2969 results about "Radar signals" patented technology

The invention relates to a multi-modal large model-based low-slow small flight target identification and classification method and system, and aims at solving the problem of monitoring and identification of low-slow small flight targets in a complex scene. According to the method, various sensor data including visible light images, infrared images, radar signals, sound features and radio signals are fused, the multi-mode large model is used for processing and fusing data of different modes, combined features are extracted, efficient detection and classification of low-speed small targets are achieved, and the method has an automatic early warning function. The method has the advantages of real-time response, automatic processing and high recognition precision, and is suitable for low, slow and small flight target defense scenes such as airports, military bases and urban safety.

The invention discloses a radar signal detection method and system based on multi-task learning of a time-frequency domain feature fusion network, and the method comprises the following steps: building a radar signal mathematical model, and obtaining various types of radar signal sequences through the mathematical model; processing the radar signal sequence to obtain a radar signal I sequence and a radar signal Q sequence; marking the radar signal I sequence and the radar signal Q sequence, and constructing a training data set; constructing a time-frequency domain feature fusion network, and training the time-frequency domain feature fusion network by using the training data set to obtain a signal detection and recognition model; and obtaining a to-be-detected radar signal, and completing radar signal detection by using the signal detection identification model. According to the method, combined modeling analysis is carried out on various radar signals, local features and global features of the signals are extracted by adopting a time-frequency domain feature fusion network from the angle of signal I / Q, and the influence of noise on signal identification is reduced by fusing the features of the frequency domain.

The invention belongs to the technical field of intelligent health monitoring, and particularly relates to an FMCW radar-based night respiration and heartbeat monitoring signal processing method, which comprises the following steps of S1, acquiring a chest vibration displacement signal of a human body by using an FMCW radar, and completing the acquisition of a radar signal; according to the recovery separation method based on the two-parameter LMS filter, monitoring and target positioning of multiple life signals can be achieved by the FMCW radar under the condition of low signal-to-noise ratio, and compared with a band-pass filtering (BPF) method and a wavelet transform (WT) method, more accurate breathing and heartbeat signals can be obtained by the method based on the two-parameter LMS filter, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. Particularly, when the breathing signal has multiple harmonic waves and the frequency changes, the method is more accurate compared with other methods, breathing and heartbeat signals can be rapidly converged by setting a proper step length, and small steady-state deviation is kept when the signals are received next time.

The invention discloses a radar signal modulation identification method and device for self-supervised contrast mask reconstruction, and belongs to the technical field of radar signal modulation identification, and the method comprises the following steps: obtaining a radar signal, constructing a radar modulation signal data set containing label data and label-free data, and converting the radar modulation signal data set into a time-frequency image; building a self-supervised contrast mask image reconstruction model comprising an online branch and a target branch; the method comprises the following steps: pre-training a self-supervised contrast mask image reconstruction model by using label-free data, performing data enhancement and random mask operation on a time-frequency image to generate double views, respectively inputting an online branch and a target branch, updating model parameters by jointly optimizing reconstruction loss and contrast loss, and obtaining a pre-training weight; and migrating the pre-training weight to a downstream identification network, freezing part of encoder parameters, and performing fine tuning by using a small amount of labeled data to obtain a radar signal modulation identification model. According to the invention, the radar signal modulation identification precision in a complex electromagnetic environment is improved.

This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for adapting a radar transmission based on a congestion level. A wireless device, such as a radar device, a UE, a base station, a vehicle, etc., may detect a congestion level of a wireless communication environment that includes the wireless device. The wireless device may have at least one of a first FOV or a first range within the wireless communication environment. The wireless device may transmit, based on the congestion level exceeding a threshold, a radar signal that corresponds to the wireless device having at least one of a second FOV or a second range in the wireless communication environment that is smaller than the at least one of the first FOV or the first range.

Access control system (1) for buildings (2) with a stationary unit (20) and a mobile unit (10), wherein the mobile unit (10) has a mobile transceiver (11) which is designed to transmit wirelessly and encrypted signals (S) for authentication and authorization of the mobile unit (10) with respect to the stationary unit (20) and for control of the stationary unit (20) by the mobile unit (10) to a stationary transceiver (21) of the stationary unit (20), wherein the stationary unit (20) comprises a control device (22) which is designed to determine an actual position of the mobile unit (10) relative to the stationary unit (20) and a mirrored position of the mobile unit (10) from the signals (S) transmitted from the mobile transceiver (11) to the stationary transceiver (21), and wherein the stationary transceiver (21) is designed to transmit radar signals (RS) and to receive radar signals (RS) reflected back to the stationary transceiver (21), wherein the control device (22) is designed to determine a position of objects from radar signals (RS) transmitted by the stationary transceiver (21) and reflected back to the stationary transceiver (21), to compare the positions of the objects with the positions of the mobile unit (10), and to verify a matching position as the actual position of the mobile unit (10).

The invention relates to an intelligent compaction monitoring method and system for a highway subgrade pavement. The method comprises the following steps: collecting construction site data of the highway subgrade pavement; wherein the construction site data comprises soil mass density, water content and temperature; inputting the construction site data into a compactness prediction model to obtain the compactness of the roadbed and the pavement; wherein the compaction degree prediction model is constructed based on a quantitative relation between construction site data and radar signals; and carrying out early warning on the compaction degree. According to the invention, intelligent and precise monitoring of the compaction degree of the roadbed and the pavement can be realized, the accuracy and the reliability of compaction quality evaluation are improved, data support is provided for optimization of a compaction construction process, and the roadbed and pavement engineering quality is effectively guaranteed.

The invention discloses a multi-scene positioning and intelligent warning system and method for industrial equipment, and the system comprises a track analysis module which obtains the radar signal data of the industrial equipment, carries out the denoising, extracts a signal intensity change feature, and generates a human body dynamic track when the matching degree of the feature and a preset metal reflection model exceeds a threshold value; the abnormity judgment module is used for acquiring the vibration sensing data of the key position, generating a vibration frequency spectrum by applying wavelet decomposition, and generating abnormal vibration alarm information if the vibration frequency spectrum exceeds a range; the intrusion alarm module is used for acquiring node signal sources, performing weighted fusion to obtain an initial positioning coordinate group, inputting the initial positioning coordinate group into a three-dimensional map model, obtaining a final coordinate result through multi-source fusion, and sending out intrusion alarm information if the result falls into a dangerous fence boundary; and the safety evaluation module is used for performing time alignment and deep fusion on the human body dynamic track, the abnormal vibration alarm information and the intrusion alarm information to generate a safety evaluation report. According to the invention, omnibearing safety monitoring and management of industrial equipment are realized.

The invention provides a wave radar anti-interference adaptive adjustment method, and belongs to the technical field of interference adjustment, and the method comprises the steps: 1, transmitting a radar signal based on a target wave radar, and collecting an interfered original echo signal from a radar receiver; 2, interference signals in the original echo signals are analyzed and extracted, and the types of the interference signals are identified; step 3, based on the interference signal type matching adaptive filtering coefficient, initializing an adaptive filter to obtain a first adaptive filter; 4, performing time dispersion processing on the frequency of the interference signal by using a frequency hopping waveform technology, inputting the interference signal into the first adaptive filter to obtain a signal adjustment parameter, and configuring to obtain a final adaptive filter; and step 5, adjusting the target wave radar based on the final adaptive filter, and evaluating an adjustment result. Radar interference is effectively suppressed through adaptive filtering and frequency hopping processing, and the signal processing precision is improved.

A method includes transmitting, via a transceiver, radar signals for object detection. The method includes determining whether a moving object is detected using received reflections of the radar signals corresponding to a current radar frame. In response to a determination that no moving object is detected using the current radar frame, the method includes determining whether a moving object is detected using received reflections of the radar signals corresponding to multiple radar frames. The method also includes generating a detection result indicating that (i) no moving object is detected using the multiple radar frames or (ii) the moving object is detected using either the current radar frame or the multiple radar frames.

The invention discloses a driver health monitoring and early warning method and system, and belongs to the technical field of automobile intelligent cabins, and the method comprises the steps: calling camera data in a DMS in an intelligent cabin, positioning the chest of a driver, and obtaining three-dimensional coordinates of the chest part of the driver in a driving cabin space; adjusting a millimeter wave radar integrated in a steering wheel to send a radar signal by combining the three-dimensional coordinates positioned to the chest part; receiving an echo signal of the radar signal interfered by the fluctuating cavity motion of the thoracic cavity part; carrying out real-time extraction on a respiration signal and a heartbeat signal based on the echo signal to obtain a vital sign signal; performing feature extraction on the vital sign signals to obtain vital sign parameters; analyzing the vital sign parameters; and when the vital sign parameter exceeds a threshold value, sending an exception handling request. According to the method, cardiopulmonary data of a driver are analyzed, and occurrence of sudden cardiopulmonary diseases such as inhibitory respiration and heartbeat pause is judged in advance.

The invention discloses a radar signal feature representation method based on self-supervised contrast learning. The method comprises the steps of obtaining radar signal time sequence data and performing preprocessing; performing data enhancement to obtain a strong enhancement view and a weak enhancement view, inputting the strong enhancement view and the weak enhancement view into an encoder to obtain a potential embedding representation, inputting the potential embedding representation into a time comparison module to perform a task of cross-view time step prediction, outputting a corresponding context vector, and calculating time comparison loss; inputting context vectors corresponding to the strong enhancement view and the weak enhancement view into a context comparison module to carry out self-supervised comparison learning and calculate context comparison loss; a total loss function is obtained through integration, a model formed by the time comparison module and the context comparison module is trained, a radar signal time sequence feature representation model is obtained, and time sequence dependence, context relation and cross-view consistency in a radar signal time sequence can be efficiently and accurately captured; therefore, the ability to understand radar signals is enhanced, and a complex radar application scene can be dealt with more flexibly.

The invention provides a large and small model collaborative unmanned aerial vehicle target tracking method, which is realized through three stages: firstly, processing multi-modal data such as images, videos and radar signals by using a multi-modal perception large model to carry out preliminary target identification so as to obtain target position information; secondly, target position information generated by the large model is transmitted to a lightweight KCF tracking algorithm for efficient tracking; and finally, when the target is lost, reactivating the large model through a dynamic feedback mechanism to perform a new round of recognition, and optimizing the overall tracking performance by combining online learning and parameter adjustment. The limitation of a traditional unmanned aerial vehicle target tracking method in the aspects of data intensive training, generalization performance, computing resource consumption and environmental adaptability is solved, and an efficient and high-precision unmanned aerial vehicle target identification and tracking method is improved.

The invention discloses a radar signal pattern recognition method based on multi-modal feature fusion, and the method comprises the steps: obtaining in-pulse IQ sampling data and full-pulse PDW data of a radar signal, and carrying out the time-frequency transformation of the IQ sampling data, and obtaining a time-frequency image; respectively preprocessing the time-frequency image and the full-pulse PDW data; performing feature extraction on the preprocessed time-frequency image and the full-pulse PDW data to obtain intra-pulse features and inter-pulse features; carrying out multi-modal feature adaptive fusion on the intra-pulse features and the inter-pulse features; and the fused features are used as classifier input data for classification and identification, and radar signal pattern identification is realized. The method shows high recognition accuracy under different signal-to-noise ratio conditions, can still maintain good performance especially under the condition of low signal-to-noise ratio, and can effectively improve the recognition accuracy and robustness.

The invention provides a Raman-meter signal all-day laser radar ratio inversion method and device, and the method comprises the steps: carrying out the denoising of signal data of a Raman laser radar in a preset time period, and carrying out the inversion, and obtaining a first aerosol backscattering coefficient profile; applying a plurality of preset laser radar ratios to the inversion of the meter laser radar signal at the same moment to obtain a plurality of second aerosol backscattering coefficient profiles; taking the first aerosol backscattering coefficient profiles at the same moment as a reference standard, calculating a relative error average value of each second aerosol backscattering coefficient profile, and obtaining an optimal laser radar ratio corresponding to the minimum relative error average value; fitting the optimal laser radar ratio with the mean value of the aerosol depolarization ratio of the meter laser radar below the boundary layer height at the corresponding moment to obtain a relation curve; and substituting the mean value of the aerosol depolarization ratio at the current moment into the relation curve to obtain the laser radar ratio. According to the invention, the inversion precision of the aerosol laser radar ratio is improved.

The invention provides a space-based radar time domain waveform adaptive method based on reinforcement learning, and the method comprises the following steps: firstly, constructing a radar signal echo model and a radar clutter echo model according to an inter-pulse frequency modulation pulse Doppler radar; calculating a signal clutter echo distance Doppler matrix according to the radar signal echo model and the radar clutter echo model; establishing a reinforcement learning environment and introducing a reward function to obtain a large amount of reinforcement learning data pairs; taking a large amount of reinforcement learning data as input, and combining with a reinforcement learning algorithm to train the intelligent agent; and finally, outputting an optimized inter-pulse frequency modulation pulse Doppler waveform by applying the trained intelligent agent, and realizing clutter position regulation and control. According to the method, the pulse Doppler waveform of inter-pulse frequency modulation is provided, so that the radar has the capability of actively regulating and controlling the clutter position.

The invention provides a real-time human body detection method and system based on a microwave radar, and is applied to the field of signal data processing. By constructing a sparse reconstruction model and combining multi-dimensional feature decomposition, the method effectively relieves the recognition difficulty caused by target signal aliasing, continuously outputs and receives radar signals, extracts distance, Doppler and angle features, constructs target distribution point clouds in a three-dimensional feature space, further utilizes angle continuity judgment and point cloud density analysis, and improves the recognition accuracy of target distribution point clouds. According to the method, the number and distribution of overlapped targets are accurately estimated, a Kalman filter is introduced for dynamic tracking for a scene in which a continuous area is not formed, continuous separation and pseudo-color image reconstruction of multiple targets are realized, and thus the resolution precision and target positioning capability of a radar in a multi-person close-range aggregation scene are remarkably improved.

The invention discloses an unknown radar radiation source sorting method based on time sequence feature clustering. The unknown radar radiation source sorting method comprises the following steps: acquiring radar signal pulse description words of pulse signals; according to the radar signal pulse description word, performing spatial clustering on the pulse signal to obtain a spatial clustering result; for each spatial clustering result, according to the radar signal pulse description word of the pulse signal in each spatial clustering result, performing time feature clustering on the pulse signal in each spatial clustering result to obtain a time sequence clustering result; and analyzing a time parallel relationship and a time continuous relationship of the time sequence clustering results, and performing pulse group sequence blending on the time sequence clustering results to obtain a sorting result. On the basis of the existing clustering algorithm, the information of the pulse signal in the time dimension is introduced, the time sequence feature clustering of the pulse signal is realized by using the ST-DBSCAN algorithm thought and introducing the TOA-PA constraint interval, and the method has strong robustness for the complex electromagnetic environment.

An automotive control system and method, includes a radar sensor, attached to a vehicle body panel, for receiving radar signals having a frequency, and processing circuitry configured with neural network encoders for encoding multiple frames of Angle-Doppler (AD), Range-Doppler (RD), and Range-Angle (RA) feature maps from the radar signals, an adaptive-directional attention block to sample rows and columns and apply self attention after each sampling instance, and a RD decoder and a RA decoder that generate RD and RA probability maps. Each map is a colorized feature map, with each pixel color representing a predicted class label for objects. An object detection component identifies the objects, and an object distance analysis component predicts a distance to the identified objects. An object velocity component predicts a velocity of the identified objects.

A radar apparatus includes: a plurality of transmission antennas including a first transmission antenna forming a first beam and a second transmission antenna forming a second beam different from the first beam; and transmission circuitry, which, in operation, performs multiplexing transmission of a transmission signal from the plurality of transmission antennas, the transmission signal being a signal to which a phase rotation corresponding to a Doppler shift amount assigned to each of the plurality of transmission antennas has been applied. In the radar apparatus, a Doppler shift interval by the plurality of transmission antennas is uneven on a Doppler frequency axis, and a first pattern of the Doppler shift amount assigned to the first transmission antenna is different from a second pattern of the Doppler shift amount assigned to the second transmission antenna.

The present disclosure generally relates to methods, systems, apparatuses, and non-transitory computer readable media for processing radar signals of a MIMO radar system. By dynamically accounting for environmental noise both spatially and temporally, systems of the present disclosure can make radar-based systems more accurate and robust against noised-induced false detections, especially with respect to the use of sparse-receivers. More precisely, systems of the present disclosure may first process MIMO radar return data to identify one or more range-Doppler-elevation (RDE) bins exceeding a candidate detection criteria. Each identified candidate detection RDE bin may then be processed by a second search procedure to identify any azimuth bins (of the particular candidate detection RDE bin) exceeding a confirmed detection criteria. Each identified azimuth bin indicates a confirmed detection of a scatterer (e.g., an object) whose azimuth, elevation, range, and Doppler-velocity indicated by the identified azimuth bin and its associated candidate detection RDE bin.

The invention discloses a substation unmanned aerial vehicle middle and low altitude automatic flight patrol safety management and control method, and relates to the technical field of intelligent operation and maintenance of power system equipment, and the method comprises the steps: setting a plurality of micro radar signal sources in a substation, and testing the signal intensity of a wireless communication system; dividing an interference area through the three-dimensional model of the transformer substation, recording the coordinates of the area causing the abnormity of the unmanned aerial vehicle equipment, and setting the area as a flight forbidden area; and generating an unmanned aerial vehicle flight path based on the transformer substation three-dimensional model, and setting a corresponding flight height and an electrified equipment distance according to different voltage class sites. According to the invention, double-AP module distributed deployment and multi-direction obstacle avoidance module configuration are adopted, so that the reliability of a communication link and the flight safety are guaranteed; based on interference area identification and differentiated parameter setting of a three-dimensional model, a precise control mechanism for different voltage levels is established, and the reliability and operation efficiency of automatic inspection of the unmanned aerial vehicle are improved.

A system and method are provided for emulating an echo signal reflected from a diffuse target, having an indeterminate shape and range in a radar environment, in response to a frequency-modulated continuous-wave (FMCW) radar signal transmitted by a radar under test. The method includes receiving the FMCW radar signal from the radar under test, the FMCW radar signal having radar signal frequencies linearly modulated between a first frequency and a second frequency; generating a bandlimited noise-like signal having frequencies within predetermined frequency limits, and a spectral shape such that amplitudes of the noise-like signal vary in a predetermined manner with respect to the frequencies; mixing the noise-like signal with the received FMCW radar signal to provide an emulated echo signal reflected from the diffuse target; and transmitting the emulated echo signal of the diffuse target to the radar under test.

A system includes a transmitter configured to transmit a radar signal towards a target object. A receiver is configured to receive a received signal in response to the transmitted radar signal. The system includes a processor configured to receive, using the receiver, a received signal, execute an iterative procedure using a desired signal prior and an interference signal prior and to determine an estimate of an interference signal component of the received signal and an estimate of the desired signal component of the received signal, wherein the interference signal prior is determined using generative modeling and each iteration of the iterative procedure executes a data consistency operation, use the estimate of the interference signal component and the received signal to determine a desired signal, and processing the desired signal to determine an attribute of the target object.

The invention relates to the technical field of radar, and discloses a three-dimensional positioning life detection radar system, and the radar system comprises a phased array antenna unit which is used for transmitting radar waves and dynamically adjusting the wave beam direction according to the target position; the signal receiving unit is used for receiving a radar signal reflected by a target; the Doppler frequency shift analysis module is used for analyzing the frequency change of the received radar signal and extracting a micro-motion signal of the target; the target motion analysis module is used for tracking the motion of the target through a Kalman filtering technology; the three-dimensional reconstruction module is used for reconstructing the three-dimensional position of the target through an electromagnetic field integral equation and multi-source sensor information fusion according to the received signals; and the deep learning module is used for identifying the micro-motion signal and optimizing target classification. By adopting the phased-array antenna unit and the beam forming technology, the technical effects of carrying out accurate three-dimensional scanning and rapid dynamic beam adjustment on the target are achieved.

Radar intercept hardware is capable of receiving and storing multiple radar signals in any context including air, land, sea or space domains as radar pulse data. However, without a priori knowledge of all of the radar emitters captured by such radar interceptors, the information or intelligence that has been gathered is of little use until such radar signals are separated or deinterleaved from the radar pulse data. The present invention is directed to model-based radar signal deinterleaver systems and methods. The characteristics of known radar models, particularly pulse repetition intervals, pulse width, time of arrival and radio frequency are used by the various embodiments of the system and method of the present invention to extract out the known signals and leave the unknown or residue signals for adjudication by a subject matter expert.

The invention discloses a two-dimensional smart noise interference method based on a centralized MIMO radar, and belongs to the technical field of radar communication.The method comprises the steps that down-conversion processing is carried out on radar transmitting signals intercepted by a digital radio frequency memory through a jammer, and the radar transmitting signals are subjected to down-conversion processing; periodic sampling is carried out on each pulse of a signal transmitted by an enemy through intermittent sampling to form a plurality of slices, then smart noise interference modulation is carried out in a distance dimension and a speed dimension, and finally, up-conversion processing is carried out on a modulation signal, so that a forwarding type two-dimensional smart noise interference signal is formed. After an MIMO radar receives an interference signal, a result obtained by a conventional signal processing method is seriously influenced by the interference signal generated in the invention. According to the method, smart noise interference is applied to an enemy radar signal in the distance dimension and the speed dimension, and an intensive false target is successfully generated, so that the detection of the MIMO radar on a real target is interfered.

The present application relates to a device and method for monitoring and early warning Low-power-consumption landslide collapse based on time division multiplexing, comprises: a camera equipment is configured to obtain monitoring images; a RF front-end is configured to transmit and receive radar signals; an intelligent sleep equipment is configured to set sleep time and power down the equipment(s) connected to it to put into sleep mode when the device requires working in a low power consumption mode; a signal processing equipment is configured to realize interference measurement and Doppler detection in a time division multiplexing manner and generate early warning or forecast information; a 5G equipment and an Ethernet port are configured to send the measurement results and monitoring images to a mobile terminal or host computer. The present application realizes the joint work of Doppler detection and interference measurement within one radar device through adaptive time division multiplexing.

The invention discloses a radar and WiFi channel state information fused dual-mode household respiration monitoring device which comprises a radar signal collecting and processing module used for detecting a human body respiration signal through a millimeter wave FMCW radar and processing an I / Q orthogonal echo signal obtained by the FMCW radar to obtain a respiration signal; the WiFi signal acquisition and processing module is used for acquiring a CSI signal containing respiratory activity information through a WiFi wireless network card and calculating a CSI ratio of a receiving channel so as to remove time-varying noise and further extract a respiratory signal; the data communication module is used for constructing a networking system based on a millimeter wave FMCW radar and WiFi through different network cable transmission modes; and the breathing information fusion module is used for dynamically fusing the two breathing signals. Starting from multi-sensor respiration signal extraction, accurate detection of personnel respiration signals in a home indoor environment is realized through a fusion algorithm, and the detection method is effective, feasible and reliable in performance.

It is suggested to process radar signals including (i) determining a variation of at least one radar parameter provided from at least one radar device; (ii) determining an estimated value of at least one radar parameter from an error compensation vector; and (iii) determining a safety condition based on the variation and the estimated value for the respective radar parameter.