57 results about "Hat matrix" patented technology

The invention discloses a non-stationary industrial process early fault diagnosis method based on a common trend model, and belongs to the field of industrial process monitoring and fault diagnosis. The method comprises the following steps: collecting training data of a non-stationary industrial process under a normal working condition, performing denoising by using singular spectrum analysis, and obtaining a stationary projection matrix and a stationary data matrix by using a common trend model; orthogonal conversion is carried out on the stationary data matrix, a conversion projection matrix is calculated, and an orthogonal conversion data matrix and a statistic data matrix thereof are obtained; calculating a mean value and a covariance matrix of the statistic data matrix to obtain a control limit; the method comprises the following steps: acquiring test data under a real-time working condition of an industrial process, processing to obtain statistic data, calculating a mahalanobis distance index, and comparing with a control limit to judge whether a fault occurs; and if a fault is detected, realizing fault separation according to fault reconstruction and a mapping relation. According to the method, the interference of the non-stationary characteristic on the fault diagnosis model can be reduced, and fault detection and separation in the non-stationary process are realized.

Auto-tuning covariances associated with a set of noise models for a variety of sensor modalities and / or perception components such that the covariances are leveled respective to one another may include whitening the covariances and / or error models and determining scalars to apply to the covariances. Determining these scalars may comprise using the residuals that result from generating the set of noise model (e.g., such as may be determined as part of least squares estimation) along with the hat matrix of the process model to determine the scalars. The covariances may iteratively be updated until the scalar adjustments converge or until another end condition is met.

The invention discloses a B5G base station T / R assembly health measurement method and system based on sparse projection and a hidden Markov model, and the method comprises the steps: collecting the multi-source operation data of a T / R assembly, and constructing a multi-dimensional operation parameter time sequence; a sparse projection matrix is constructed through covariance analysis and eigenvalue decomposition, and high-dimensional operation parameters are mapped into low-dimensional sparse health eigenvectors; training a hidden Markov model based on the feature sequence in the normal state, fitting an observation probability by using a Gaussian mixture model, and establishing a normal state reference model; calculating a KL distance between the current feature sequence distribution and the reference distribution, and mapping the KL distance into a normalized health degree index; and adaptively determining a state division threshold value by using a K-means clustering algorithm to realize health state grading of the T / R assembly. According to the method, environmental noise is effectively stripped through sparse projection, the dynamic reference model is utilized to adapt to complex working conditions, and online sensing and accurate evaluation of early weak degradation of the B5G base station assembly are realized.

The invention provides a non-stationary industrial process monitoring method and system. The method comprises an offline training stage: calculating a time Laplacian matrix and a space Laplacian matrix based on a historical data matrix; constructing an objective function of the stationary subspace analysis method, and adding a time constraint term of a time Laplacian matrix and a space constraint term of a space Laplacian matrix into the objective function; solving the objective function to obtain a stable projection matrix; calculating a stationary component and a monitoring index of each sample in the data matrix X in sequence; determining a control limit by using a kernel density estimation method; an online monitoring stage: based on the real-time operation data x, calculating a stationary component of the real-time operation data x and a corresponding real-time monitoring index according to the stationary projection matrix, and if the real-time monitoring index is greater than a control limit, judging that the operation of the non-stationary process has a fault; the monitoring accuracy can be improved.

The invention discloses an integrated navigation optimization method and a computer system, and the method comprises the steps: GNSS telegraph text extraction: obtaining an MSM observation telegraph text and a BRDC ephemeris telegraph text; gNSS telegraph text decoding is carried out to obtain a multi-system GNSS observation value and a multi-system satellite broadcast ephemeris parameter; constructing a GNSS pseudo-range observation equation: calculating a satellite position, and correcting a system error to obtain a linearized error equation; estimating a position parameter and a variance-covariance matrix thereof, and extracting satellite ranging precision from the broadcast ephemeris; the standard deviation of the position parameters is extracted, a distance measurement precision projection matrix is calculated, and the GNSS protection level is calculated; and setting an alarm threshold along the course, and when the protection level is smaller than the alarm threshold, incorporating a GNSS resolving result into the integrated navigation system to correct the accumulated drift of the INS system. The method has the advantages that an overall better integrated navigation output result is obtained, and the overall precision of the integrated navigation system in a complex environment is optimized.

The invention relates to the technical field of edge-end collaboration, and discloses an edge-end collaboration AI model construction system and method for energy consumption prediction, and the method comprises the steps: collecting node parameters of edge nodes according to a fixed time interval in a preset time period, and obtaining a parameter matrix; calculating a sample covariance based on the parameter matrix, and executing strip projection to obtain a projection matrix; respectively constructing a variance matrix and a correlation matrix by using the projection matrix; constructing a power grid sparse matrix according to the sensor topology; splicing the power grid sparse matrix and the correlation matrix to form a normalized correlation matrix, and generating a multi-layer covariance matrix in combination with the variance matrix; performing positive definite processing on the multi-layer covariance matrix to obtain a positive definite covariance matrix; and whitening the parameter matrix by using the positive definite covariance matrix to obtain a whitened parameter matrix.

A plant three-dimensional reconstruction method for a weak texture image and scale distortion comprises the following steps: step 1, recovering an initial Gaussian point cloud and camera parameters through an SfM method by using a multi-view RGB image; 2, differential rendering is executed in training iteration, a predicted image is compared with a real image, and a loss function containing various constraints is calculated to serve as a basis for gradient solving and parameter updating; 3, calculating a pixel weighted average gradient and an artifact suppression coefficient of each Gaussian in a visible view angle; 4, updating Gaussian parameters through back propagation, and adaptively triggering Gaussian cloning, splitting or deleting operation based on the pixel gradient, the covariance matrix and transparency; and 5, dynamically adjusting the Gaussian projection matrix according to the focal length and the resolution of the camera during rendering so as to maintain scale consistency, and performing anti-aliasing rendering in combination with pixel-level super-sampling. According to the method, the reconstruction precision and the structure reduction capability of the virtual plant in the weak texture region are remarkably improved.

The application discloses a near-field strong interference suppression method based on spherical wave deconvolution beam forming positioning, which comprises the following steps: performing fine grid division on a near-field scanning area of a linear array; performing spherical wave focusing beam forming on the near-field scanning area and two-dimensional deconvolution beam forming about distance-angle under the condition of unknown sound source position; calculating the distribution entropy characteristics of the deconvolution beam forming result in the angle dimension, and using a preset threshold to correlate the interference positioning of a target; constructing an array flow pattern matrix A of each near-field target l and generating a covariance matrix A; calculating the orthogonal complement space projection matrix of the covariance matrix A, and applying the orthogonal complement space projection matrix to the array element domain covariance matrix C to perform near-field interference suppression; using a far-field plane wave model to perform deconvolution beam forming calculation on the covariance matrix for suppressing near-field interference, so as to obtain passive detection results of the near-field strong interference suppression. The application can realize near-field interference positioning and interference suppression under the condition that the interference position is unknown.

This invention proposes an online incremental high-influence sample detection method for smart grids, relating to the field of online incremental learning technology in machine learning. The method constructs a local hat matrix for samples arriving at the edge computing device's buffer, then calculates the influence of the samples in the buffer based on this matrix and sorts them by size. The top-ranked samples are stored in a fixed-size buffer. When a new sample arrives at the device, it is merged with the previously selected high-influence samples, and the high-influence samples are re-detected, updating the rearranged buffer. This process is repeated until the online learning ends. At this point, the high-influence samples recorded in the rearranged buffer represent the subset of samples with the highest influence among all training samples up to the current stage. This invention solves the problem of constructing a hat matrix based on all samples in edge computing environments with limited storage space, reducing the time and space overhead of the detection algorithm.

Provided are a URA calculation method and device for a low-orbit navigation satellite, a computer storage medium and a computer program product. The URA calculation method comprises: in precise orbit determination and clock error calculation for a low-orbit satellite, a covariance submatrix of a predicted orbit and clock error parameter in a predicted arc segment is given by using a transfer matrix, denoted as a first covariance submatrix; a projection matrix from a geocentric geodetic coordinate system to a satellite orbit coordinate system is calculated; the first covariance submatrix is converted into a second covariance submatrix of the satellite orbit coordinate system according to the projection matrix; the satellite orbit prediction accuracy and the clock error prediction accuracy are calculated according to the second covariance submatrix; and the URA is calculated according to the satellite orbit prediction accuracy and the clock error prediction accuracy. The present disclosure proposes a URA calculation method for a low-orbit satellite based on a covariance matrix, which guarantees the real-time performance and reliability of integrity monitoring.

The invention discloses a low earth orbit satellite clock error integrity monitoring method based on a batch least square method, which solves the problem of lack of integrity monitoring for low earth orbit satellite clock error calculated by an efficient batch least square method in the prior art, and comprises the following steps: determining an observation noise upper bound and an SISRE error decomposition upper bound; constructing an observation upper bound covariance matrix of each epoch according to the data and GNSS observation data; combining the data and the constraint equation to obtain a complete upper bound covariance matrix; calculating according to the complete upper bound covariance matrix and the first design matrix to obtain an upper bound covariance matrix and an upper bound standard deviation of all unknown numbers; according to the projection matrix from the observation domain to the result domain and the SISRE upper bound system deviation, the maximum deviation influence and the upper bound deviation of all unknown numbers are obtained through calculation; according to the upper bound standard deviation and the upper bound deviation, the protection level of the low earth orbit satellite clock error is calculated; and comparing the protection level with the low earth orbit satellite clock error alarm threshold to complete low earth orbit satellite clock error integrity monitoring.

The application discloses a medical image class incremental learning method and system based on feature principal direction guided projection, and belongs to the technical field of medical image analysis; the method acquires medical image features through a pre-trained feature extractor; random projection is performed on the features to obtain first-view features; a covariance matrix is calculated based on a feature cache area and is decomposed to extract the first K principal directions, construct a guided projection matrix, and obtain second-view features; the double-view features are spliced to obtain final representation; the classifier weight is directly calculated through a ridge regression closed-form solution without backward propagation training; the application adaptively captures the core structure of the feature space through data-driven principal direction projection, retains the detailed information in combination with random projection, effectively improves the feature separability of fine-grained classes, adopts an analytical classifier updating mechanism, realizes stable class incremental learning without storing historical data, improves the model updating efficiency, and is suitable for efficient continuous learning tasks in various medical image scenes.

The invention discloses a circuit fault diagnosis method based on a fractional order observer, and the method comprises the steps: constructing a fractional order circuit model based on the Kirchhoff's law, and describing a capacitor end voltage and an inductive current as a Caputo fractional order derivative form; a double-disturbance decoupling mechanism is designed, total disturbance is decomposed into a decoupling disturbance component and a non-decoupling disturbance component, physical isolation of the decoupling disturbance is achieved through a projection matrix, and the influence of the non-decoupling disturbance is eliminated through a dynamic suppression coefficient. Constructing a state observer to generate a state estimation error and a fault error vector, and dynamically compensating a fault estimation value in combination with an adaptive gain matrix; and ensuring the system stability through a Lyapunov function, and proving that an error state is converged to a preset boundary domain under the condition of satisfying a characteristic value correlation matrix inequality. Compared with the prior art, rapid and accurate diagnosis of faults such as inductance saturation and capacitance aging can be realized.

The application discloses a network fault positioning fast response system based on distributed log analysis, relates to the technical field of computer network and operation and maintenance, and comprises the following modules: a data acquisition module, which generates time series data frames based on a sliding window; a state vector construction module, which extracts indexes and constructs standardized state vectors; a covariance matrix updating module, which recursively updates a dynamic covariance matrix by using a forgetting factor; a feature space decomposition module, which constructs a residual projection matrix based on dynamic principal component dimension parameters; an anomaly detection module, which calculates residual anomaly energy and determines a fault candidate set based on contribution degrees; and a parameter self-adaptive module, which calculates residual space entropy values and feeds back to adjust principal component dimension parameters of the next frame; and the application realizes adaptive and accurate bounding of gray faults through entropy feedback closed loop and orthogonal projection.

The present invention discloses a multi-mode industrial process monitoring method, apparatus, equipment, and storage medium, including: collecting and preprocessing operating data and performing pattern division through clustering; using affinity matrices between and within patterns to calculate the divergence matrices between and within patterns, respectively; constructing a final objective function; iteratively updating the projection matrix, sharing matrix, and error matrix therein to obtain the final projection matrix; calculating the statistics and control limits of each pattern; calculating the statistics of new samples when they arrive, comparing them with the corresponding control limits, and issuing fault information. High-dimensional data is mapped to a low-dimensional space using the projection matrix, preserving the original features; analyzing the industrial process through the local structure and inherent characteristics of the data within each pattern contained in the features, as well as the relationships between different patterns and common information. The method can fully consider the inherent characteristics of each pattern and the correlations between patterns to monitor the industrial process, thereby improving the accuracy of fault monitoring.

The invention relates to the technical field of radars, in particular to an inverse main lobe interference method and device based on signal subspace steering vector reconstruction, and the method comprises the steps: constructing a first covariance matrix corresponding to an interference sample in a radar array receiving signal, carrying out the eigendecomposition according to the first covariance matrix, and obtaining a first eigenvector; selecting a first feature vector corresponding to the main lobe interference, constructing a feature projection matrix, and eliminating the main lobe interference through the feature projection matrix to obtain an intermediate radar signal; constructing a second covariance matrix of the intermediate radar signal, and performing characteristic decomposition on the second covariance matrix to obtain a second characteristic vector; obtaining a signal subspace according to the second feature vector, reconstructing a steering vector of the signal subspace, and substituting the reconstructed steering vector into a preset algorithm to obtain a target angle spatial spectrum so as to determine a target angle; the method has high target direction estimation precision and has the capability of suppressing multi-lobe interference.

The invention relates to the technical field of natural language processing, in particular to a model prefix parameter and hyper-parameter joint optimization method and system. Comprising the following steps: S1, acquiring a high-dimensional prefix parameter and hyper-parameter joint optimization instruction; s2, generating a random projection matrix, and mapping the high-dimensional prefix parameters into low-dimensional prefix parameters through the projection matrix; s3, searching an optimal low-dimensional prefix parameter in a low-dimensional space through a covariance matrix adaptive evolution strategy, reconstructing the low-dimensional prefix parameter into a dynamic high-dimensional MLP parameter, generating a key-value prefix required by each layer of the model, and embedding and injecting the key-value prefix into the large model; s4, training hyper-parameters are dynamically adjusted based on a particle swarm algorithm and a cosine annealing strategy, and the hyper-parameters are transited step by step in the training process; s5, training the model based on the optimal prefix parameter and the hyper-parameter, and outputting the trained model; and S6, applying the trained model to the medical question and answer task. According to the method, the precision and stability of prefix tuning in the medical question and answer task can be improved.

The invention discloses a noise elimination method and system based on matrix reconstruction, a terminal and a storage medium, and relates to the technical field of underwater acoustic array signal processing. The noise elimination method comprises the following steps: receiving a sound source signal; establishing a noise-containing data model of the sound source signal; obtaining a data snapshot sample of the sound source signal based on the noise-containing data model, and calculating a sample covariance matrix; performing characteristic decomposition on the sample covariance matrix to obtain a signal subspace and a noise subspace; obtaining a distance relationship between two projection matrixes formed by each column vector in the signal subspace and the noise subspace based on a translation invariant subspace theory, and establishing a diagonal matrix based on the distance relationship between the two projection matrixes; and reconstructing a sample covariance matrix by using the diagonal matrix to realize noise elimination. The method has lower sidelobe, better robustness and overall performance, can adaptively process data, does not need to adjust the diagonal load shedding coefficient, and can improve the angular resolution of a target signal to a greater extent.

The invention provides a URA calculation method and device for a low-orbit navigation satellite, a computer storage medium and a computer program product. The URA calculation method comprises the following steps: when precise orbit determination and clock error calculation are carried out on a low-orbit satellite, giving a covariance submatrix of a forecast orbit and a clock error parameter in a forecast arc section by using a transfer matrix, and recording the covariance submatrix as a first covariance submatrix; calculating a projection matrix from the earth-centered earth-fixed coordinate system to the satellite orbit coordinate system; converting the first covariance sub-matrix into a second covariance sub-matrix of a satellite orbit coordinate system according to the projection matrix; and calculating satellite orbit forecasting precision and clock error forecasting precision according to the second covariance submatrix, and calculating URA according to the satellite orbit forecasting precision and the clock error forecasting precision. The invention provides a low earth orbit satellite URA calculation method based on a covariance matrix. The real-time performance and reliability of integrity monitoring are guaranteed.

The application discloses an industrial process anomaly detection method and device and a computer device, relates to the technical field of industrial control, and comprises the following steps: obtaining target working condition data of an industrial process, performing working condition identification on the target working condition data according to a target clustering algorithm, and obtaining a target working condition type corresponding to the target working condition data. According to a target projection matrix of the target working condition type, an inverse matrix of a covariance matrix of a hidden variable under the target working condition type and the target working condition data, T 2 statistics and SPE statistics are calculated. 2 Then, according to the comparison result of the target T 2 statistics and the target T 2 statistics and the comparison result of the SPE statistics and the target SPE statistics, whether an abnormal situation exists at the collection time of the target working condition data and the cause of the abnormal situation are determined. The embodiment of the application can realize accurate detection of the industrial process.

The invention discloses a network fault positioning quick response system based on distributed log analysis, and relates to the technical field of computer networks and operation and maintenance, and the system comprises a data acquisition module which generates a time sequence data frame based on a sliding window; the state vector construction module is used for extracting indexes and constructing standardized state vectors; the covariance matrix updating module is used for recursively updating the dynamic covariance matrix by using the forgetting factor; the feature space decomposition module is used for constructing a residual projection matrix based on the dynamic principal component dimension parameters; the anomaly detection module is used for calculating residual anomaly energy and determining a fault candidate set based on the contribution degree; the parameter self-adaptive module is used for calculating a residual spatial entropy value and feeding back and adjusting a principal component dimension parameter of a next frame; through entropy feedback closed loop and orthogonal projection, adaptive accurate delimitation of the gray scale fault is realized.

The present application is to solve the problems of spectrum leakage, large loss of useful signal and insufficient interference locking precision in the existing narrow-band interference suppression technology; a narrow-band interference suppression method for Beidou navigation is provided, comprising the following steps: S101: discrete signal sequence sampling and segmented caching; S102: constructing an adaptive signal covariance matrix; S103: performing fast eigenvalue decomposition on the covariance matrix; S104: determining the number of interference components based on the eigenvalue distribution; S105: constructing an interference subspace projection matrix; S106: performing orthogonal projection to suppress narrow-band interference; S107: signal gain compensation and output reconstruction; S108: detecting whether the interference environment has changed dramatically; all components pointing to the interference in the signal space are filtered through the orthogonal projection matrix, avoiding the convergence problem of time-domain adaptive filtering, and effectively dealing with the strong narrow-band interference scene with high power density.

The invention belongs to the technical field of image processing, and particularly relates to a hyperspectral image dimension reduction and classification method and system based on discriminant spatial-spectral tensor locality preserving projection and a medium, and the method comprises the steps: inputting hyperspectral image data, and constructing three-dimensional tensor representation; performing blocking processing on the tensor, and extracting local tensor blocks; calculating a weighted covariance matrix based on a nuclear spectrum angle weighting strategy; adopting affine invariant Riemannian measurement to construct a weight matrix of the adjacency graph; solving the multi-modal projection matrix through an alternative optimization algorithm to realize dimension reduction; and finally, inputting the dimensionality-reduced features into a classification model and outputting a classification result. According to the method, by fusing the spatial-spectral tensor representation and the Riemannian geometry, the problems that a traditional method destroys spatial coherence and is sensitive to noise are solved, and the dimension reduction effect and the classification precision are remarkably improved.

The invention belongs to the technical field of multichannel signal detection, and particularly relates to a self-adaptive subspace detection method and system suitable for a partially uniform environment, and the method comprises the following steps: 1, constructing a target signal subspace matrix and an interference signal subspace matrix; step 2, constructing a sampling covariance matrix by using the training sample; 3, constructing a quasi-whitening matrix by using the sampling covariance matrix, and then performing quasi-whitening processing to respectively obtain whitening matrixes; 4, constructing an orthogonal projection matrix of the target signal and a corresponding complementary projection matrix; step 5, constructing an intermediate variable matrix and non-zero characteristic decomposition, and obtaining non-uniform parameters; step 6, constructing detection statistics by using the to-be-detected data matrix, the orthogonal projection matrix of the target signal, the orthogonal projection matrix of the interference signal and the non-uniform parameters; step 7, determining a detection threshold; and 8, judging whether a target exists or not. According to the invention, the detection performance of the airborne radar in a partially uniform environment is improved.

The present invention discloses a robust adaptive beamforming method. First, a linear array is divided into two sub-arrays, and the initial arrival directions of the desired signal and interference and the nominal steering vector are obtained by using the rotational invariant subspace algorithm. A projection matrix 1 is constructed to obtain the optimal steering vector of the desired signal. A projection matrix 2 is constructed to remove the desired signal component and estimate the power of the interference. The noise power is estimated by using the eigenvalue matrix corresponding to the noise subspace. Based on the nominal steering vector of the interference and the estimated power, the interference-plus-noise covariance matrix is reconstructed by using the zero broadening method of virtual interference. Based on the optimal steering vector of the desired signal and the interference-plus-noise covariance matrix, robust beamforming is performed on the array received data. The present invention can obtain a more accurate steering vector of the desired signal, and the reconstructed interference-plus-noise covariance matrix is used to calculate the weight vector, which has a stronger suppression effect on the interference and improves the robustness of the beamformer.

The invention discloses a multi-mode industrial process monitoring method and device, equipment and a storage medium, and the method comprises the steps: collecting operation data, carrying out the preprocessing, carrying out the mode division through clustering, calculating the divergence matrixes between modes and in the modes through employing the affinity matrixes between the modes and in the modes, and constructing a final objective function, iteratively updating the projection matrix, the shared matrix and the error matrix to obtain a final projection matrix, calculating the statistical magnitude and the control limit of each mode, calculating the statistical magnitude of a new sample when the new sample arrives, comparing the statistical magnitude with the corresponding control limit, and sending out fault information. High-dimensional data is mapped to a low-dimensional space through a projection matrix, original features are reserved, and the industrial process is analyzed through local structures and characteristics of data in all modes contained in the features, the relation between different modes and common information; the industrial process can be monitored by fully considering the characteristics of each mode and the association between the modes, and the fault monitoring accuracy is improved.

The application provides a high-dimensional small sample data dimension reduction method for industrial detection classification, acquires high-dimensional small sample data of industrial detection and converts the data into a data matrix; a local neighborhood structure is used to replace global statistics, a local inter-class scatter matrix and a local total scatter matrix are calculated, and based on this, a robust trace ratio discriminant local preserving projection model is constructed, so that the small sample problem is fundamentally avoided; an auxiliary variable is introduced to convert the model into a joint optimization problem about a projection matrix and the auxiliary variable, an iterative solution is obtained by using an alternating optimization strategy, convergence of the algorithm is ensured, and a final projection matrix is obtained; and test data is projected onto the final projection matrix to realize feature dimension reduction. The application improves the precision and robustness of industrial detection classification in a high-dimensional small sample scene.

The invention belongs to the technical field of multi-channel signal detection, and particularly relates to a subspace signal detection method and system suitable for a scene with insufficient training samples, and the method comprises the steps: firstly constructing a target signal matrix, an interference signal matrix and a training sample matrix, and carrying out the skew symmetry processing of the constructed matrixes; constructing a splicing matrix by using the target signal matrix and the interference signal matrix; constructing an orthogonal projection matrix by using the interference signal matrix and the splicing matrix after the skew symmetry processing; using the transformed sampling covariance matrix to construct a quasi-whitening matrix for whitening processing; constructing a detection statistic by using the whitened matrix; and determining a detection threshold by using a false alarm probability preset value, comparing the detection statistic with the detection threshold, and judging whether a target exists or not. The detector designed by the invention integrally realizes interference suppression, signal accumulation and constant false alarm characteristics, and compared with the existing method, the method has the advantage that the detection performance is improved in a scene in which training samples are insufficient.

The present application belongs to the technical field of multi-channel signal detection, and particularly relates to a self-adaptive subspace detection method and system suitable for a partially homogeneous environment, comprising the following steps: Step 1: constructing a target signal subspace matrix and an interference signal subspace matrix; Step 2: constructing a sample covariance matrix by using a training sample; Step 3: constructing a quasi-whitening matrix by using the sample covariance matrix, and then obtaining a whitening matrix by performing quasi-whitening processing; Step 4: constructing an orthogonal projection matrix of the target signal and a corresponding complementary projection matrix; Step 5: constructing an intermediate variable matrix and a non-zero eigenvalue decomposition, and obtaining a non-uniform parameter; Step 6: constructing a detection statistic by using a to-be-detected data matrix, the orthogonal projection matrix of the target signal, the orthogonal projection matrix of the interference signal and the non-uniform parameter; Step 7: determining a detection threshold; and Step 8: determining whether the target exists. The present application improves the detection performance of an airborne radar in a partially homogeneous environment.