2277 results about "Radar signals" patented technology

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 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 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 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.

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.

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.

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.

The invention relates to the technical field of radar electronic countermeasures, and particularly discloses a radar radiation source open set identification method (ARPLAD) based on countermeasure reciprocity point learning. The method comprises the steps that adaptive noise reduction and feature extraction are conducted on radar signals through a feature extraction network fusing a DRSN module and an ECA module, channel-level adaptive noise reduction is achieved through the DRSN by means of a soft threshold function, and key features are strengthened through dynamic weight distribution by means of the ECA; an adversarial reciprocity point learning framework is adopted, reciprocity points are set for each known class to serve as out-of-class space representation, and known and unknown class feature spaces are separated by maximizing the distance between known class samples and the corresponding reciprocity points; introducing a central loss function to compress intra-class feature distribution, and constructing a weighted total loss function optimization network in combination with an open space limitation function; and calculating a class self-adaptive threshold value based on the training sample, and comparing the maximum distance from the sample to each reciprocity point with the threshold value during testing to judge known and unknown classes.

The invention discloses a human body posture estimation method based on radar point cloud imaging and multi-dimensional feature fusion, and relates to the cross technical field of computer vision and radar signal processing, and the method comprises the following three key technical links: firstly, improving the target resolution through spatial energy distribution estimation; reconstructing target three-dimensional space distribution by using the positive correlation between radar signal energy and a target reflection area and adopting a least square estimation algorithm; secondly, constructing a structured multi-dimensional point cloud matrix, and converting sparse radar point cloud into high-information-density imaging representation through a distance-speed hierarchical sorting strategy; and finally, designing a multi-dimensional feature fusion attitude estimation network, integrating three-dimensional convolution, a multi-head attention mechanism and a gating circulation unit, and realizing collaborative extraction of spatio-temporal features. According to the method, the problems of sparse target features, noise sensitivity and poor universality in traditional millimeter wave radar attitude estimation are solved.

A radar based, fill level measuring device for determining fill levels in containers, wherein the fill level measuring device can be applied for a large number of different applications, includes a signal production unit designed to produce a first transmitted radar signals within a first frequency band and supplementally a second transmitted radar signal within a second frequency band that does not overlap with the first frequency band and is clearly separated therefrom. The evaluation unit of the fill level measuring device can determine a first fill level value based on the first received signal. Based on the second received signal potentially another fill level value can be determined. In this way, the fill level measuring device can be adapted as regards frequency band to the particular application. The ascertained fill level can be checked for plausibility by comparing the fill level values ascertained in different frequency bands.

The invention discloses a non-contact electrocardio detection method which comprises the following steps: transmitting a frequency modulation continuous wave signal to the heart part of a human body by using an MIMO millimeter wave radar so as to obtain an echo signal reflected by the human body and pre-process the echo signal to obtain a discrete three-dimensional radar signal; enhancing the discrete three-dimensional radar signal by using a 2D beam forming technology to obtain a 2D signal; extracting a target area signal from the 2D signal, and performing phase extraction on the extracted target area signal to obtain phase data; the method comprises the following steps: constructing a WaveGRU-Net network comprising an MODWT module, a CNN module and a Bi-GRU module; and training the WaveGRU-Net network by using the phase data so as to output a reconstructed electrocardiogram by using the trained WaveGRU-Net network, thereby realizing non-contact electrocardiogram detection. According to the method, accurate extraction of the electrocardiosignal is realized, and meanwhile, the signal reconstruction precision is improved.

The invention discloses a hydropower station water level detection method and system. The method comprises the steps that an original water flow characteristic parameter set is obtained through multiple sets of Doppler effect radar flow measuring devices; performing segmentation extraction to obtain a water flow feature sub-data set; inputting a Transform network coding layer to generate a water flow feature coding vector; outputting a preliminary water level predicted value sequence through a decoding layer; a radar flow measurement model is called for correction to obtain a correction sequence; time sequence integration is carried out to obtain a water level change curve. The system comprises a multi-source Doppler radar signal acquisition unit, a spatial-temporal feature segmentation extraction unit, a Transform coding processing unit, a water level preliminary prediction unit, a radar model correction unit and a time sequence integration output unit. According to the method and system, multi-source parameters are deeply fused, the environmental adaptability is improved, the continuity and reliability of a detection result are enhanced, the water level of the hydropower station can be accurately monitored, and support is provided for scheduling decision and safety prevention and control.

The invention discloses a radar communication radiation source identification method and system based on multi-modal feature alignment, and belongs to the technical field of electronic reconnaissance and signal processing. The method comprises the following steps: preprocessing a received radar signal to generate a standardized time-frequency graph; extracting a signal feature vector through a specially designed convolutional neural network encoder; extracting a text feature vector by using a Transform encoder; through joint optimization of cosine similarity loss and physical parameter constraint loss, alignment of signal-text features in a unified vector space is realized; and finally, zero sample identification of unknown radar signals is realized through vector similarity calculation. According to the method, the problems of low recognition rate and insufficient cross-modal information fusion in a low signal-to-noise ratio environment of a traditional method are effectively solved, and the recognition precision and the system robustness are remarkably improved.

The invention belongs to the technical field of radar signal back-end processing, and relates to a millimeter wave radar tracking method in a multi-target complex environment, which comprises the following steps of: 1, processing a radio frequency signal received by a radar to generate millimeter wave radar point cloud data; 2, delimiting a radar action range, removing point cloud data beyond the range, and then carrying out point cloud clustering through a DBSCAN clustering method to obtain merge points; and step 3, starting a track, matching measured data with the track through data association gate judgment, if association succeeds, updating a target state and then outputting the track, and otherwise, executing track loss or maintenance operation by adopting a scoreboard mechanism. According to the method, the accuracy of target tracking can be effectively improved, the error tracking rate of multi-target tracking can be reduced, meanwhile, the calculation complexity of an algorithm is reduced, and the calculation time is greatly shortened.

The invention discloses a multi-source data fusion and intelligent analysis method for an intelligent traffic enterprise, and relates to the technical field of intelligent traffic, and the method comprises the following specific steps: S100, collecting video frame data, radar track data and voiceprint data of the traffic enterprise in real time, S200, calculating a quantized video definition score, S300, calculating a radar signal-to-noise ratio, and S500, calculating a video definition score. According to the method, the video, radar and voiceprint data quality can be accurately judged through multi-modal data quality real-time evaluation and credibility quantification, and a cross-modal attention weight matrix is dynamically adjusted according to a quality evaluation result in a complex environment, that is, when the video quality is reduced, the video, radar and voiceprint data quality can be accurately judged; the system automatically increases the radar data weight, ensures that high-quality data source key information is always focused, learns more accurate and comprehensive abnormal characteristics, effectively reduces the traffic accident rate, and guarantees the road traffic safety.

The invention relates to the technical field of radar signal processing and quantum computing, in particular to a vehicle-mounted radar signal interference noise reduction method based on a quantum convolution region proposal network. According to the technical scheme, the vehicle-mounted radar signal interference noise reduction method based on the quantum convolution region proposal network can efficiently extract features in a vehicle-mounted radar signal by combining the parallel processing advantages of the quantum convolution region proposal network and quantum calculation, accurately identify and suppress various complex interference signals, and improve the noise reduction performance of the vehicle-mounted radar signal. Self-adaptive processing can be carried out according to the characteristics of different interference signals, it is ensured that the system can cope with variable interference sources, the parallel processing capacity of quantum computing is utilized, rapid and accurate feature extraction and interference suppression can be achieved in the signal processing process, and compared with a traditional high-cost laser radar or visual image recognition system, the system has the advantage that the system is more accurate in feature extraction and interference suppression. On the basis of the radar signal processing method and the advantage of low power consumption of quantum calculation, lower hardware cost and energy consumption can be realized.

The invention discloses a radar signal noise reduction method for deep learning, and relates to the technical field of radar signal processing, and the method comprises the steps: converting a time domain radar signal into a two-dimensional time-frequency graph through short-time Fourier transform; frequency domain feature extraction is carried out on the two-dimensional time-frequency graph, and frequency domain feature representation is output through a DnCNN and a cascade CNN in sequence; performing time domain feature extraction on the time domain radar signal and outputting time domain feature representation; fusing the frequency domain features and the time domain features based on a multi-head attention mechanism to generate cross-domain joint features; and inputting the cross-domain joint feature into a residual shrinkage network for signal reconstruction, and outputting a denoised time domain signal. A time-frequency double-domain collaborative learning framework is constructed, and signal high-fidelity reconstruction in a complex noise environment is realized through deep fusion of time domain characteristics and a frequency domain structure distribution rule. According to the noise reduction method, an extremely low phase error and an extremely high feature retention rate can be kept in a strong noise environment.

The invention discloses an OFDM radar vital sign signal separation method based on FGO-VMD and multi-feature clustering, and belongs to the technical field of radar communication integration. According to the method, an OFDM signal radar is used for carrying out human body detection, multi-subcarrier phase difference linear regression is used for estimating displacement, thoracic cavity micro-motion signals are extracted, an FGO-VMD joint optimization model is provided, the modal number and penalty factor parameters of a VMD decomposition algorithm are adaptively determined through minimum envelope entropy, and a multi-feature clustering and weighted reconstruction strategy is designed. And in combination with frequency band screening and correlation coefficient weighting, the separation robustness of the respiratory signal and the heartbeat signal is enhanced, and high-precision separation of the vital sign signal is realized. According to the method, non-contact vital sign detection based on OFDM radar signals is achieved, the problems that in a traditional algorithm, parameters of modal numbers and penalty factors need to be manually set, and signal amplitude attenuation and modal aliasing are caused under complex noise are solved, the vital sign monitoring precision is remarkably improved, and high-precision separation of vital sign signals is achieved.

The invention discloses a composite precipitation cloud layer identification method based on a laser radar-cloud radar, and the method comprises the steps: obtaining the observation data of the laser radar and the cloud radar, and carrying out the data matching of the observation data; processing a signal of the laser radar in a height interval of 18-20 km to obtain a preprocessed laser radar signal; performing laser radar cloud boundary identification based on a VED algorithm, and endowing different laser radar cloud scores according to the signal attenuation rate and the signal gradient; removing earth surface clutters by using a threshold method based on observation of spectral width and Doppler velocity; carrying out cloud radar significant echo recognition based on a threshold method, and determining the cloud score of the cloud radar according to different reflectivity factor signal intensities; determining a light rain area and a heavy rain area according to the Doppler velocity; cloud layer boundary information and a hairy rain area are obtained through cloud layer recognition result fusion, and cloud layer recognition and rainfall area recognition are completed; according to the invention, the accuracy of weather prediction is improved.

The invention discloses a lunar radar horizon tracking method based on a dynamic search center, and solves the problems that an existing method is high in dependence on artificial experience and low in processing efficiency, and the false horizon misjudgment rate is high due to lunar soil non-uniformity interference. Comprising the following steps: performing Hilbert transform on a radar signal to extract an envelope; based on historical horizon data, predicting a dynamic search center of a current channel through Gaussian weighted average and determining a search window; screening local maximum value points in the window as candidate points, and calculating signal intensity, smoothness and edge direction scores of the candidate points; selecting an optimal candidate point as a horizon position through multi-feature linear fusion; and according to the lunar soil interface form and the signal-to-noise ratio dynamic adjustment parameters, tracking and outputting a final horizon path one by one. According to the method, historical path constraints and local signal features are fused, the identification precision and robustness of the lunar soil layered interface in the complex electromagnetic environment are remarkably improved, and the method is suitable for radar data processing of lunar exploration tasks such as Chang'e 4 and the like.

A system comprises a light source, a radar transmitter, a light receiver, a radar receiver, and a processor. The light source is configured to emit a light pulse. The radar transmitter is configured to transmit a radar signal, wherein an emission direction of the light pulse of the light source and a transmission direction of the radar signal of the radar transmitter are at least in part synchronized. The light receiver is configured to detect a reflected light pulse, the radar receiver is configured to detect a reflected radar signal, and the processor is configured to determine a dimensional representation of an environment based at least in part on the detected reflected light pulse and the detected reflected radar signal.

The invention relates to the technical field of signal processing, in particular to an unmanned aerial vehicle identification method and system based on a random forest, and the method comprises the steps: transmitting a radar signal, receiving an echo signal reflected by an unmanned aerial vehicle, carrying out the signal processing and target detection of the echo signal, and extracting a target trace point set; performing track association and filtering processing based on the target trace point set to generate a target track set; based on a target track state in a preset time window, extracting a multi-dimensional motion feature vector of the target track; and inputting the feature vector into a pre-trained random forest model, and outputting an unmanned aerial vehicle identification result. The objective of the invention is to realize low-cost and high-precision unmanned aerial vehicle identification.

The invention discloses an LPI radar modulation identification method and system based on a dynamic wavelet frequency band enhancement module and a depth residual shrinkage network, and computer equipment, and belongs to the field of radar signal identification, and the method comprises the steps: 1, receiving an LPI radar signal; step 2, carrying out Choi-Williams distribution CWD time-frequency transformation on the received LPI radar signal, and generating a time-frequency TFI image; step 3, an LW-BERSNet model is established; and processing the received LPI radar signal through the established LW-BERSNet model, and outputting a modulation type classification result of the LPI radar signal. According to the invention, high-precision identification can be realized in complex electromagnetic environments with low signal-to-noise ratio, multipath fading effect, non-stationary noise coupling and the like.

The invention provides an advanced geological forecasting method and system, and the method comprises the steps: arranging a plurality of radar measurement points on the ground surface of a to-be-constructed region according to the operation plan of tunneling equipment, so as to install a radar; before the tunneling equipment executes the tunneling operation, a radar is used for detecting whether a geological abnormal body exists under the earth surface of the to-be-constructed area or not. According to the scheme, the radar is arranged on the ground surface for detection, the problem that in a traditional method, the radar is difficult to arrange between a cutterhead and a tunnel face of tunneling equipment is solved, and therefore effective monitoring on poor excavation construction conditions is achieved; the influence of electromagnetic interference generated by underground tunneling equipment on radar signals is remarkably reduced, and the accuracy of advanced geological forecast is improved.

Methods, systems, and devices for wireless communications are described. A user equipment (UE) in a vehicle-to-everything (V2X) system may receive configuration information from an assisting node, such as a roadside unit (RSU), for calculating location information for a target UE in the V2X system. The assisting node may reflect one or more radar signals from the UE towards the target, and from the target back towards the UE according to the configuration information. That is, the assisting node may modify one or more waveform parameters of the reflection according to the configuration information. The UE may calculate location information for the target based on the reflection, such as by classifying the target as non-line-of-sight (NLOS) based on modified waveform parameters, location information of the assisting node, or both.