91 results about "Radio spectrum" patented technology

XCB poly-radio devices as finders, locators, scanners, sensors, and Bluetooth (BT) radio topology reporters as part of a global lost-and-found services system. XCB devices function as “smart objects” and combine dual transceivers operating in the telecommunications and the low power ISM radio spectra, with default power management overrides under local control of the BT modem. “Familiar zones” are defined, and a “familiarity index” versus a “strangeness index” based on meta-analysis of BT radio contact logs are used to assign location. Location, in concert with other sensor data, is coupled to conditional logic rules programmable by the user or the system administrator. Locations, determined by monitoring familiar and unfamiliar BT radio topology, trigger executable commands to smartphones, to smart devices generally, and to remote machines, and can for example, generate alerts to a user if radiotagged assets, children or pets have strayed, are lost, or are left behind. When tied to cloud predictive services or increased edge computing capacity, incipient loss of radiotag objects can be detected before an owner knows the smart object is lost. In another embodiment, XCB devices having a novel chip architecture for improved power management of cellular and MCU circuits mediated by BT radio modem that achieves “situational awareness” by monitoring the BT signal environment around the device.

The invention relates to a wireless communication test method and system based on a portable frequency spectrograph and a medium, and belongs to the technical field of radio frequency spectrum analysis, and the wireless communication test method comprises the steps: collecting electromagnetic signal original data of a target area; constructing a dynamic electromagnetic environment baseline model; performing filtering and background subtraction processing on the original data of the electromagnetic signal; carrying out modulation mode identification, protocol analysis and terminal identification extraction on the preprocessed signal matrix, generating a multi-dimensional feature vector, carrying out convolution similarity matching on the multi-dimensional feature vector and a legal signal feature template, and distributing a threat level label; dynamically reconstructing scanning parameters of the portable spectrum analyzer according to the threat level label, generating an equipment parameter adjustment record and updating the electromagnetic fingerprint database; and generating a spectrum optimization control instruction based on the threat level label and the equipment parameter adjustment record, and issuing the spectrum optimization control instruction to the target base station or the terminal equipment. According to the application, the identification precision of the threat signal and the control closed-loop linkage capability in a complex electromagnetic environment are improved.

The invention discloses an unmanned aerial vehicle detection method based on multi-band radio spectrum analysis, and the method can recognize the flight control behaviors of a target, such as hovering, cruising or hovering, through the extraction of a signal track and the introduction of a behavior modeling mechanism, and effectively overcomes the defect that the prior art lacks the dynamic behavior analysis capability. A multi-node synchronization mechanism and a TDOA positioning method are combined, so that high-precision traceability of a target space position is realized; meanwhile, risk level assessment is carried out based on signal features, a track mode and a space region, so that the system has intelligent early warning capability. According to the whole scheme, a full-link unmanned aerial vehicle detection method from multi-frequency receiving, frequency spectrum identification, behavior judgment, target positioning to risk output is constructed, and the problems of single frequency band, rough identification means, lack of tracking and evaluation capability and the like in the prior art are effectively solved; and the practicability and the accuracy of the unmanned aerial vehicle detection system in a complex airspace environment are remarkably improved.

A method and an apparatus for automatically recognizing an unmanned aerial vehicle, an electronic device, and a computer medium are provided. A specific implementation of the method includes: in response to determining that a radio detection device detects a radio signal, preprocessing the radio signal to generate preprocessed radio spectrum data; extracting radio signal features in the preprocessed radio spectrum data; inputting the radio signal features into a pre-trained unmanned aerial vehicle signal recognition model to obtain an unmanned aerial vehicle signal recognition result; in response to determining that the unmanned aerial vehicle signal recognition result represents an unmanned aerial vehicle signal, controlling a high-definition camera apparatus associated with the radio detection device to photograph an environmental video of a surrounding environment at a first time in real time; and generating unmanned aerial vehicle detection information corresponding to the first time according to a three-dimensional feature information group.

The invention discloses an unmanned aerial vehicle detection method based on radio frequency spectrum identification and deep learning, and relates to the technical field of unmanned aerial vehicle monitoring, and the method comprises the steps: deploying a radio receiving station to collect radio signals, converting the radio signals into a time-frequency graph, extracting multi-scale signal features, carrying out the global average pooling of the multi-scale signal features, and carrying out the global average pooling of the multi-scale signal features; calculating a scale adjustment coefficient to generate a weighted feature, extracting a time sequence signal feature to generate a fusion feature, constructing a full connection layer to perform multi-task processing on the fusion feature, and outputting an unmanned aerial vehicle detection result; a state space is defined, an action space is defined according to environmental parameters, nonlinear harmonic response characteristics in a current state are measured to construct a nonlinear reward function, a comprehensive reward function is formed in combination with action interference effects, and a final interference strategy is output through reinforcement learning. According to the invention, the scientificity and robustness of the identification and interference strategy are significantly improved, the accuracy of the interference strategy is improved, and the interference effect of the unmanned aerial vehicle is effectively improved.

The invention relates to the technical field of network monitoring, in particular to a radio spectrum measurement system based on mobile crowdsourcing, which comprises a crowdsourcing acquisition module, a time sequence processing module, a space optimization module and a dynamic fusion module. According to the method, periodic scanning is combined with a multi-source heterogeneous data encapsulation protocol, timestamps, geographic coordinates, a signal-to-noise ratio and equipment voltage are integrated, multi-dimensional data standardized acquisition and dynamic association are achieved, time sequence weight parameters are calculated through a time sequence alignment algorithm, high and low frequency event distinction degree is enhanced, time noise is restrained, space weight coefficients are calculated through a DBSCAN algorithm, and multi-dimensional data acquisition and dynamic association are achieved. The method has the advantages that comprehensive credibility is generated through linear weighting, space-time coupling precision and evidence theory sorting credibility are improved, correlation between the credibility and the signal-to-noise ratio is analyzed, Kalman filtering is used for compensating voltage fluctuation, multi-source data fusion and error suppression are achieved, spectrum feature anti-interference capacity is enhanced, positioning deviation is reduced, and spectrum allocation efficiency and monitoring system stability are improved.

The invention discloses a non-uniform sampling scene self-adaptive frequency spectrum completion method based on an unmanned aerial vehicle platform. The method comprises the following steps: identifying an area shielded by a building; simulating the relationship between the signal intensity and the distance change; training a multi-head graph attention network embedded by building constraint to obtain a frequency spectrum completion intelligent model; in an initial sampling stage, a safety area is generated, candidate waypoints are created, and waypoints around each building are connected to form an unmanned aerial vehicle flight path; adopting an inverse distance weighting algorithm or a Kriging algorithm to finish initial completion so as to estimate the approximate position of the radiation source; and simulating deployment of the unmanned aerial vehicle for secondary sampling, carrying out dense sampling around the identified radiation source, then adaptively perfecting a sampling result by using a spectrum completion intelligent model, completing space completion of spectrum data, and constructing a radio frequency spectrogram. According to the method, the spectrum reconstruction performance is remarkably improved, and the collaborative optimization problem of high-precision requirement and low-cost implementation in urban complex scenes is effectively solved.

The invention belongs to the technical field of integrated circuits, and particularly relates to a time mismatch extraction method based on parallel windowing autocorrelation. According to the method, firstly, clustering processing is carried out on signal points; thirdly, preliminarily screening out legal signals based on the time occupancy rate of the signals, and constructing an initial legal signal library; a similarity score between the signals is further calculated through an IDK algorithm, and adjacent frequency bands are divided through a Pettitt method; and finally, continuation of legal signals is realized by means of a DBSCAN clustering algorithm, and automatic construction of a legal signal library is completed. The method is not only suitable for scenes without prior information, but also can be applied under the condition with partial prior information, and can be dynamically updated according to actual requirements. The method is based on radio frequency spectrum monitoring big data, can construct a legal signal library with high reliability and strong generalization ability, provides effective support for subsequent illegal signal monitoring, and has important application value.

Methods, systems, and devices for wireless communication are described. In some systems, a sidelink user equipment (UE) may prioritize multiple sidelink feedback channels for transmission via at least partially overlapping time resources. The UE may prioritize sidelink feedback channels according to prioritization parameters, where each prioritization parameter may be associated with a respective likelihood of success of a listen before talk (LBT) process for a respective sidelink feedback channel. The UE selects a subset of sidelink feedback channels based on the prioritization, and may perform one or more LBT procedures to access a shared radio spectrum band for transmission of the subset of sidelink feedback channels. The UE may transmit a subset of sidelink feedback channels during one or more channel occupancy time intervals of one or more successful LBT processes based on performing the one or more LBT processes.

The invention relates to an unmanned aerial vehicle detection and control method and system for regional defense, and the method comprises the steps: dividing a to-be-defended region, and setting a differentiated flight forbidding threshold and a detection response priority; secondly, constructing a multi-dimensional cooperative detection network of radio spectrum monitoring, millimeter wave radar, photoelectric tracking and an acoustic sensor based on sub-region division, and collecting multi-source data of the unmanned aerial vehicle in real time; judging a sub-region to which the unmanned aerial vehicle belongs through coordinate matching, screening out over-limit target data, and extracting a target feature set of flight speed, height, communication protocol type, radar reflection cross section and voiceprint information; and inputting a target dynamic classification model, and dynamically outputting three threat levels of a harmless civil type threat level, a suspicious reconnaissance type threat level and a high-risk aggressive type threat level. The hierarchical management and control are executed in combination with the sub-region priority and the threat level, the management and control effect is dynamically evaluated based on the preset evaluation index, and the detection parameter and the management and control strategy are adaptively adjusted, so that the intrusion risk of the core region is effectively reduced, and the intellectualization and adaptive ability of regional defense are improved.

The invention discloses an unmanned aerial vehicle detection and directional interference system based on an artificial intelligence technology, which belongs to the technical field of unmanned aerial vehicle detection interference and comprises a data acquisition module, an unmanned aerial vehicle identification module, a geographic information correction module, a trajectory prediction module and a directional interference module. The data acquisition module is used for acquiring geographic information data of a target area based on a geographic information system, comprises a radar, an acoustic sensor and a radio frequency spectrum detection sensor, and is used for acquiring feature data of a moving object in the target area; and the unmanned aerial vehicle identification module obtains feature data of a moving object in the target area based on the data acquisition module. According to the unmanned aerial vehicle detection and directional interference system based on the artificial intelligence technology, the obstacle in the target area is identified, the personalized behavior of the unmanned aerial vehicle is analyzed, the trajectory prediction and interference strategy of the unmanned aerial vehicle is optimized in combination with topographic features, and the trajectory prediction precision and the directional interference effect are improved.

The utility model discloses a tower crane radio environment monitoring device which comprises a shell installed at the position consistent with the height of a tower crane communication antenna, and a multi-frequency-band signal receiving module, an active frequency spectrum scanning module, a signal filtering processing module and a data aggregation and output module which are arranged in the shell. The multi-frequency-band signal receiving module is used for receiving radio signals of a plurality of preset frequency bands used for normal communication of the tower crane; the active frequency spectrum scanning module is isolated from the multi-band signal receiving module in a physical structure and is used for actively scanning a radio frequency spectrum in a tower crane operation area; the signal filtering processing module is in signal connection with the multi-band signal receiving module and the active spectrum scanning module; the data aggregation and output module is in signal connection with the signal filtering processing module, and the communication interface is in signal connection with the data aggregation and output module. The radio environment monitoring device can sense the radio environment experienced by the tower crane communication antenna in the actual operation process in real time.

The invention relates to the field of network security, in particular to a radio spectrum anomaly monitoring device based on CNN and blockchain, the system comprises five modules of spectrum sensing, feature processing, anomaly detection, lightweight reasoning and blockchain storage, firstly, the spectrum sensing module collects multi-band signals and generates an energy density matrix; then, the feature processing module applies adaptive wavelet transform to reduce noise, and constructs a two-dimensional feature topological graph; the anomaly detection module deeply extracts features by using a dynamic convolution kernel and a frequency domain attention mechanism, and generates an anomaly probability distribution diagram; the lightweight reasoning module fuses expert knowledge and a deep learning result, and accurately judges an attack type and confidence; and finally, a block chain storage module records a judgment result, data security is ensured and updated parameters are stored through distributed consensus verification, and the device adopts multi-mode sensing and combines network traffic and spectrum features to improve attack detection difficulty.

The invention provides a frequency spectrum knowledge graph construction method based on machine learning and Neo4j, and aims to realize intelligent management of radio frequency spectrum information. The method comprises the following steps: step 1, collecting real-time spectrum data through radio spectrum monitoring equipment; step 2, identifying and extracting feature information of the signal from the frequency spectrum data collected in the step 1; step 3, training and classifying the identified and extracted signal feature information by using a machine learning algorithm to obtain a signal modulation type; and step 4, constructing a spectrum knowledge graph based on the classified signal feature information, and performing visual display. According to the invention, through combination of machine learning and the Neo4j graph database, intelligent processing and display of radio frequency spectrum information are realized, the efficiency and accuracy of spectrum information management are improved, and the method is suitable for the fields of wireless communication and artificial intelligence.

The application relates to the technical field of radio signal detection, and discloses a method and system for detecting radio frequency spectrum of an unmanned aerial vehicle based on difficult example mining. The method comprises the following steps: performing time-frequency analysis on a received radio signal to obtain one-dimensional spectrum sequence data; inputting the one-dimensional spectrum sequence data into a target detection model based on an SSD architecture to perform detection, wherein the model adopts multi-scale features for detection and is trained through an improved adaptive loss function and a negative sample enhancement strategy; and determining the starting and ending frequency points of a target subsequence corresponding to the spectrum of the unmanned aerial vehicle signal according to the model output. The system comprises a double-antenna array, a radio frequency receiving unit, an industrial control processing unit and a front-end device for signal receiving and processing. The application effectively improves the detection precision and robustness of the unmanned aerial vehicle signal in a complex electromagnetic environment, significantly reduces the false alarm rate, and is suitable for real-time monitoring and spectrum management of the unmanned aerial vehicle.

A method, a network device, and a non-transitory computer-readable storage medium are described in relation to an radio spectrum binding service. The service may include generating and provisioning binding information. The binding information may include radio frequencies correlated to applications or categories of applications associated with packet data unit sessions or radio frequencies correlated to applications or categories of applications associated with packet data unit sessions, and network slice identifiers. The service may include selecting and using the binding information for packet data unit sessions.

The invention relates to the technical field of signal intensity detection, and discloses a signal intensity detection visualization processing method based on unmanned aerial vehicle three-dimensional data acquisition, and the method comprises the steps: installing a radio frequency spectrum sensor on an unmanned aerial vehicle, and carrying out the flight path planning of the unmanned aerial vehicle for a target region after grid division; the unmanned aerial vehicle collects signal data in real time in the flight process; based on the unmanned aerial vehicle positioning information and the signal intensity, positioning a signal source by using an extended Kalman filtering algorithm; carrying out fitting and signal diffusion processing on the signal intensity based on the signal source position; and visualizing the signal intensity of the target area based on the signal intensity distribution detection result. According to the invention, the extended Kalman filtering algorithm is used to filter signal data of each cluster in combination with timestamp information, signal source positioning is realized, signal intensity fitting results of different signal source positions are generated, signal intensity calculation is carried out, and signal intensity visualization is carried out on different positions of a target area.

Procedures, methods, architectures, apparatuses, systems, devices, and computer program products related to multi-radio spectrum sharing (MRSS) cell access are described. One method may include receiving, in a cell access broadcast channel, a cell access broadcast transmission associated with a first radio access technology, determining that cell access information associated with the first radio access technology is received in the cell access broadcast transmission, determining that cell access information associated with a second radio access technology is received in the cell access broadcast channel, determining the cell access information associated with the second radio access technology based on reserved bits in the cell access broadcast channel and the cell access information associated with the first radio access technology, and receiving cell system information associated with the second radio access technology using the determined cell access information associated with the second radio access technology.

The invention discloses an energy efficiency self-adaptive regulation and control device and method for frequency spectrum detection equipment, and relates to the technical field of radio frequency spectrum monitoring. The device comprises a dynamic sleep wake-up module, an energy efficiency self-adaptive adjustment module, a task priority management module and a feedback optimization module, during standby, environment radio frequency signals are sampled at a low rate, intensity, bandwidth and period characteristics are extracted, and a wake-up threshold is dynamically adjusted based on target characteristic judgment and noise and leak detection to trigger wake-up; during working, the gain, the sampling rate, the quantization precision and the algorithm complexity are jointly adjusted according to task requirements and energy efficiency constraints, and a performance accepting and rejecting instruction is generated according to task priorities when resources are limited; and collecting detection, power consumption and performance data in an idle state, and updating a wake-up threshold value and operation parameters for a next period through reinforcement learning or regression analysis. By means of the device and method, invalid wakeup can be restrained, the missing detection risk can be reduced, and self-adaptive balance of energy efficiency and task performance is achieved.

The present application relates to the field of data processing, in particular to a radio spectrum monitoring method and system of low altitude defense system, comprising: performing short-time Fourier transform on signal time sequence data collected by any monitoring point to obtain a time-frequency matrix, taking the time-frequency matrix of any monitoring point as a target matrix, calculating the frequency domain reliability of the target matrix according to the energy proportion of each frequency point and the correlation degree of each frequency point, combining with information entropy theory, calculating the periodicity degree of the target matrix, obtaining the monitoring reliability of the monitoring point corresponding to the target matrix according to the frequency domain reliability and the periodicity degree, inputting the time-frequency matrix of each monitoring point into a preset prediction model of the probability of existence of unmanned aerial vehicle to obtain the prediction probability of each monitoring point, taking the monitoring reliability of each monitoring point as a weight, and weighting and summing the prediction probability of all monitoring points to obtain the total probability of existence of unmanned aerial vehicle. The present application can improve the accuracy of unmanned aerial vehicle monitoring.

The present invention discloses a system for the effective capturing, amplifying, and redistribution of radio spectrum services. The system comprises a donor site with an antenna to receive signals, which are then filtered into multiple frequency bands. The filtered signals are amplified and converted into modulated light by a Hybrid Fiber Coax RF over Glass (RFOG) headend transmitter. The modulated light is carried via a fiber-optic cable to a Planar Lightwave Circuit (PLC) splitter that divides the light beam, directing it to multiple service sites. Each service site is equipped with an RFOG HFC node receiver that reconverts the modulated light back into radio signals. A transmission amplifier boosts these radio signals, enabling broad coverage and efficient redistribution of various radio spectrum services.

This application discloses a method and system for constructing and locating radio maps in a wireless communication network. The method specifically includes the following steps: collecting radio spectrum data; selecting edge users to participate in training in response to the completion of radio spectrum data collection; performing federated learning training to obtain a trained neural network model in response to the selected edge users; outputting and saving the neural network model; and locating the radio map based on the saved neural network model. This application, by protecting user privacy, uses a neural network to accurately generalize and generate a high-precision radio map that meets the requirements, making it more suitable for intelligent scenarios.

The application relates to a wireless communication test method, system and medium based on a portable spectrum analyzer, and belongs to the technical field of radio spectrum analysis. The wireless communication test method comprises the following steps: collecting electromagnetic signal original data of a target area; constructing a dynamic electromagnetic environment baseline model; performing filtering and background subtraction processing on the electromagnetic signal original data; performing modulation mode identification, protocol analysis and terminal identification extraction on the pretreated signal matrix, generating a multi-dimensional feature vector, and performing convolution similarity matching with a legal signal feature template to assign a threat level label; dynamically reconstructing the scanning parameters of the portable spectrum analyzer according to the threat level label, generating a device parameter adjustment record, and updating an electromagnetic fingerprint library; and generating a spectrum optimization control instruction based on the threat level label and the device parameter adjustment record, and delivering the spectrum optimization control instruction to a target base station or a terminal device. The application improves the identification accuracy of threat signals and the control closed-loop linkage ability in a complex electromagnetic environment.

This invention discloses a method for optimizing the direction-finding time efficiency of pulse signals in direction-finding equipment, belonging to the fields of radio communications and radio spectrum monitoring. The design utilizes the periodicity of pulse signals, such as those from radar, to determine the currently sampled data using a single direction-finding threshold. Separate sampled data exceeding the threshold is then combined and filled in the data format required by the direction-finding equipment, eliminating time losses caused by switching. Compared to traditional direction-finding methods, this method can improve the measurable pulse time efficiency from microseconds to nanoseconds.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) may determine resource selection priority levels associated with available resources for sidelink signaling in an unlicensed radio frequency spectrum band, the resources selection priority levels may be determined based on resource reservation information of the available resources, a time domain location of the available resources, or both. For example, a first resource of the available resources may be associated with a higher priority than a second resource of the available resources in resource selection. The UE may select one or more resources for the sidelink signaling from the available resources based on the resource selection priority levels associated with the available resources. The UE may transmit the sidelink signaling on the unlicensed radio frequency spectrum band using the one or more selected resources.

This invention discloses a mobile radio spectrum monitoring system and method based on a quadruped robot platform, belonging to the field of radio direction finding technology. The system includes a quadruped robot body and a monitoring module mounted on it. The monitoring module includes: a seeker head, mounted above the robot body via an elevated frame, with a built-in array antenna for acquiring radio frequency signals; a signal processing chamber, containing an SDR receiver connected to the array antenna to receive signals; a pose sensor module to acquire the robot's pose; a data processing module to perform spatial spectrum direction finding on the signals and fuse the pose data, outputting the coordinates of the radiation source and transmitting them through a communication module; and a power supply chamber to power the system. This invention effectively integrates a quadruped robot with a radio monitoring system, improving the accuracy of transient signal capture and monitoring response efficiency. It can meet the needs of radio management departments for large-scale deployment and routine inspections in complex scenarios, contributing to the maintenance of electromagnetic space safety and order.

The invention relates to the technical field of wireless spectrum monitoring and intelligent identification, in particular to an identification method based on wireless spectrum feature position statistics. Establishing an environment frequency spectrum baseline through unsupervised clustering and a Gaussian mixture model; for a new signal, calculating an abnormal response index by using the baseline model, and comparing the abnormal response index with a dynamic threshold value to judge a potential abnormal event; the core innovation of the method is as follows: when a potential anomaly occurs, the method combines an anomaly index and a signal information entropy to calculate a wake-up cost so as to assess the analysis value of the wake-up cost; only high-value events can start fine analysis, so that computing resources are ensured to be preferentially used for processing key events, and resource waste caused by analysis of harmless signals is remarkably reduced; and if the new signal is confirmed to be legal, the model is updated online.

The utility model discloses a high-performance radio reconnaissance device, which comprises an unmanned aerial vehicle detection and direction finding integrated antenna and a radio reconnaissance processing unit, and the integrated antenna comprises a base, a first mounting plate, a second mounting plate and a shell. The base is mounted on a base of the first mounting plate, the second mounting plate is mounted on the top surface of the first mounting plate through the supporting columns, and the mounting plates are circular; eight horn antennas are uniformly mounted on the first mounting plate along the circumference, and nine dipole antenna elements are uniformly mounted on the second mounting plate along the circumference; the horn antenna and the dipole antenna element are connected with the switch matrix through a bunched cable; the radio reconnaissance processing unit completes reconnaissance and positioning of the unmanned aerial vehicle, and interferences equipment is linked to counter the locked unmanned According to the utility model, a radio frequency spectrum detection direction finding technology is adopted, and an unmanned aerial vehicle protocol analysis technology is combined, so that ultra-long distance discovery, accurate direction finding and pitch angle direction finding of the low-altitude flight unmanned aerial vehicle are realized.

The application discloses an energy efficiency adaptive regulation device and method for a spectrum detection device, and relates to the technical field of radio spectrum monitoring. The device comprises a dynamic sleep-wake module, an energy efficiency adaptive regulation module, a task priority management module and a feedback optimization module. When on standby, low-rate sampling of environmental radio frequency signals is performed, and intensity, bandwidth and periodicity characteristics are extracted. Based on target characteristic determination, noise and missed detection are dynamically adjusted to trigger wake-up. When working, gain, sampling rate, quantization accuracy and algorithm complexity are jointly adjusted according to task requirements and energy efficiency constraints, and performance trade-off instructions are generated according to task priority when resources are limited. When idle, detection, power consumption and performance data are collected, and wake-up threshold and operating parameters are updated for the next period through reinforcement learning or regression analysis. Through the device and method, invalid wake-up can be inhibited, the risk of missed detection can be reduced, and adaptive balance of energy efficiency and task performance can be achieved.