection tracking model based on Bernoulli random finite sets; the tracking model comprises a motion model created based on a target state space and a measurement model created based on an observation space; secondly, Bernoulli prediction is performed on a target state probability density according to the tracking model; then, a target posterior probability density is obtained through a Bernoulli updating algorithm improved based on multi-source information fusion; the Bernoulli updating algorithm improved based on the multi-source information fusion involves complex multiple set integral calculation, and then a Gaussian mixture method is adopted to realize; finally, an accurate estimation of the target state is obtained through track pruning and edge multi-target estimators, and a target track output is formed.

The present application provides an intrusion detection system, which includes a processing host and a plurality of intrusion detection devices arranged at preset intervals on the fence of a target area, each of which includes a visual detection component and a vibration detection component; the visual detection component identifies the object type of the intrusion object from the captured target image of the intrusion object that initially enters the early warning identification area outside the target area, and sends the captured target image and the identified object type to the vibration detection component in the same target intrusion detection device; the vibration detection component collects the vibration signal generated by the intrusion object contacting the fence, determines the behavior type of the intrusion object according to the vibration signal, and generates an intrusion detection result and sends it to the processing host; the processing host generates intrusion alarm information according to the object type and the behavior type. Through the present application, the combination of active detection and passive detection can be realized, thereby improving the detection accuracy and reducing the false alarm rate.

The application discloses a phase encoding pulse radar signal direction finding method, which is used for two-phase encoding and four-phase encoding signals, and comprises the following steps: firstly, a flat method or a four-time method is used to pre-process a received signal; then, energy accumulation is carried out on frequency domain data of the received signal; and finally, a pulse starting time and a pulse ending time are obtained through adaptive threshold detection processing; intrapulse sampling data of an original sampling wideband phase encoding pulse signal corresponding to the starting time and the ending time are sent into a channelization detection processing module based on a multi-phase filter combined with a short-time Fourier transform, so that signal amplitude and phase parameters and high-precision frequency information corresponding to a single code element are obtained; and finally, target angle information is obtained based on an optimal ambiguous multi-baseline interferometer direction finding algorithm. The application can detect and identify weak and complex modulation signals, and can provide more accurate and reliable guiding information for a passive detection system.

The application discloses an agricultural sensor adaptive topology networking device, method and storage medium. The device comprises a sink node and a plurality of sensor nodes, the sensor node comprises a microcontroller module, an environment perception module and a multi-mode radio frequency transceiver module, the microcontroller module reads the current relative humidity and photosynthetically active radiation attenuation rate, calculates the directed link cost, and when the directed link cost exceeds the cost threshold and the current packet loss rate change slope is greater than zero, the omnidirectional microstrip antenna is cut off and the directional spiral antenna is turned on, the media access control layer radio frequency transmission power is improved, and the network topology reconstruction is initiated by executing the directional routing request with the direction identifier. The application can solve the problem of route response lag caused by passive detection, and improve the stability of network structure and communication data.

The present invention provides a network data asset cross-validation method and system, which relates to the field of data processing technology. The method includes: obtaining active detection results of network data assets through active asset detection technology; obtaining passive detection results of network data assets through passive asset detection technology; cross-validating the active detection results of network data assets with the passive detection results of network data assets to determine the current network security. The present invention can effectively combine active detection and passive detection technology, make full use of the advantages of both for cross-validation, and improve the comprehensiveness of network data asset detection results and the accuracy of network security monitoring results.

The present application relates to a kind of joint underwater acoustic target passive detection and azimuth estimation method based on YOLO model, the method is first based on the underwater acoustic data received by towed horizontal array, using wideband beam forming method extracts frequency-beam domain FBD feature, and is divided into training set and test set, and sample expansion is carried out to training set;Then, the convolutional neural network of YOLO model is built, loss function is designed, and the FBD feature sample in the training set after expansion is used to train YOLO model;Finally, the FBD feature sample in test set is input into trained YOLO model, to obtain the existence probability of target spectrum in FBD feature and the specific azimuth of target spectrum.The present application can directly input FBD feature into YOLO model, and simultaneously use regression method to obtain target existence probability and target DOA information in FBD sample, effectively utilize the joint information of target spectrum and target azimuth, can detect the existence probability of target source in the marine environment of multiple interference, while giving the specific azimuth of target source.

A kind of charged small target detection and positioning device based on double-sphere differential array, by double-sphere differential electrostatic sensor array, front-end signal conditioning unit, synchronous acquisition unit, data transmission unit and parameter solving unit composition.Flight small target carries electric charge in the process of movement due to friction, contact separation, air action or environmental electric field induction, when through detection area, different induced charge changes are generated on the detection electrode and reference electrode of each double-sphere differential electrostatic sensor, after current-voltage conversion, differential amplification, filtering and synchronous acquisition, multiple channel differential electrostatic response signals are obtained.Parameter solving unit is based on calibration correction mirror model, combined with multiple sensor observation, adopts variable-width sliding window electrostatic distortion weighted nonlinear target detection algorithm to solve target position and charged quantity, and obtains target speed, incident direction and motion trajectory by sliding window trajectory fitting, realizes non-contact passive detection and positioning of charged small target.

The invention discloses a moving target tracking method combining active and passive information fusion and ensemble learning, and mainly solves the problems that a single data source is easily influenced by noise and abnormal values, the model complexity is high and the stability is poor in the prior art. The method comprises the following implementation steps of: fusing active detection data and passive detection data to obtain observation data; fitting observation data by using a polynomial fitting least square method and an extreme learning machine to respectively obtain a basic learner 1 and a basic learner 2; combining prediction results of the two learners through an integrated learning stacking method to obtain a prediction state of the target; gaussian filtering smoothing processing is carried out on the target prediction state; calculating the movement speed of the target; and judging whether the observation data is the last group of observation data, and if yes, ending tracking. Through combination of active and passive information fusion and an ensemble learning method, the stability of the model is improved, and efficient and accurate tracking of the target is realized.

Provided are a device and a method for passively detecting tunnel excavation adverse geology. The device for passively detecting tunnel excavation adverse geology includes a plurality of detection mechanisms. The plurality of detection mechanisms are disposed in a forepoling borehole located at a working face of a tunnel. The plurality of detection mechanisms includes a protection system, a signal acquisition system, a signal transmission system, and an intelligent terminal system. The signal acquisition system is disposed inside the protection system. The protection system is configured to protect the signal acquisition system. The signal acquisition system is connected to the intelligent terminal system through the signal transmission system to transmit acquired signals to the intelligent terminal system for processing.

The invention provides a reconfigurable interconnection dual-mode underwater acoustic transducer array transceiving system, which comprises an underwater acoustic transducer array, a reconfigurable interconnection module, a reconfigurable interconnection module, a reconfigurable interconnection module and a reconfigurable interconnection module, the reconfigurable interconnection network is connected with the underwater acoustic transducer array and is switched between a first mode and a second mode; the front-end receiving circuit is connected with the reconfigurable interconnection network; the signal processing module is connected with the front-end receiving circuit and is used for performing index output and detection in the first mode and outputting a multi-beam imaging result and / or a target detection result in the second mode; the control module is used for controlling the mode switching of the reconfigurable interconnection network and coordinating the working time sequence of the system; the first mode is that the plurality of transduction array elements are configured as an aggregation receiving structure to execute passive detection, and the second mode is that the plurality of transduction array elements are configured as an independent reading structure to execute active detection and multi-beam imaging. The passive detection and active imaging capabilities are considered on the same hardware platform, and the channel number, the power consumption and the system complexity are reduced.

The invention relates to a time-space fusion intelligent identification method for a network flow asset portrait, and the method comprises the following steps: 1, collecting comprehensive equipment information through employing active detection, passive detection and non-intrusive detection based on a search engine according to a detection mode in network space detection, and completing the detection of basic data; step 2, based on the equipment information received in the step 1, a network flow asset portrait space-time intelligent fusion module carries out deep service identification on specific attributes, functions and security conditions of equipment or systems in the network found by the detection module; and step 3, based on an identification result in the step 2, constructing a multi-level layer portrait visualization strategy by a visualization display module. The method can provide data support for network attack and defense confrontation.