1558 results about "Clutter" patented technology

According to the radar moving target detection method based on small sample transfer learning and the attention mechanism, a small amount of actual radar echoes and high-fidelity scene simulation data are used as a data training set, and direct detection of a moving target under a strong clutter background can be realized. An attention mechanism is introduced by utilizing the point / line distribution characteristics of a moving target and a scene clutter in a space-time two-dimensional plane, the clutter sorting and recognition capability of the network can be ensured while the network training speed is improved, meanwhile, a small sample migration twin network is further combined, a small amount of actually measured data can be utilized to correct a target detection network, and the target detection accuracy is improved. The adaptability of the network to actual working conditions is improved, and the generalization ability is improved. According to airborne flight calibration test data analysis, under the condition of a low signal-clutter-noise ratio, compared with a traditional STAP + CFAR technology, the method has higher detection performance and lower false alarm rate. Meanwhile, based on network pre-training and a small sample transfer learning mechanism, the method has practicability in the aspect of early warning radar real-time processing and has high engineering application value.

The invention provides a self-adaptive radar target detection method, system and device and a medium, and belongs to the technical field of self-adaptive radar target detection, and the method comprises the steps: carrying out the high-resolution decomposition and Gaussian fitting of a radar echo signal, analyzing the spectral centroid and spectral width characteristics, and determining the frequency domain interval of the radar echo signal; decomposing the radar echo signal into a plurality of intrinsic mode components, and screening out the mode components in a target frequency domain interval for reconstruction; key features in the reconstructed radar echo signals are extracted from the time domain dimension and the frequency domain dimension, and features with distinguishing force on target detection are reserved through a feature screening method; the method comprises the following steps: segmenting a long-time signal in a radar echo signal into a plurality of small windows, and respectively carrying out VMD decomposition and reconstruction; and carrying out target detection on the extracted key features by using a CA-CFAR algorithm. The method improves the precision of target detection under the background of complex sea conditions and strong sea clutters, and improves the accuracy and robustness of target detection.

The invention relates to the technical field of ship navigation system safety, and provides a Beidou satellite navigation deception detection method and system based on multi-source information, and the method comprises the steps: obtaining Beidou satellite navigation data, AIS information, navigation radar data and sea chart data; performing denoising processing on the Beidou satellite navigation data, and performing space-time compensation on the AIS information; clutter suppression is carried out on the navigation radar data; calculating ship body position deviation, and verifying the ship body relative position according to the ship body position deviation; calculating the real position of the ship body and the Beidou positioning deviation, and verifying the absolute position of the ship body; performing hull course consistency verification; and performing Beidou satellite navigation deception detection according to the ship body relative position verification, the ship body absolute position verification and the ship body course consistency verification. According to the invention, multi-dimensional detection is carried out on key information such as the position and course of the ship, so that the probability that the ship falls into danger due to wrong navigation signals is greatly reduced, and the navigation safety of the ship is guaranteed.

The invention discloses a fixed dual-runway FOD detection method based on multi-source circumferential scanning, and belongs to the technical field of data fusion detection, and the method specifically comprises the steps: synchronously obtaining dual-runway multi-polarization echo data through a fixed circumferential scanning rada; performing time-varying clutter suppression by combining an adaptive clutter cancellation method matched with a dynamic background template and a runway material feature library, and generating a time-frequency domain three-dimensional feature matrix through time-frequency analysis; synchronously collecting and preprocessing image data, and marking a target area through a constant false alarm rate algorithm and image detection; a multi-source data geographic coordinate mapping model is established, data registration and cross validation are completed under a runway physical coordinate system, and real-time tracking of a real FOD target is realized based on radar and image fusion data through multi-dimensional confirmation of radar polarization / micro-motion features and image visual features; according to the method, the FOD detection accuracy and the position identification precision are improved through multi-source data fusion, the false alarm rate is effectively reduced, and a guarantee is provided for safe operation of an airport.

The invention discloses a pulse Doppler radar target detection method and system based on a background contrast attention mechanism, and relates to the field of radar target detection. The method comprises the following steps: preprocessing data to generate a distance Doppler image; constructing a neural network based on a background contrast attention mechanism; the neural network is trained, and the trained neural network is obtained; and obtaining a detection result of a pulse Doppler image to be detected by using the trained neural network. Based on a background contrast attention mechanism, the method focuses on learning clutter distribution characteristics of a background in a distance Doppler map and inhibiting background features from a feature level, thereby improving the significance of target features. A multi-scale background learner module based on a random mask and an attention mechanism module based on background contrast are designed in a targeted manner, and JS divergence is introduced into a loss function, so that background interference can be better suppressed from a feature level, and the detection performance in a low signal-to-clutter ratio scene is improved.

The invention discloses a target detection method and system based on a millimeter wave radar. The target detection method and system are used for realizing accurate detection, positioning and dynamic and static recognition of multiple targets under a complex background. According to the method, a distance-Doppler spectrogram is generated through the technical means of sliding window construction, spectral analysis, clutter suppression and the like, and candidate target points are detected by adopting an SO-CFAR algorithm. Then, determining a target position through high-resolution direction estimation and coordinate transformation, performing spatial clustering in combination with a density-based DBSCAN algorithm, and extracting a target geometric center and a bounding box; in the aspect of target tracking, Kalman filtering is used for predicting and updating the position and speed of the target, and a beam forming technology is used for enhancing a target signal, so that the target recognition stability is improved. And finally, the system performs robust dynamic and static state recognition on the target through a dynamic and static judgment module, so that high precision and robustness of the target detection process are ensured. The method can effectively cope with static background interference and dynamic target changes, and is suitable for target detection and tracking in a complex environment.

The invention belongs to the technical field of radar signal processing, and discloses a sea surface small target detection method based on optimized characteristic mode decomposition, which comprises the following steps: S1, acquiring to-be-detected signal data; s2, decomposing an original signal into a plurality of modal components by using FMD, and selecting an envelope spectrum entropy as a fitness function; s3, performing global optimization on the fitness function in the FMD by using an SOS algorithm; s4, introducing a PSO algorithm to carry out local optimization on key parameters of the FMD; s5, components with low envelope spectrum entropy values and correlation coefficients larger than a threshold value are reserved; s6, extracting an envelope spectrum entropy and frequency band energy ratio feature from the screened modal components, introducing a Gini coefficient as a weighting factor, and constructing a GSEBE joint feature; and S7, inputting the entropy value of the envelope spectrum into a DELM classifier with a controllable false alarm, and realizing target detection based on comparison between a predicted value and a judgment threshold. According to the invention, the capability of distinguishing sea clutters and target echoes is enhanced, and more accurate classification detection is realized.

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 discloses an airborne radar ground moving target stable tracking method, and belongs to the technical field of avionics, and the method comprises the following steps: S1, obtaining and fusing multi-modal measurement data; s2, clutter suppression and pretreatment; s3, target state estimation; s4, data association and track repair; s5, group target collaborative tracking; and S6, outputting a tracking result. According to the airborne radar ground moving target stable tracking method, multi-mode fusion and track adhesion are used for supplementing a single measurement short plate and repairing a broken track; by means of clutter map and optimization filtering strong clutter suppression, the state precision is improved; the multi-target tracking is optimized by using the graph model and the group target modeling, the complexity is reduced, the prior is fused, and the tracking stability of the complex scene is enhanced.

The invention relates to a radar one-dimensional range profile multi-target detection method based on CFAR and isolated forest fusion. The radar one-dimensional range profile multi-target detection method comprises the steps of obtaining radar original data and performing preprocessing to obtain radar one-dimensional range profile data; detecting a target in the radar one-dimensional range profile data by using a CA-CFAR detection adaptive threshold technology and normalizing an energy value; calculating a target score through an isolated forest method; performing weighted fusion on results of the CA-CFAR detection and the isolated forest to obtain a fusion result, delimiting a new detection threshold to confirm a target, setting a new detection threshold # imgabs0 #, and judging whether the target exists or not; according to the method, CFAR detection and an isolated forest method are combined, false alarms caused by radar clutter interference are effectively suppressed, the risks of false detection and missing detection are reduced, the target detection accuracy is improved, the method is suitable for various clutter environments, the operation speed is high, the calculation complexity is low, and the radar multi-target detection requirement in a complex scene is met.

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 tunnel concrete ground penetrating radar forward modeling data automatic generation method and system, and relates to the technical field of radar detection. The problems of lack of data sets, low modeling efficiency and insufficient signal generalization ability in the prior art are solved. According to the method, automatic tunnel concrete internal defect model generation and radar signal forward modeling simulation based on a finite difference time domain (FDTD) method are combined, various concrete structure defect models are automatically constructed through a random parameter generator according to an electromagnetic parameter range input by a user, and visual preview and verification functions are provided; and the reasonability of the model is ensured. According to the method, diversified ground penetrating radar simulation B-scan data sets can be efficiently generated, the method has clutter suppression and envelope enhancement functions at the same time, the signal quality is optimized through the filtering and adaptive gain technology, and the interpretability of images is improved. According to the method, reliable data support is provided for subsequent deep learning model training.

The invention provides a three-coordinate radar reverse modeling-based penetration aircraft detected probability calculation method, relates to the technical field of three-coordinate radar reverse modeling, and provides a multipath propagation factor dynamic correction model and an adaptive clutter suppression method through reverse modeling ship-borne radar power distribution characteristics and an environment coupling effect. And the calculation precision of the detection probability in a complex electromagnetic environment is improved. Based on a directional diagram propagation factor quantization radar wave attenuation law, and in combination with a sub-beam system constant inversion technology, the dependence of a traditional model on confidential parameters is broken through, and high-reliability simulation based on public parameters is realized. Through pre-generation of a typical scene database and cubic spline interpolation, dynamic response of a penetration strategy is supported, a multi-radar networking cooperative detection correction model is introduced, and a global detection blind area is identified.

The invention belongs to the technical field of radar signal processing, and particularly relates to a Wald-based distance extension target detection method under an interference and clutter background. Assuming that the interference subspace is linearly independent of the signal subspace and coordinates of the two subspaces are unknown, modeling clutter components as complex Gaussian vectors with a mean value of zero; and solving the maximum likelihood estimation of an unknown clutter covariance matrix, an interference coordinate vector and a signal coordinate vector by using a dimensionality reduction subspace method and an oblique symmetry transformation matrix structure, thereby establishing an RPD-2Wald detector for target detection. Simulation analysis shows that under the background of interference and unknown clutters, the detection performance of the provided distance expansion target subspace detector is superior to that of a contrast detector, and the distance expansion target subspace detector has a good inhibition effect on interference.

The invention discloses a radar adaptive multi-target tracking method under dense clutters, which relates to the technical field of radar signal processing, adaptive filtering and multi-target tracking, and effectively reduces the calculation burden by simplifying a joint probability data association algorithm, reconstructing a confirmation matrix and directly using the confirmation matrix to calculate the association probability. Secondly, a self-adaptive extended Kalman filtering algorithm is introduced, an error covariance matrix is adjusted through a self-adaptive factor, and the tracking stability is improved; and finally, introducing a life cycle to perform target track management, dynamically determining the starting and ending of the track, and accurately tracking the high maneuvering target in the dense clutter environment. The problems of high calculation complexity, poor anti-interference performance and insufficient adaptability to a high-speed maneuvering target can be solved.

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 microwave laser composite radar cloud identification method and a computer program product, and the method comprises the steps: removing ground clutters based on transfer learning and an R-FCN network, and eliminating the noise caused by the ground clutters of a microwave radar; a deep learning model is constructed by using transfer learning and R-FCN algorithms, microwave radar and laser radar echo images are input, an attention mechanism is optimized and used in the model to realize adaptive feature extraction, a multi-scale feature pyramid is introduced on the basis of an RPN network, and the recognition and detection capability of cloud regions with different scales is enhanced. Single-result joint inversion of microwave radar and laser radar cloud identification is realized by utilizing cloud boundary self-inspection, cloud feature marking, initial matching and joint inversion of cloud boundaries, and a composite identification cloud area is obtained. The method has a cloud identification function under complex atmospheric conditions such as multilayer clouds and equipment noise interference, is accurate in inversion result, is not sensitive to ground clutter and daytime background noise, and fully integrates the identification advantage of a laser radar on a thin-roll cloud area and a cloud bottom and the cloud top identification advantage of a microwave radar.

The present application discloses a GPR signal denoising method based on variational mode decomposition and singular spectrum analysis. First, the optimal mode number K is determined based on the minimum energy loss ratio method, the original signal is decomposed by VMD and K components are obtained, and then the effective signal component is obtained by the Pearson correlation coefficient method. For the small amount of noise that still exists in the effective signal component, the singular spectrum analysis is introduced for secondary filtering to further improve the signal-to-noise ratio and reconstruct the denoised signal. Simulation experiments, numerical simulations and measured data have verified the effectiveness of the above method. The method of the present application can effectively suppress background noise and clutter interference, making the signal characteristics of the abnormal body more prominent, further confirming the effectiveness and applicability of the method for processing GPR signals, and providing a valuable reference for the data processing of GPR actual data and the secondary utilization of effective signals.

Methods and apparatus for determining losses due to obstructions in a wireless communications system are described. Pairs of measurements points, one of each side of an obstruction are selected, and wireless device reported received signal energy measurements corresponding to the pair of selected locations are used to determine a path loss through the obstruction. In some embodiments, the pair of measurements points are selected based on one or more of: wireless device orientation at the time of measurement, application in use at the time of measurement, time between measurements, or location between measurements. In one example, a first selected location in the pair corresponds to an outdoor location with a unobstructed line of sight view to the base station, while the second selected location corresponds to an indoor location.

A method of simulating radar ground or sea clutter for testing and designing of radar by providing a terrain model including discrete flat ground patches each having a reflectivity, area, surface normal vector, and position within a global coordinate frame, calculating triplet values of reflected energy, range and Doppler shift for each discrete patch of the terrain model for a given position and pose of an antenna with known gain and phase characteristics within the global coordinate frame; and for a given radar receiver sample rate and Pulse repetition frequency (PRF), resampling and integrating over all patches through carrying out a 2D transformation using a non-uniform Fast Fourier Transform. The method can correct the lack of spatial correlation and increase the speed at which realistic ground clutter modelling can be generated.

The invention belongs to the technical field of intelligent wireless sensing and radar signal processing, and particularly relates to a millimeter wave radar gesture recognition feature processing method, which is particularly suitable for scenes with environment interference (such as walking of others and static clutter), and specifically comprises the following steps: S1, preliminary filtering by a self-adaptive filter; s2, carrying out improved I CEEMDAN decomposition; s3, I MF component classification and processing; s4, signal reconstruction; the experimental result shows that the method provides a robust and efficient solution for gesture recognition, and can be widely applied to the fields of man-machine interaction, virtual reality, intelligent equipment control and the like.

The invention discloses a weather radar echo intensity online calibration method, and relates to the technical field of weather radar detection. The radar collects and processes an environment signal to obtain effective clutter data; determining a relative calibration offset of the weather radar according to the effective clutter data and the relative calibration baseline; and calibrating the weather radar based on the relative calibration offset. According to the method, the weather radar is calibrated online in real time by using the ground clutter creatively, and the dependence on external equipment is reduced. And the precision and timeliness of radar echo intensity measurement are obviously improved. The maintenance cost and the operation difficulty are reduced, and the calibration inconsistency caused by manual operation errors is reduced. The calibration cost of the weather radar is remarkably reduced, continuous high-quality output of data is ensured, the accuracy and reliability of data acquired by the weather radar are remarkably improved, higher requirements of modern meteorological observation and forecasting are met, and powerful support is provided for meteorological observation and early warning and forecasting. And the long-term consistency of the data is ensured. And the method has wide adaptability.

The invention discloses a foreign matter detection method and system based on millimeter wave radar three-dimensional point cloud imaging, and the method comprises the steps: carrying out the Fourier transform of a distance dimension and a speed dimension, achieving the coherent accumulation, and effectively improving the signal energy; incoherent accumulation is achieved by means of a multi-channel technology, target information is extracted by applying a two-dimensional CFAR algorithm, so that the anti-interference capability in the recognition process is enhanced, the problem that clutter and noise interference in the environment is extremely serious is effectively solved, the detection precision is improved, meanwhile, a clustering algorithm and multi-frame point cloud data fusion are combined to solve the point cloud data sparsification problem, and the detection accuracy is improved. An overfitting phenomenon occurring when a classification algorithm model is directly used is prevented, the efficiency of target feature extraction is improved, and remote detection is broken through.

The invention discloses a non-uniform clutter and interference cooperative suppression target fusion detection method and system, and relates to the technical field of broadband radar signal processing, and the method comprises the steps: carrying out the unitary transformation of test data, a clutter covariance matrix, a target coordinate matrix and an interference coordinate matrix based on the skew symmetry of the clutter covariance matrix; constructing a distance extension target Gradient detection statistical magnitude under the condition of a known clutter skew symmetry covariance matrix; calculating the maximum likelihood estimation of the clutter skew symmetry covariance matrix; based on the maximum likelihood estimation of the clutter skew-symmetric covariance matrix and the distance expansion target Gradant detection statistic, constructing a target detection statistic of non-uniform clutter and interference cooperative suppression; performing target fusion detection on the target detection unit based on the target detection statistical magnitude and a preset detection threshold; according to the invention, the technical problems of complex construction process and high calculation complexity of the target detector in the prior art are solved.

The invention discloses a ship complex HRRP estimation method and a ship complex HRRP estimation system matched with non-Gaussian characteristics of sea clutters. The method comprises the following steps: acquiring original radar echo data; a threshold detection method is adopted for the original radar echo data to obtain a distance interval where the ship multiple HRRP is located; representing the radar echo signals in the distance interval as a vector matrix model; adopting a K-S minimum distance criterion to select a composite Gaussian model matched with the statistical characteristics of the interference vector in the vector matrix model; and estimating the ship complex HRRP by adopting a sparse optimization method corresponding to the composite Gaussian model. According to the method, a single-parameter probability model is adopted to constrain the sparsity of the ship multiple HRRP, a K-S minimum distance criterion is adopted to determine which composite Gaussian model is adopted to constrain the statistical characteristics of sea clutters, and A-D test is adopted to determine the sparse parameter q of the ship multiple HRRP; and the performance loss of ship multi-HRRP estimation caused by clutter model mismatch is obviously reduced.

An advanced driver assistance system (ADAS) radar includes at least one transmit antenna coupled with a vehicle, the at least one transmit antenna configured to transmit radar signals from the vehicle towards a scene of interest, at least one receive antenna coupled with the vehicle, the at least one receive antenna configured to receive reflected radar signals, and at least one processor. The processor is configured to execute computer-executable instructions to obtain multiple time-samples of the reflected radar signals from the at least one receive antenna at specified periodic intervals, execute an auto-correlation algorithm on the obtained multiple time-samples, normalize a ratio of zeroth lag and first lag according to an output of the auto-correlation algorithm, and compare the normalized ratio of the zeroth lag and the first lag to a specified threshold, to determine whether the reflected radar signals include an identified road clutter object.

The invention relates to a sea clutter suppression method, device and equipment based on polarization coherent filtering. The method comprises the following steps: acquiring distance-pulse echo data of a polarized radar in a sea detection scene; calculating a polarization characteristic difference angle sequence between adjacent pulses of the same distance unit of the distance-pulse echo data, and constructing pulse amplitude weighted filtering of the distance unit according to the obtained polarization characteristic difference angle sequence; and calculating a pulse amplitude weighting filter of all distance units, and performing pulse dimension Fourier transform on the distance-pulse echo data to obtain a distance-Doppler radar map after clutter suppression. By adopting the method, effective suppression of sea clutters can be realized by utilizing polarization scattering information of a target.

The invention relates to the technical field of radar echo denoising, in particular to a radar echo signal filtering noise self-adaptive suppression method and a radar echo signal filtering noise self-adaptive suppression system. Acquiring radar station position and beam pointing data, and synchronously acquiring forest vegetation data and environment interference data of a beam pointing target area to form radar environment data; the radar environment data are identified through the multi-band decision model, and multi-modal waveform parameters are obtained; based on the multi-modal waveform parameters, radar equipment is controlled to perform periodic detection on the target area, and radar echo data are obtained; the method comprises the steps of identifying radar echo data and radar environment data, extracting dynamic spectrum characteristics in the radar echo data, performing clutter suppression processing on the dynamic spectrum characteristics by using the radar environment data, calculating the confidence probability that an early fire exists in a target area, and executing space-time consistency joint verification; according to the invention, fire early warning is carried out through the confidence probability of the fire.

The invention provides a multi-channel ATI-SAR optimal fusion moving target detection method under a strong clutter background, and the method comprises the steps: firstly carrying out the local detection through a group of multi-baseline clutter removal ATI phase testing and AMF amplitude testing, and then designing global detection optimization based on an optimal fusion rule, and carrying out the global detection. According to the method, the maximum target detection performance can be realized under a constant false alarm probability. In order to facilitate implementation, the invention develops a cognitive detection framework without priori target knowledge. According to the invention, the spatial degree of freedom is fully utilized, and the information fed back by the environment is fully utilized, so that the detection performance of the weak and small target with low signal-to-noise ratio and small radial speed in the strong clutter background is effectively improved.

The invention relates to the technical field of radar signal processing, and particularly provides a radar target detection constant false alarm rate control method and system based on sample quantile characteristics, a terminal and a storage medium, and the method comprises the steps: obtaining a radar echo signal of a to-be-detected unit; the method comprises the steps of grouping radar echo signals of a to-be-detected unit, constructing a sample set, and extracting quantile features from grouped samples according to a plurality of preset quantile levels to obtain quantile feature vectors; based on the quantile feature vector, calculating to obtain a statistic of the to-be-tested unit; comparing the statistical magnitude of the to-be-detected unit with a preset judgment threshold value, and if the statistical magnitude is greater than the judgment threshold value, judging that a target exists in the to-be-detected unit; and if the statistical magnitude is smaller than or equal to the judgment threshold value, judging that no target exists in the to-be-detected unit. According to the invention, the target detection sensitivity and the anti-clutter capability are significantly improved.