912 results about "Radar detection" patented technology

The invention discloses an offshore small target radar detection method and system, and relates to the technical field of radio detection. The method comprises the following steps: acquiring radar echo data, a visible light image, an infrared image, an AIS signal and navigation attitude data in real time; performing sea clutter suppression and pre-filtering on the radar echo data, and correcting target plots in combination with a historical radar plot set to obtain a radar single-source target track set; performing target detection on the visible light image and the infrared image to obtain an optical target detection set and an infrared target detection set; the radar single-source target track set, the optical target detection set, the infrared target detection set, the AIS signals and the navigation attitude data are associated and fused, and finally deception feature recognition and consistency verification are performed to obtain an updated fused target track set; according to the method, man-made confrontation and cheating behaviors on the sea can be accurately identified, and reliable support is provided for safety monitoring on the sea, maritime affair supervision and emergency response.

The invention discloses a phased array weather radar calibration method based on a generalized metal ball calibration theory. Comprising the following steps: in a microwave anechoic chamber, collecting two-dimensional antenna pattern data of a phased array weather radar in at least one working mode and pointing to a plurality of different beams; performing numerical integration on the two-dimensional antenna pattern data, calculating a combined beam main lobe solid angle of the transmitting antenna and the receiving antenna, and calculating an equivalent beam width product; calculating a calibration theoretical reflectivity factor of the metal ball based on the product of the theoretical radar scattering sectional area and the equivalent beam width of the metal ball; actual detection echo data of the phased array radar on the metal ball under different working modes and beam pointing is acquired, and an actual reflectivity factor is calculated; and comparing the actual reflectivity factor with the calibration theoretical reflectivity factor to complete calibration of the phased array weather radar. Aiming at the non-Gaussian working mode of the phased array weather radar, the reflectivity factor of the phased array weather radar can be quantitatively evaluated, and the radar detection performance is improved.

The invention relates to a solid-state laser radar ranging method and system based on neural network processing, and belongs to the technical field of laser radar ranging, and the method comprises the steps: obtaining a laser echo signal when laser radar detection is carried out on a surrounding target object, and obtaining a photon counting histogram based on the laser echo signal; preprocessing the photon counting histogram, and inputting the preprocessed photon counting histogram into a pre-trained neural network model to obtain a peak confidence map containing an echo peak value and a peak time offset map; on the basis of the peak confidence map and the peak time offset map, obtaining one or more echo peak values of which the confidence exceeds a predetermined threshold and time position information of the one or more echo peak values of which the confidence exceeds the predetermined threshold; and based on the time position information, the distance information with one or more target objects is determined, so that the time position information measurement accuracy of one or more echo peak values is improved, and the laser radar ranging accuracy is improved.

The invention discloses an external damage hidden danger identification method and system based on AI image and radar dual verification, and relates to the technical field of artificial intelligence and multi-source perception fusion, and the method comprises the following steps: extracting the motion trail features of perception data in a target region, solving a coordinate transformation matrix through an iterative nearest point algorithm, carrying out the time sequence alignment, and carrying out the recognition of the motion trail features of the perception data; obtaining the calibrated sensing data; taking the calibrated image data and the calibrated radar data as input, and outputting a radar detection result and a visual identification result; projecting radar coordinates to an image coordinate system according to a coordinate transformation matrix based on a visual identification result and a radar detection result, outputting a space overlapping degree, and generating a matching result set; carrying out confidence fusion on the matching result set through a Bayesian probability model, and judging an external damage hidden danger level in combination with a radar detection result; according to the invention, through the AI vision and radar dual verification fusion technology, the problems of inconsistent multi-source perception and low external damage hidden danger identification precision are solved.

The invention relates to the technical field of wind power generation, in particular to a wind turbine generator deformation vibration monitoring device based on millimeter wave radar. The core function module comprises a benchmark sensing radar, a blade monitoring radar, a reference beacon, an inertial measurement unit, a data synchronization and acquisition unit and a signal processing unit; a reference beacon fixed on a substrate is detected through a reference sensing radar, an absolute motion vector A of the reference sensing radar is solved, then the absolute motion vector A is converted into an absolute motion vector B of a blade monitoring radar, then data C measured by an IMU (Inertial Measurement Unit) is converted into data D of the blade monitoring radar in a coordinate system of the blade monitoring radar, and the blade monitoring radar can be used for monitoring the blade. The absolute motion vector E of the blade monitoring radar is fused and extracted through an extended Kalman filter, and is finally used for compensating original data of blade measurement by the blade monitoring radar, so that the influence of cabin motion on deformation vibration monitoring is eliminated.

The invention discloses a robustness tracking filtering method for group targets, and belongs to the field of radar and signal processing. The implementation method comprises the following steps: establishing a kinetic model of a group target and a three-coordinate ground-based radar detection probability and measurement model; bayesian recursion of a multi-target state random finite set is realized by a Poisson random finite set, and an intensity function of the Poisson random finite set is described in a Gaussian mixture form. Estimating the maximum motion distance of the target between two adjacent filtering steps based on the dynamic characteristics of the group target, and achieving the detection of the target; the detection probability is described by using Bernoulli distribution, under the condition that the clutter intensity does not exceed the real target intensity, the prior clutter intensity is taken as a threshold value, the detection probability is adaptively adjusted in the filtering iteration process, the respective weights of the measurement irrelevant part and the measurement relevant part are updated, and the strong-robustness multi-target tracking under the condition of sensor leak detection is realized. The method also has the advantages of strong robustness, high tracking precision and small calculation amount.

The invention relates to a fixed-wing unmanned aerial vehicle track anomaly detection method and device and a medium, and the method comprises the steps: building a radar detection noise model based on a radar equation and a signal-to-noise ratio formula; on the basis of the motion equation, simulating the motion process in normal and abnormal states to generate a real track, and adding noise to the real track by using a radar detection noise model to obtain noise-containing simulation track data; constructing a dynamic graph based on the simulated track data; inputting the dynamic graph into a space-time diagram neural network model; the time-space diagram neural network model comprises a diagram attention network layer, a long-short term memory network layer and a linear layer which are connected in sequence; and performing feature extraction and classification on the dynamic graph through a time-space diagram neural network model, and outputting a track anomaly detection result. Therefore, precise detection of tiny track abnormalities of the fixed-wing unmanned aerial vehicle, especially gliding faults and the like, is realized.

The invention relates to the technical field of low-altitude unmanned aerial vehicle detection, in particular to an unmanned aerial vehicle detection method based on radar infrared visible light multi-mode fusion, which comprises the following steps: S1, respectively inputting an obtained unmanned aerial vehicle infrared image and an obtained visible light image into two visual branches of a double-flow feature extraction network, and extracting scale features by adopting a lightweight convolutional backbone network; s2, acquiring three-dimensional coordinate data of the unmanned aerial vehicle after radar detection and clutter removal processing in real time, and projecting the three-dimensional coordinate data into two-dimensional image coordinates through a camera external parameter matrix; s3, generating a two-dimensional Gaussian distribution probability graph based on the real-time two-dimensional image coordinates, and inputting the two-dimensional Gaussian distribution probability graph as a mask into the radar feature extraction network to extract a radar feature graph; and S4, under the same scale, performing dynamic weighted fusion on the scale features of the infrared branches and the visible light branches to generate a visual fusion feature map. The method greatly improves the capability of detecting the low-altitude unmanned aerial vehicle.

The invention discloses a multi-source data fusion vehicle anti-collision system based on a 4D millimeter-wave radar, which is applied to the technical field of vehicle active safety, and comprises a 4D millimeter-wave radar detection module used for collecting 4D information of targets around a vehicle and distinguishing a pavement target from an air interference target; the vehicle state detection module is used for synchronously acquiring running parameters of the vehicle at the same acquisition rate as that of the radar detection module; the data fusion processing module is used for carrying out fusion and noise reduction processing on the 4D information and the running parameters of the vehicle on the basis of a fusion algorithm of Kalman filtering and a DS evidence theory; the collision risk estimation module is used for calculating the collision time and the collision probability based on the fusion data and constructing a two-parameter risk level judgment model based on the collision time and the collision probability; and the anti-collision execution module is used for executing a corresponding anti-collision intervention action according to the risk level. The accuracy, the stability and the adaptability of the vehicle anti-collision system are effectively improved.

A lidar system and method. Each emitter of the emitter array emits a light beam directed toward the FOV through the scanning unit. In one embodiment, an emission light beam guided according to an alternating energy level illumination protocol enables detection under halo, in which a first light beam emitted by a first emitter and having a first energy level illuminates a first FOV region, and a second light beam emitted by a second emitter and having a second energy level illuminates a second FOV region. And a second light beam emitted by the second emitter and having a second energy level irradiates a second FOV region adjacent to the first FOV region, wherein the first energy level is lower than the second energy level. In another embodiment, an emission light beam guided according to an offset illumination protocol enables detection under halo, where a first light beam emitted by a first emitter in a first plurality of scan lines illuminates the FOV portion at a first time and a second light beam emitted by a second emitter in a second plurality of scan lines at a second time. And a second light beam emitted by the second emitter in the second plurality of scan lines illuminates the FOV portion at a second time after the first time. Each detector of the detector array detects a reflection of the emitted light beam.

The invention relates to the field of shield construction, in particular to a shield tunnel influence area boulder detection and intelligent construction method which comprises the steps that S1, a horizontal drill hole is drilled along the design axis of a shield tunnel, geological radar detection is adopted in the horizontal drill hole, and first detection data are obtained; s2, based on the first detection data, preliminarily identifying a boulder abnormal area in front of the tunnel; s3, for the boulder abnormal area, vertical drill holes are formed, cross-hole CT detection is carried out, and second detection data are obtained; s4, fusing the first detection data and the second detection data, and constructing a three-dimensional geologic model; and S5, based on the three-dimensional geologic model, performing inference analysis through the knowledge graph, and generating an adjustment suggestion of the shield construction parameters. The geological radar and the cross-hole CT are combined to maximize the detection effect under limited cost, and through data fusion, construction of a three-dimensional geological model, reasoning analysis of a knowledge graph and generation of adjustment suggestions of construction parameters, intelligentization of construction control is realized, and safety is improved.

The invention discloses a three-dimensional ground penetrating radar detection method, system and equipment for road roadbed diseases and a medium, and relates to the technical field of road engineering detection. Controlling a high-frequency antenna and a low-frequency antenna to emit electromagnetic waves to a road roadbed based on the movement of the detection vehicle so as to receive reflected high-frequency data and low-frequency data; processing the high-frequency data and the low-frequency data based on the positioning attitude data to obtain processed high-frequency data and processed low-frequency data; processing and fusing the processed high-frequency data and the processed low-frequency data to obtain a full-band reflection coefficient sequence; calculating the full-band reflection coefficient sequence to obtain broadband geological radar data; inputting broadband geological radar data into the convolutional neural network model to obtain a disease type and a disease semantic segmentation map; and generating a three-dimensional visual model of the roadbed disease based on the disease semantic segmentation map, and outputting maintenance suggestion data matched with the disease type. By implementing the technical scheme, the accuracy of deep hidden disease recognition is improved.

The invention relates to the technical field of laser radars, in particular to a non-aliasing frequency-modulated continuous wave radar detection system and method, and the system comprises a light source, a beam splitter, a circulator, a 90-degree mixer, a signal conversion module and a signal processing unit. The signal processing unit is configured to construct a composite vector signal S based on the first electric signal I and the second electric signal Q, S = I + jQ, j is an imaginary part operator, obtain a double-sideband spectrum based on the composite vector signal S, determine the frequency of a signal peak in an upper frequency sweeping period and the frequency of a signal peak in a lower frequency sweeping period in the double-sideband spectrum, and determine the frequency of the signal peak in the upper frequency sweeping period and the frequency of the signal peak in the lower frequency sweeping period; and obtaining the distance and speed of the target to be measured based on the frequency of the signal peak in the upper frequency sweeping period and the frequency of the signal peak in the lower frequency sweeping period. According to the invention, the problem of signal aliasing during detection of the to-be-detected target can be solved at least.

The application discloses a kind of millimeter wave radar and the fusion method and system of visual identification target, the method comprises: obtaining the data information of multiple first target points and multiple second target points;The first target point represents the target object detected by millimeter wave radar sensor, and the second target point represents the target object detected by camera;With the first target point as the center, the absolute threshold set is expanded longitudinally and transversely, to form the first confidence interval;With the second target point as the center, the relative threshold is expanded longitudinally and transversely to form the second confidence interval;The size of the relative threshold is linearly related to the distance and speed of the second target point;KM algorithm is executed to fuse, as many as possible millimeter wave radar detected first target points and camera detected second target points are one-to-one associated as the same target object.The method can improve the target fusion effect and reduce the generation of virtual target.

This disclosure provides systems, devices, apparatus, and methods, including computer programs encoded on storage media, for bistatic radar assistance sensing. A radar-receiver receives (210), from a radar-transmitter, a configuration message that configures the radar-receiver to assist the radar-transmitter with bistatic radar sensing. The radar-receiver receives (214) a reflection of the radar signal. Responsive to the receiving the reflection of the radar signal, the radar-receiver transmits (218), to the radar-transmitter, a radar measurement report message. The radar-receiver receives (202), from the radar-transmitter, a radar capability enquiry. The radar-eceiver transmits (204), to the radar-transmitter in response to the radar capability enquiry, a radar capability response that indicates radar capabilities supported by the radar-receiver for the bistatic radar sensing.

The invention discloses a radar interference effect evaluation method based on a constraint learning dynamic Bayesian network, is applied to the field of radar interference evaluation, and aims at solving the problem that the accuracy of interference effect evaluation is reduced due to radar detection data missing in a complex electromagnetic environment. Meanwhile, parameter constraints of five types of evaluation indexes and interference effect grades are defined; secondly, constructing a constraint learning dynamic Bayesian network, and learning a conditional probability and a transition probability under a data missing condition; then, proposing a prior constraint expectation maximization algorithm, converting parameter learning into an optimization problem with constraint by combining convex optimization, and overcoming the defects of a traditional expectation maximization algorithm; secondly, a cloud model is introduced to quantify discrete probability distribution into a continuous interference degree value; finally, simulation shows that the method can effectively improve parameter learning stability and evaluation accuracy under the conditions of suppressing and deception jamming and single index deficiency, and provides a reliable scheme for radar jamming effect evaluation in a complex environment.

The invention discloses a deformation rolling stone integrated road slope radar monitoring system and a detection method, and belongs to the field of radar detection, the deformation rolling stone integrated road slope radar monitoring system comprises a signal processing module, a multi-channel radio frequency module and an antenna array module, the multi-channel radio frequency module is connected with the signal processing module through a hardware line, and the antenna array module is connected with the signal processing module. The antenna array module is connected with the multi-channel radio frequency module through a hardware line; the signal processing module comprises a programmable main control chip, a data memory, an analog-to-digital converter and a direct digital frequency synthesizer, a line of the signal processing module is also connected with a camera, and the system also comprises a power supply module for supplying power to the signal processing module, the multi-channel radio frequency module and the camera. According to the deformation and rolling stone integrated road slope radar monitoring system and the detection method, deformation and rolling stone integrated monitoring is realized, two sets of equipment do not need to be arranged, the cost and the arrangement difficulty are reduced, early warning is reliable, the environmental adaptability is high, and the requirement for long-term field road monitoring is met.

The invention relates to the technical field of microwave remote sensing, in particular to a Ku, Ka and W three-frequency-band meteorological radar multi-source joint detection system and method and a storage medium, and the system comprises an antenna feeder subsystem, a main control computer, an integrated digital processor, a Ku / Ka integrated radio frequency machine, a Ku-frequency-band power amplifier, a Ka-frequency-band power amplifier, a W-frequency-band transmitter, a W-frequency-band power amplifier, a W-frequency-band receiver and a servo mechanism. The system is designed according to a working mode and working parameters of a main control computer, realizes multiple functions such as Ku / Ka / W single-frequency-band independent working or multi-frequency-band simultaneous working and the like, and has co-polarization and cross-polarization echo signal receiving capability; by controlling the servo mechanism, two modes of fixed-point detection and conical scanning detection are realized, and detection requirements of different meteorological elements are met; the detection capabilities of three frequency bands of Ku, Ka and W are integrated in the system, the detection mode is flexibly configured according to weather conditions and detection requirements, the system has the multi-frequency-band, multi-polarization and multi-element integrated detection capability, and the detection efficiency of the meteorological radar is improved.

The invention discloses an emergency method for extreme weather prediction of a power system. The method comprises the following steps: comprehensively analyzing data from a meteorological satellite, a ground meteorological station and a radar detection system by using a multi-source data fusion algorithm; developing a precise weather prediction model by integrating numerical weather prediction, machine learning optimization and data visualization technologies; carrying out vulnerability analysis on the key components of the power system to establish a risk assessment model, and assessing the operation risk of the power system under the extreme weather event; based on a risk assessment result, a real-time monitoring system and an automatic early warning mechanism are established, and an early warning signal is sent to an electric power system operator in time; strategy measures are formulated and implemented according to different weather situations, so that reliable power supply of the key area is ensured; and according to geographical and climate conditions of different regions, configuration and function optimization are carried out on the power management software. Through accurate meteorological data processing and model prediction, the weather prediction accuracy is improved, and early warning information is sent out in time.

The present application relates to the technical field of radar detection, and particularly relates to a particle flow multi-parameter detection method based on MIMO-FMCW radar, a medium and equipment, the particle flow multi-parameter detection method based on MIMO-FMCW radar comprises the following steps: S1, arranging a virtual channel array structure of × in a detection area, and obtaining a digital signal matrix of multiple frames of measurement data corresponding to the detection area by using the virtual channel array structure; S2, obtaining multiple parameters of the particle flow in the detection area based on the digital signal matrix.The present application synchronously obtains data in the time, frequency and spatial domain by using the MIMO-FMCW radar system, realizes the joint measurement of the multiple parameters of the particle flow, can extract the speed information by the Doppler effect of the FMCW signal, can form a virtual channel to extract the angle information, and simultaneously combines the signal amplitude to estimate the concentration, so that comprehensive particle flow movement characterization is realized.

The invention discloses a steel plate cutting method and device, a medium and equipment, and belongs to the field of cutting. According to the method, three-dimensional coordinates of all points on the surface of the head of a steel plate are generated by obtaining real-time motion parameters of the steel plate and the head distance and angle detected by a millimeter-wave radar and combining a preset coordinate conversion model. And then, converting the three-dimensional coordinates into a steel plate head shape image by using a morphological reconstruction model constructed based on preset three-dimensional point cloud data and morphological feature extraction rules. According to the steel plate cutting method and device, accurate positioning and efficient cutting are achieved, the production efficiency and the material utilization rate are improved, and the problem that the steel plate cannot be accurately cut in the prior art is effectively solved.

The invention discloses a target detection system and method applying a millimeter wave radar, and relates to the technical field of target detection, and the system comprises a millimeter wave radar detection module, an auxiliary sensing module, a signal processing module and a data interaction module. The millimeter wave radar detection module is used for collecting direct millimeter wave signals and multipath reflection wave mixed signals through a radar. The auxiliary sensing module is used for acquiring a scene image and a vehicle motion state through a high-definition camera and an inertial measurement unit; the signal processing module is used for carrying out feature extraction and space-time filtering optimization on the collected signals; the data interaction module is used for outputting a target detection result; according to the invention, the direct wave signal and the multipath reflection wave signal in the collected signals are accurately distinguished through the specific analysis and evaluation parameters, and the authenticity of the target represented by the point cloud is verified through the analysis of the point cloud position change rate. The effect of detecting the false target point cloud appearing in the point cloud image caused by the multipath reflection wave signal is achieved.

The invention provides a drilling operation method adopting a pipe shed drilling machine, and belongs to the technical field of drilling operation of drilling machines. Initial positioning deviation is controlled through three-dimensional positioning calibration of a guide pipe and a space stabilization structure, drilling track deviation correction is achieved through real-time monitoring of a hole inclination angle and an eccentric drilling tool; the method comprises the following steps: dynamically adjusting drilling parameters according to a geological radar detection lithology type, establishing a slurry seepage diffusion fluid-solid coupling model to calculate a slurry diffusion radius and a filling rate, establishing a double-layer game optimization model to solve a Nash equilibrium point of grouting pressure and a slurry water-cement ratio, and adopting double-slurry pre-plugging combined with superfine cement reinforcement for a fracture zone. A drill rod and stratum interaction finite element model is established to recognize the dangerous section position, and a stabilizer or a thickened drill rod is additionally arranged, so that the technical problem that grouting reinforcement parameters and drilling track control precision are difficult to optimize synchronously in the pipe shed drilling process is solved.

The invention relates to the field of low-altitude security and protection, and discloses an intelligent identification and interference countering system of a low-altitude aircraft, which comprises a radar detection module, a radio detection module, a data fusion module, a data processing module and an early warning countering module, through cooperative detection of radar and radio, the micro-motion, track and signal fingerprint features of a target are obtained; performing deep analysis and time sequence verification on the fused multi-dimensional features by using a pre-trained unmanned aerial vehicle identification model to realize high-confidence unmanned aerial vehicle identification, model identification and threat level evaluation; and then, according to the dynamically calculated comprehensive threat index, a countering process including dynamic review and effect evaluation is started, countering means can be adaptively selected and switched, and accurate, layered and effective disposal of the suspicious unmanned aerial vehicle is realized. According to the method, the problems of low identification accuracy, high false alarm rate and single and rigid countering measures in the prior art are solved, and the effectiveness of low-altitude security and protection is improved.

The invention belongs to the technical field of intelligent traffic, and particularly relates to a vehicle-road cloud integrated communication method based on communication-perception-reflection cooperation. Comprising the following steps: step 1, constructing an integrated sensing and communication system model assisted by an intelligent reflecting surface; step 2, base station active beam forming optimization based on equivalent conversion; thirdly, IRS passive beam forming is optimized through multi-agent reinforcement learning; step 4, alternative optimization and system deployment; the method is used in the scene that obstacles such as buildings and large vehicles exist on urban roads, and the problems of communication interruption and sensing failure in a non-line-of-sight area are solved through an intelligent reflecting surface auxiliary integrated sensing and communication system. According to the method, the active wave beams and the passive wave beams of the base station are alternately optimized, and the communication rate and the radar detection performance are jointly improved on the premise that accurate channel state information is not needed.

According to the vehicle-mounted radar automatic leveling device and the control method thereof, the anti-overturning radius of a platform is increased by expanding supporting arms, the supporting arms are folded on the two sides of the platform through connecting rod mechanisms in the folded state, and the states of the supporting arms are locked through electric lock pins; in the working state, the supporting arms are unfolded through the four-connecting-rod mechanism, the working state of the supporting arms is fixed through mechanical dead points of the four connecting rods, the positions of the supporting arms are further locked through the lock pins, and it is guaranteed that the supporting arms do not shake when the equipment is subjected to transverse force loads; besides, the leveling device is simultaneously provided with a gradienter and a clinometer, the torsion conditions of the front and back structures can be monitored in real time, the data difference of the gradienter and the clinometer is considered in a leveling algorithm, and when the torsion is too large, adjustment is carried out; the leveling control algorithm in the scheme is combined with the leveling device in the scheme, so that the vehicle-mounted radar can be quickly and quickly stabilized in a complex field environment, and the radar detection and tracking precision can be improved.

The application discloses a traffic radar pitch angle installation error calibration method and device and a storage medium. The method comprises the following steps: changing the frequency of a traffic radar according to a preset strategy in a preset frequency range; sequentially obtaining echo signal energy values of a preset target under different frequencies; if the obtained echo signal energy values meet preset conditions, determining the frequency corresponding to the maximum echo signal energy value as the working frequency of the traffic radar, so as to calibrate the traffic radar pitch angle installation error; wherein the preset target is arranged on the ground, and the distance between the preset target and the projection point of the traffic radar in the horizontal direction of the ground is the length of the horizontal projection of the central beam of the traffic radar when the traffic radar is installed at an expected pitch angle. The traffic radar pitch angle installation error calibration method and device and the storage medium not only calibrate the traffic radar pitch angle installation error at a low cost, but also solve the problem that the pitch angle installation error affects the detection range and the capture rate of the traffic radar.

A user interface system enables communication through inaudible speech by detecting physiological signals associated with inner voice communication. The system includes a wearable device positioned in, on, or around a user's ear, housing one or more sensor modalities such as electromyography (EMG) sensors for detecting muscle movements, functional near-infrared spectroscopy (fNIR) sensors for brain activity, and / or detection-and-ranging systems using sonar or radar for micro-deformations. Pre-processing modules condition the sensor data, which is then analyzed by one or more machine learning models to reconstruct the content of the user's inaudible communication and produce output representations. The system enables high-bandwidth natural language interaction without audible vocalization, maintaining privacy while supporting applications including AI assistant interaction, real-time translation, secure authentication, and inter-party communication.