679 results about "Clutter" patented technology

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 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 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 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 discloses an indoor personnel trajectory tracking and anti-interference method based on a millimeter wave radar. The method comprises the following steps: firstly, dividing echo data into dynamic and static point cloud branches for respective processing; performing static clutter suppression on the dynamic point cloud branch, then performing speed dimension fast Fourier transform, and performing direct processing on the static point cloud branch to obtain a distance-Doppler spectrum; determining a target signal and a background clutter; carrying out point cloud screening by taking the point with the strongest energy as a spectrum peak center, and constructing compact representation of a target; and finally, predicting the target state of the current frame, and carrying out target track association. And carrying out track intersection judgment and recording an intersection state, and outputting a human body target position and a track. The method provided by the invention can dynamically adapt to human body sensing requirements in different scenes by combining various correlation analysis technologies, has relatively low calculation complexity and relatively high robustness, and is particularly suitable for human body sensing detection tasks in low-power-consumption application scenes. The system has the advantages of low cost, small size and low power consumption.

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 target track association method in a multi-target environment. The method comprises the following steps: firstly, preprocessing a new received trace point; secondly, predicting the next position of each track by using the existing track historical information and adopting a Kalman filtering algorithm, meanwhile, adjusting a state transition matrix and a process noise covariance matrix in real time by considering factors such as ocean current and wind direction in an offshore environment, and adjusting an observation noise covariance matrix according to meteorological information; then comprehensively considering distance, speed difference and motion direction difference to calculate a correlation degree magnitude between a new receiving trace point and each predicted track position; and finally, judging whether the newly received trace point belongs to the existing target track or forms a new target by adopting a threshold judgment method according to the correlation degree value, and updating the state of the track. The method can effectively cope with multi-target intersection, observation loss and clutter interference, improves the stability of a matching result, can realize continuous tracking of a marine target, can adjust parameters according to the environment and target characteristics, and has high adaptability.

The invention relates to the technical field of radar signal processing, in particular to a ground penetrating radar reinforcing steel bar clutter suppression method based on physical constraints, and mainly solves the problem that a data set is difficult to obtain in an existing ground penetrating radar reinforcing steel bar clutter removal method based on deep learning. The method is an improved method based on the CUT network, the CUT network structure and a comparative learning mechanism determine that the requirement of the network for the data size of a data set is low, a waveform smoothness constraint is added on this basis, the clutter removal effect and generalization ability are improved by introducing physical prior, the physical constraint serves as a regularization item, and the regularization efficiency is improved. The problem that a CUT network is prone to model collapse under a small data set is solved. Finally, the improved model is compared with other models through different evaluation indexes, and the result shows that the improved model has more advantages.

The invention discloses an SAR ship image detection method based on an improved YOLOv11 model, an improved C2PSA module C2DyMoETAttn is introduced, the core innovation point is that a PSABlock module is replaced by a DyMoETAttnBlock module, the DyMoETAttnBlock module fuses a Dynamic Tanh activation function, a Mona module, a TSSA attention mechanism and a frequency domain enhancement feedforward network (EDFFN), multi-dimensional modeling and robust enhancement of features are realized, and the detection accuracy is improved. The feature expression capability and the noise suppression performance under the background of small targets and complex sea clutters are effectively improved; in the deep feature fusion stage, a C3k2 module of YOLOv11 is optimized, an ScConv structure is introduced, adaptive fusion of space and channel features is realized through a joint reweighting mechanism of SRU and CRU, and the multi-scale target discrimination capability and feature selectivity are enhanced; on the bounding box regression layer, a Focaler-MPDIOU loss function is provided, and a Focaler-IoU sample difficulty adaptive mechanism is combined with MPDIOU positioning matching constraint, so that the learning ability of the model for small targets and shielded targets is enhanced, and the bounding box positioning precision and convergence stability are improved.

The invention discloses a clustering algorithm for Terahertz radar extended target tracking. The clustering algorithm comprises three steps of drawing an azimuth distance map by using original data, carrying out two-dimensional unit average constant false alarm rate detection on an azimuth distance dimension, and clustering by a connected domain labeling method. In the first step, a grid interpolation method is used for carrying out zero interpolation on the reflection intensity of discrete points, so that subsequent calculation of background noise is facilitated; in the second step, a threshold value is calculated by setting related parameters, and part of clutter interference is filtered out; in the third step, adjacent data points are communicated through an eight-field method, then morphological closed operation is designed to merge adjacent targets, the problem of excessive segmentation is effectively avoided, then a minimum area threshold value is added to filter clutter interference, effective targets are reserved, and finally the clustering effect is more remarkable through target center point labeling. The clustering algorithm for Terahertz radar extended target tracking is low in cost, high in processing speed and remarkable in effect, and has certain application value in the fields of Terahertz radar extended target tracking and the like.

The invention discloses a radar weak target detection and positioning method based on four-polarization time-frequency feature fusion. The method comprises the following steps: constructing an initial four-polarization time-frequency feature detector; the initial four-polarization time-frequency characteristic detector is constructed by introducing four parallel Backbones into a YOLOv11 network; one parallel Backbone correspondingly processes one polarization channel; training the initial four-polarization time-frequency feature detector by using a pre-constructed training label set to obtain a four-polarization time-frequency feature detector; calculating detection statistic distribution based on the confidence corresponding to each pure clutter sample map, and determining a detection threshold according to the detection statistic distribution; based on a to-be-detected time-frequency diagram, a radar weak target detection and positioning method which is sufficient in feature utilization, high in detection capability and stable is provided by using a four-polarization time-frequency feature detector and a detection threshold.

The invention provides a low-altitude weak target detection method and device based on synthetic wavelength, which can be applied to the field of target detection. The method comprises the following steps: imaging echo data of a to-be-detected area without any to-be-detected target to obtain a first image; imaging the echo data of the to-be-detected area with the to-be-detected target to obtain a second image; performing interference operation on the first image and the second image to obtain a phase interferogram frequency spectrum; cutting the phase interferogram frequency spectrum to obtain a first wavelength phase interferogram and a second wavelength phase interferogram; generating a differential synthesis wavelength interferogram and an additive synthesis wavelength interferogram according to the first wavelength phase interferogram and the second wavelength phase interferogram; and unwrapping the additive synthetic wavelength interferogram by using the differential synthetic wavelength interferogram, and detecting the target to be detected based on the unwrapped additive synthetic wavelength interferogram. In this way, the target and clutter in the low-altitude environment are effectively recognized, and the detection precision is improved.

The invention relates to the technical field of radar information, in particular to a ground clutter suppression method for a ground reconnaissance radar, which comprises the following steps of: firstly, establishing a clutter profile map by using echo data, judging information inside and outside a clutter according to the clutter profile map, and selecting different signal processing flows, namely, adopting a detection channel 1 outside the clutter, adopting a detection channel 2 and a detection channel 3 inside the clutter and adopting a detection channel 2 and a detection channel 3 outside the clutter; after the detection channel 1 passes through the MTD filter, clutter suppression processing is not carried out, and a detection result is output; the detection channel 2 is a coherent processing channel, firstly performs mean filtering processing, then performs coherent processing, performs clutter suppression processing through phasor mean filtering, and outputs a detection result; and the detection channel 3 is a zero Doppler processing channel and is used for detecting a low-speed target in a clutter area, compared with the prior art, the method can reduce Doppler sidelobes as much as possible on the premise of ensuring that the ground clutter height is suppressed, and has the minimum main lobe width and the minimum signal-to-noise ratio loss.

The invention discloses a high-resolution range profile feature retention enhancement method based on iterative weighted sparse decomposition, and the method comprises the steps: obtaining radar echo data collected by a radar receiver within a coherent processing time, and constructing a two-dimensional radar echo matrix composed of a range unit number and a pulse sequence number after range direction matching filtering; representing a two-dimensional radar echo matrix as a superposition relation of adding a low-rank clutter component matrix and a sparse target component matrix; based on the superposition relation, an optimization model is constructed, and the objective function of the optimization model is the sum of the weighted nuclear norm of the low-rank clutter component matrix and the L1 norm of the sparse target component matrix. According to the method provided by the invention, the adaptive weight is applied to the singular value, so that clutters are effectively suppressed under a strong clutter background, and key structure features such as the number, position distribution and relative amplitude relationship of scattering points in the target high-resolution range profile are kept.

The invention provides a space-time dimension reduction adaptive clutter suppression method and system, and the method comprises the steps: inputting original space-time two-dimensional echo data, executing space-time two-dimensional smoothing processing, and obtaining multiple groups of subspace-subpulse two-dimensional data; vectorizing each group of subspace-subpulse two-dimensional data to obtain vectorized subspace-subpulse data; estimating a clutter and noise covariance matrix based on the vectorized subspace-subpulse data, and calculating to obtain an inverse matrix; calculating a clutter suppression weight vector of each group of clutter plus noise covariance matrix based on the inverse matrix, and performing clutter suppression on the group of data through the clutter suppression weight vector to obtain a clutter suppression result of the group; performing coherent accumulation on clutter suppression results of all groups to obtain a final clutter suppression result; and searching a plurality of Doppler channels to complete clutter suppression of a full Doppler domain. According to the method, distance training samples are effectively expanded through a space-time two-dimensional smoothing technology, aperture loss caused by a sliding window is overcome, and clutter suppression performance is improved.

The invention discloses a radar system clutter background modeling and adaptive suppression method and system, and relates to the technical field of radar signal target detection. The radar system clutter background modeling and adaptive suppression method comprises the following steps of covariance matrix construction, clutter model construction, clutter adaptive suppression and adaptive suppression processing optimization. According to the method, covariance matrix stability evaluation is carried out through the constructed covariance matrix, whether covariance matrix correction is carried out is judged, then the initial clutter model is established according to the covariance matrix and the preset probability distribution model, then clutter model adaptability evaluation is carried out, whether clutter model optimization is carried out is judged, and clutter model optimization is carried out. According to the method, the radar signal is subjected to self-adaptive suppression processing, and finally the judgment result of self-adaptive suppression processing optimization is obtained based on the signal-to-noise ratio of the radar signal after self-adaptive suppression processing, so that the clutter suppression effect in the radar signal is improved, and the problem of poor clutter suppression effect in the radar signal in the prior art is solved.

The invention provides an infrared small target segmentation method based on target center region feature enhancement, and the method comprises the steps: inputting a to-be-detected image into a trained infrared small target segmentation model, and obtaining a target segmentation image; according to the method, the target center position attenuation matrix is provided, and the ubiquitous problem that in the existing infrared small target segmentation technology, due to the fact that target signals are weak and the features of the target signals are not prominent, the target signals are extremely prone to being submerged by complex backgrounds and strong clutters is solved in a targeted mode. The attenuation matrix can adaptively perform weighted amplification on the identified feature signal of the target center area, and effectively suppress interference information of a wide background area around the target center area. According to the mechanism, the signal-to-noise ratio and the contrast ratio of the infrared weak small target are directly and greatly improved, so that a real target can be clearly highlighted even under extremely complex background interference. Finally, high-precision segmentation of the infrared small target is realized, the omission ratio and the false alarm rate are remarkably reduced, and excellent performance is shown.

The invention relates to the technical field of ground penetrating radar data processing, in particular to a hidden disease-oriented ground penetrating radar space-time registration and detection method. Comprising the following steps: S1, establishing a continuous mapping relation between a radar signal and a geographic space; s2, constructing a local trajectory indexing mechanism based on anisotropy measurement; s3, constructing a space-time joint correction space based on error prior distribution; s4, performing parameter inversion based on maximum posterior probability estimation constraint; s5, establishing a registration quality evaluation and signal feature decoupling mechanism; and S6, constructing a dielectric priori driven dual physical attribute index and adaptive discrimination model. According to the method, the influence of GPS positioning errors and track swing is overcome, centimeter-level physical space alignment is realized, and false alarms caused by registration deviation are remarkably reduced; by introducing the physical constraint of the frequency dimension, the signal-to-clutter ratio under the complex city background is improved, and the false alarm rate is remarkably reduced.

The invention relates to the field of radar detection, in particular to an unmanned aerial vehicle radar cross-sectional area full-angle measurement method, which comprises the following steps of: measuring the radar cross-sectional area of a to-be-measured unmanned aerial vehicle by using an unmanned aerial vehicle with radar equipment as a measuring device; the measuring device and the to-be-measured unmanned aerial vehicle are lifted to a preset height, and the measuring device points the antenna beam direction of the radar equipment to the to-be-measured unmanned aerial vehicle; recording radar output signal power under each group of attitude angles when the to-be-measured unmanned aerial vehicle flies to a specified position with a radar distance R from the measuring device; acquiring position information and attitude angle information of the measuring device and the to-be-measured unmanned aerial vehicle; and aligning the output signal power measured by the radar with the attitude angle of the to-be-measured unmanned aerial vehicle in time, and calculating the radar scattering cross-sectional area at each attitude angle to realize the full-angle measurement of the radar scattering cross-sectional area of the unmanned aerial vehicle. According to the method, the radar cross-sectional area of the unmanned aerial vehicle can be measured at a low altitude, interference of ground clutters on the radar is avoided, and the method is not limited by a measurement space.

The invention discloses a rainfall monitoring method and device based on millimeter wave radar, and relates to the technical field of meteorological monitoring. The method comprises the following steps: controlling a 24 / 60GHz commercial millimeter wave radar to transmit a millimeter wave signal of an adaptive parameter to a preset area; a raindrop echo signal is received, and clutters are removed through Kalman filtering and wavelet noise reduction; extracting a one-dimensional energy feature, a two-dimensional speed feature and a speed-energy two-dimensional distribution matrix; inputting a low-error model trained by a random forest algorithm, calculating a rainfall energy value and mapping the rainfall energy value into a rainfall level; data is output through a LoRa / ZIGBEE network; the device comprises a millimeter wave radar module, a signal processing module, a calculation output module and a wireless communication module. The problems of single monitoring dimension, high cost and difficult deployment in the prior art are solved, non-contact, low-cost, high-real-time and distributed rainfall monitoring is realized, the error is less than or equal to 5%, the response period is less than or equal to 100ms, and the method is suitable for traffic, municipal administration, emergency and other scenes.

The invention discloses a rolling stone multi-target radar monitoring and early warning method adaptive to a complex terrain, and relates to the technical field of data processing, and the method comprises the following steps: obtaining an echo signal through a radar, obtaining a moving target according to the echo signal, obtaining basic data according to the moving target, obtaining a theoretical path according to the basic data, and carrying out the calculation of the theoretical path. And acquiring monitoring data according to weather forecast of a weather bureau, establishing a corresponding relation according to the monitoring data and the theoretical path and the duration, judging whether to give out early warning or not according to the corresponding relation, obtaining final evaluation, and updating the corresponding relation according to the final evaluation. According to the method, a fixed clutter filtering technology based on Doppler frequency difference analysis is adopted, zero-frequency channel signals are abandoned in combination with fast Fourier transform, a prediction-matching mechanism is combined, multiple rolling stones are stably tracked, the quality of the rolling stones is inversed through radar echoes, kinetic energy is calculated in combination with a velocity vector, and the accuracy of the rolling stones is improved. And quantitative evaluation of the collapsing force of the rolling stone is realized.

The invention relates to the technical field of defect detection, in particular to a defect detection method and system based on a microwave frequency crystal resonator, and the method comprises the steps: forming a microwave probe through a microwave frequency crystal resonator array, placing a to-be-detected material below the microwave probe, and setting a scanning area and scanning step lengths in two directions on the surface of the material; controlling the probe to perform point-by-point scanning according to step length, and collecting a plurality of reflection coefficients of each sampling point to form an echo signal matrix; performing singular value decomposition on the matrix, applying an adjustable coefficient to a first singular value and a second singular value, and adaptively selecting an optimal coefficient according to imaging quality to reconstruct an echo signal; performing two-dimensional Fourier transform on the reconstructed matrix, constructing a spatial filter in combination with probe spacing and defect depth to perform phase compensation, performing two-dimensional Fourier transform to obtain a defect image, and performing threshold segmentation on the defect image to obtain a binary image only containing defects; and analyzing the connected regions to obtain the gravity center position of each defect and the defect area determined by the pixel number and the single pixel area. The problems of serious clutter interference, insufficient defect imaging contrast ratio, difficulty in quantitative evaluation of defect size and the like in the existing microwave detection can be solved.

The invention discloses a frequency domain adaptive clutter suppression method and system for a dual-polarization weather radar, and relates to the technical field of weather radar processing. According to the invention, pulse-level quality control and ground feature identification marking are carried out on radar original echoes; calculating power spectrums of the marking units, performing dynamic noise measurement and data quality grading, and screening out effective processing units; constructing a Gaussian mixture model of a power spectrum of an effective unit, and iteratively estimating model parameters through an expectation maximization algorithm to realize complete separation of a ground feature spectrum and a meteorological spectrum in a frequency domain; inverting single-channel parameters based on the separated meteorological spectrum; a cooperative spectrum separation strategy is adopted for horizontal and vertical polarization channels, and dual-polarization parameters such as differential reflectivity, correlation coefficients and differential phases are inverted based on the filtered dual-channel signals; according to the invention, high-precision separation of clutters and meteorological echoes is realized, the loss of meteorological signals is significantly reduced while ground features are effectively suppressed, and the accuracy and reliability of dual-polarization parameter inversion are improved.

The invention relates to the technical field of radar sea surface target detection, in particular to a sea surface small target detection method and system based on neighborhood clutter attention enhancement. The method comprises the following steps: acquiring radar echo data to be detected; performing adaptive whitening preprocessing on the radar echo data to obtain processed data; based on the processed data, performing short-time Fourier transform on the time domain data of the to-be-measured distance unit and the reference distance unit to obtain a time-frequency graph; performing conjugate multiplication operation on the time-frequency graph of the distance unit to be measured and the time-frequency graph of each reference distance unit to obtain conjugate enhanced feature graphs, and combining the conjugate enhanced feature graphs to form a multi-channel time-frequency feature tensor; and inputting the multi-channel time-frequency feature tensor into the constructed ACGAQ Net network model to complete detection. According to the method, the difference characteristics of the target and the clutter in spatial correlation are fully utilized, and reference unit information is supplemented for sea surface target detection.

The invention provides an SAR target screening method based on binary super-dimensional calculation, and relates to the technical field of real-time target detection. The method comprises the following steps: directly carrying out real and virtual part solution and sign function quantization on I / Q dual-channel complex data of an original echo of a synthetic aperture radar (SAR) to obtain a binary fusion matrix; constructing an abnormal profile based on a preset background statistical model, and adaptively dividing the quantized data into multiple segments according to the abnormal profile; performing super-dimensional mapping on each segment through an independent random binary projection matrix, generating segmented super-vectors, and splicing the segmented super-vectors into a complete sample super-vector; and finally, target discrimination is realized by calculating the Hamming distance between the target and a pre-stored background / target prototype super vector. The quantization logic can be dynamically switched according to the target size, and the problem that a single quantization strategy is poor in adaptability is effectively solved. And through an adaptive segmentation mechanism of energy perception, the weak target characterization capability is effectively enhanced, and the screening accuracy under a strong clutter background is improved.

The invention discloses a small sample non-uniform multi-mode clutter modeling and partition suppression method, which is applied to the technical field of radars, performs multi-mode clutter modeling aiming at a complex environment, partitions the non-uniform clutter environment based on the difference of non-uniform clutter covariance matrix geometric matching, and improves the robustness of the multi-mode clutter modeling. Then, the problem that the performance of a traditional clutter suppression method is reduced under the small sample condition is solved through a dimensionality reduction self-adaptive filtering method in different areas; according to the method, firstly, multi-mode clutter echoes are generated for a complex environment, different-mode clutter region boundaries are judged through the difference of different clutter covariance matrix geometric matching, clutters are partitioned, and then the degree of freedom is reduced through data dimension reduction in different regions, and filter weight vectors are designed to achieve self-adaptive filtering; according to the method, effective partitioning and suppression of multimode clutters can be realized under the conditions that the number of samples is less than the degree of freedom of a radar system and the clutter environment is not uniform.

The invention relates to the technical field of target detection and tracking, in particular to a low, small and slow target detection and tracking method and system based on multi-modal fusion. The method comprises the following steps: preprocessing a visible light image, an infrared image and radar data; visible light and infrared images are detected based on a double-flow structure network; the detection result is input into a cross-modal Transform fusion module for feature fusion, and a multi-modal target detection result is obtained; a fusion detection result and radar data are input into a radar-vision fusion processing module, and radar clutter suppression, space-time alignment and information fusion are completed; maintaining a tracking ID and a historical track of the target through target tracking and track prediction; and a result is pushed to an external system in real time through asynchronous task processing and MQTT real-time communication. Visible light and infrared information are efficiently fused, the complex environment detection stability is improved, the real-time requirement is met, and the low, small and slow target detection precision and tracking stability are improved.

The invention provides a millimeter-wave radar personnel perception method based on a time-frequency domain and a deep CNN, and relates to the technical field of radar signal processing, and the method comprises the steps: carrying out the preprocessing, spectrogram conversion and enhancement of a millimeter-wave radar echo signal; spatial features are extracted by using depth separable convolution of a residual structure, time-frequency features are extracted by combining dual-tree complex wavelet transform and attention-enhanced cavity convolution, motion features are extracted through optical flow estimation and three-dimensional convolution, and the three features are adaptively fused; the method comprises the following steps: constructing a dynamic spatio-temporal reasoning network, obtaining key spatio-temporal features by using a recurrent neural tensor network, a non-local neural network and deformable convolution, extracting multi-scale features and performing adversarial feature alignment, and completing personnel target classification through a dynamic routing mechanism based on a capsule network. According to the method, clutter interference can be effectively suppressed, space, time frequency and motion information is fully utilized, accurate perception of personnel targets is realized, and the classification accuracy is improved.

The invention discloses a weather radar metal ball calibration precision improving method, and belongs to the technical field of metal ball calibration, and the method comprises the following steps: carrying out the quantification of a background clutter error of a metal ball calibration position, and removing the background clutter error in the calibration process: constructing a metal ball stability model, and carrying out the calibration of the metal ball stability model; dynamic errors caused by swinging of the metal ball body are quantified and compensated; after the background clutter error is removed and the stability of the metal ball meets a certain condition, the deviation error of the metal ball from the beam center is quantized and compensated; and meanwhile, the cross-library phenomenon of the metal balls is identified, adjusted and compensated. According to the method, the calibration precision of the metal ball of the weather radar is improved.

A system and method for distinguishing targets of interest from main lobe clutter and sidelobe clutter includes an ESA or AESA divided into subarrays. A sum beam power profile is produced via one or more scans of the subarrays, and a difference beam power profile is produced via calculations involving the subarrays. A comparison of the sum beam power profile and the difference beam power profile accentuates signal power disparities between targets of interest and clutter. A threshold is then applied to isolate targets of interest. Different difference beam power profiles may be used for elevation comparisons and azimuth comparisons. Alternatively, subarrays may be selected such that the same difference beam power profile may be used for both elevation and azimuth. Scans may be time multiplexed such that data from different scans may be used to produce the sum beam power profile, the difference beam power profile, or both.