17 results about "Passive probing" patented technology

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 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 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.

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.

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 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 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 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.

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 discloses a network asset identification method based on a Qwen3 large model technology, which relates to the technical field of network security, and specifically comprises the steps of probe-based flow collection, flow metadata analysis, Qwen3 large language model-based asset fingerprint extraction, sampling strategy-based network asset identification, passive detection as a basis, large model world knowledge, large model-based asset fingerprint extraction and network asset identification. According to the method, asset fingerprints are learned and summarized from flow messages, then identification of network assets is realized based on the asset fingerprints, in a real network environment, network messages are acquired by deploying a user-defined probe tool, element data are extracted by using a DPI deep message analysis technology, and then the asset fingerprints are automatically extracted and generated by using world knowledge of a large language model. And realizing identification of network assets by using the asset fingerprints.

The application belongs to the technical field of distributed multi-sensor passive detection multi-target tracking. The application provides a factor graph optimization passive detection multi-target tracking method based on a Gaussian mixture model. The embodiment of the disclosure distributes multi-target batch numbers by constructing a distributed passive sensor cooperative coordinate system and combining the same target determination results between two sensors; based on two-dimensional observation of the sensor, a direction finding line is calculated, the same batch number direction finding lines are combined, a least square method is used to obtain a multi-target position estimation point set, and a weighted fusion is used to obtain a multi-target rough positioning point; a Gaussian mixture model is used to model and fuse the measurement distribution characteristics, an expectation maximization algorithm is used to solve the parameters and complete the solvable conversion of the optimization problem; a factor graph optimization model containing multiple factors is constructed, a sliding window optimization is used to realize state estimation, and the Markov distance and the Hungarian algorithm are combined to complete trajectory association and state updating; and the performance of distributed sensor passive detection multi-target tracking is effectively improved.

A passive detection resource parameter dynamic optimization system and method based on spectrum monitoring. A spectrum monitoring resource dwell parameter and a passive detection resource dwell parameter are respectively controlled on the basis of task data, a radar received signal and spectrum monitoring data. When a radio frequency signal is received and processed by means of a passive radar system, dynamic optimization of the passive detection resource dwell parameter is periodically performed on the basis of a spectrum monitoring result and in combination with a spectrum knowledge base. According to the passive detection resource parameter dynamic optimization system and method, a search resource parameter can be customized on the basis of an external environment, thereby increasing the probability of intercepting a target during a search dwell. In addition, when the target intercepted during the search is tracked, a tracking resource parameter is dynamically adapted to a tracking target parameter, thereby improving target tracking stability.