1221 results about "Clutter" patented technology

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 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 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.

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 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.

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.

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 the technical field of signal processing, and provides an airborne radar multipath clutter suppression method and device. The method comprises the following steps: calculating test statistics of each training sample in a sample set, and selecting a training sample corresponding to a median of the test statistics as a reference sample; calculating the mahalanobis distance between the reference sample and each training sample, carrying out the non-uniform measurement of each training sample through employing the mahalanobis distance, obtaining a uniform training sample through screening, and determining a to-be-detected unit sample where the multipath clutter is located; constructing an air-space domain clutter covariance matrix of the to-be-detected unit sample according to the to-be-detected unit sample, calculating the rank of the air-space domain clutter covariance matrix so as to construct a multi-stage Wiener filter and a blocking matrix, and constructing an air-space domain adaptive weight vector according to the multi-stage Wiener filter and the blocking matrix; and performing space-time joint processing according to the space-space domain adaptive weight vector to obtain an output signal. According to the invention, the target detection performance of the airborne early warning radar in the multipath clutter environment of the complex mountainous area is improved.

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 linear frequency modulation continuous wave (LFMCW) radar signal processing, and discloses a multi-dimensional domain interference suppression method based on LFMCW radar echo characteristics, and the method mainly comprises the steps: firstly, carrying out the weighted beam forming of an array receiving signal in a space domain, so as to reduce the interference influence in an unexpected direction; performing short-range gain control filtering and time domain compression in a fast time domain; moving target display, moving target detection and adaptive motion clutter elimination are carried out in the Doppler frequency domain; and then carrying out improved variable index constant false alarm detection in an energy domain, reducing the influence of energy fluctuation interference, and finally outputting a multi-dimensional domain interference suppression result. According to the method, the interference is effectively suppressed in the airspace, the fast time domain, the Doppler frequency domain and the energy domain through the characteristics of the LFMCW radar echo signal, on one hand, the problem of overload of a radar receiver caused by strong interference can be avoided, and on the other hand, the target detection capability and the false alarm resistance of the radar in a complex environment are improved.

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.

The present invention discloses a radar CFAR target detection method based on the prefix sum algorithm. The method performs Fourier transform and square-law detection on the radar echo signal to obtain the range-Doppler matrix A, and performs constant false alarm rate (CFAR) processing on the range-Doppler matrix A to determine whether a target exists. When performing CFAR detection, a rectangular reference sliding window is used. First, the prefix sum algorithm is used to obtain the prefix sum matrix SUM of the range-Doppler matrix A, and then the average value of the data of the surrounding reference cells of the cell a to be measured is calculated through the prefix sum matrix. The average value is multiplied by a coefficient to obtain an estimated value of the background clutter, and the estimated value is compared with the cell to be detected to determine whether the cell to be detected is a target. The method of the present invention ensures the accuracy of detection while reducing the complexity of the algorithm, reducing the computational amount of the original CFAR detection, and improving the detection efficiency. i,j The method of the present invention ensures the accuracy of detection while reducing the complexity of the algorithm, reducing the computational amount of the original CFAR detection, and improving the detection efficiency.