1013 results about "Clutter" patented technology

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 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 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 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 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.

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 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 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 discloses a radar target detection method and system based on deep learning and a storage medium, and relates to the technical field of target detection, and the method comprises the steps: carrying out the standardization processing of a millimeter wave radar, obtaining the standardized data, inputting the standardized data into an LSTM-KF model, and carrying out the filtering, combining data obtained by filtering with echo data to realize target detection; in order to solve the problems that a small target echo signal is weak, is susceptible to clutter interference, is complex in motion mode and the like, a weak signal change mode and a nonlinear motion characteristic of continuous multi-frame echoes are captured through LSTM, and dynamic trajectory constraint of KF is combined, so that time sequence enhancement and false alarm suppression of the weak signal are realized; the problems that traditional linear filtering is high in small target omission ratio, false alarm is difficult to control, nonlinear motion tracking lags and the like are effectively solved.

An aerial monopulse active electronically scanned array (AESA) radar system includes a phased array of independently controllable radio frequency (RF) channels, a beamforming module, and a transmit / receive module. The beamforming module is configured to cause the phased array to produce a radiation pattern with intercardinal sidelobes oriented along a shortest axis to ground, during flight. The transmit / receive module is configured to half-duplex operation of the phased array by switching between left-hand circular polarization and right-hand circular polarization.

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 weather radar data quality control method based on a dynamic clutter map and texture features, and relates to the technical field of weather radars, and the method comprises the following steps: inputting radar body scanning data; clutter frequency statistics is carried out; generating a mask matrix based on the clutter map and a reflection threshold, preliminarily determining clutter positions based on the mask matrix, and preliminarily filtering clutters; defining 9 * 9 window sliding capture scanning elevation angle data, extracting speed features and spectral width texture features of each layer of scanning elevation angle clutter region, and calculating a speed standard deviation and a spectral width texture standard deviation; performing secondary judgment on the preliminarily filtered clutters by combining the speed standard deviation and the spectral width texture standard deviation, filtering echoes which are secondarily judged to be clutters, and retaining other echoes which are not secondarily judged to be clutters; filling the holes; and outputting the processed radar data. According to the invention, the accuracy and reliability of weather radar data are improved, so that higher-quality data support is provided for meteorological monitoring and forecasting.

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 provides a distributed MIMO clutter echo simulation method based on GIS information, and the method comprises the steps: obtaining the GIS information of a geographic information system, and dividing a surface scatterer irradiated by an MIMO radar into a plurality of basic scattering units based on the GIS information; calculating slope distances, Doppler frequencies, terrain elevation values and land cover types of the plurality of basic scattering units to form a scene model element set; mapping a plurality of basic scattering units to a radar resolution unit by combining a backscattering coefficient empirical model and a resolution adaptive grid mapping method according to the scene model element set; dividing the radar resolution unit into a permanent scattering area and a dynamic scattering area according to GIS information; performing clutter statistical characteristic simulation processing on the permanent scattering region and the dynamic scattering region to obtain a clutter amplitude sequence; and performing echo analysis processing on the clutter amplitude sequence in a time-frequency domain to obtain clutter echo simulation data. And the simulation precision of clutter echo simulation data is integrally improved.

The invention discloses an unmanned aerial vehicle target detection and tracking method based on thunder-vision fusion, and the method comprises the following steps: obtaining camera measurement data obtained through a camera and radar measurement data obtained through a radar, and carrying out the alignment of the camera measurement data and the radar measurement data; a camera is used as an auxiliary sensor to screen radar detection; and fusing the radar measurement data and the camera measurement data, outputting the fused data to the tracker, and generating and updating the track of the target identified in the field of view of the sensor. According to the invention, through fusion of the double-level radar sensor and the visual sensor, the radar and visual data are preliminarily integrated and processed before the target is tracked to reduce noise and clutter, so that the calculation efficiency can be improved, the situation that the radar is submerged by environmental noise due to weak sensor signals during low-altitude flight can be prevented, and the reliability of flight is improved. Therefore, the detection accuracy is improved.

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 discloses a radar target template signal establishment method based on depth model adaptive segmentation, mainly relates to the technical field of template signals, and is used for solving the problems that a target contour is easy to fracture or false detection by clutters is easy to cause when a time-frequency map is subjected to binary segmentation by a simple threshold slice in the prior art. And the threshold parameter is very sensitive to the signal-to-clutter ratio and the environmental change. Comprising the following steps: reading a one-dimensional radar echo sequence, and mapping the one-dimensional radar echo sequence to a two-dimensional time-frequency domain to obtain a time-frequency image; multi-scale features are obtained, and a matrix is prompted; obtaining a binary mask corresponding to the mask feature through the multi-scale feature and the prompt matrix; determining the binary mask corresponding to the highest confidence score as a final mask; screening time-frequency transformation data corresponding to the radar echo sequence by using the final mask to obtain output data; and recovering the output data into a time domain signal by using inverse short-time Fourier transform, and taking the time domain signal as a target template signal.

Apparatus and associated methods relate to a target measurement system (TMS) configured for measuring moving targets. In an illustrative example, the TMS may add back clutter signals to clutter removed data if, after a first FFT is generated, a peak is identified within first few frequency bins to measure slowly moving targets. For example, the TMS may compute a cluster area based on statistical boundaries and statistical centers for multiple clusters associated with target objects, and combine one or more of the plurality of first clusters with overlapping cluster areas. For example, the TMS may generate N spectral energy heatmaps using N independent detection algorithms. For example, values of each spectral energy heatmap may be generated based on raw sensor data independent of values of other spectral energy heatmaps. For example, the TMS may validate a target detection when the target is identified in at least two of N spectral energy heatmaps. Various embodiments may advantageously detect target objects at a high degree of precision.

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 field of low-altitude monitoring and target identification, and discloses a dynamic target detection method based on a three-dimensional low-altitude monitoring radar. The method comprises the following steps: performing three-dimensional scanning to obtain low-altitude echoes, and resolving three-dimensional coordinates of a target based on Doppler frequency shift and array angle measurement; clutters are suppressed by adopting space-time adaptive processing and constant false alarm rate detection; separating real echo and mirror image interference by using a sparse reconstruction method; fine classification of dynamic targets such as an unmanned aerial vehicle is realized through micro-Doppler features; judging abnormity in combination with trajectory entropy and divergence, and correcting refraction errors by using atmospheric stratification imaging; and further identifying a group cooperation behavior based on graph clustering and a speed consistency index, and outputting multi-level layered early warning information. Compared with the prior art, a closed-loop link from single-target detection to group situation analysis is realized, high reliability, anti-interference performance and pre-warning capability are realized, and the detection precision and situation awareness level in a complex low-altitude environment can be remarkably improved.

This paper proposes a moving target detection method using optimal fusion of multi-channel ATI-SAR in a strong clutter background. This method first performs local detection using a set of multi-baseline clutter-free ATI phase tests and AMF amplitude tests. It then designs and optimizes global detection based on the optimal fusion rule. The present invention achieves maximum target detection performance with a constant false alarm probability. To facilitate implementation, the present invention develops a cognitive detection framework that does not require prior target knowledge. Because the present invention fully utilizes spatial degrees of freedom and information from environmental feedback, it effectively improves the detection performance of small, weak targets with low signal-to-noise ratios and low radial velocities in a strong clutter background.