153 results about "Scatter matrix" patented technology

The invention provides an MIMO radar target blind detection method based on characteristic values under a correlated noise background. The method is suitable for large-array bistatic MIMO radar with the receiving and transmitting number of array elements and the snapshot number being close. The method comprises the steps that a random matrix theory is used as a tool, the defects that in the prior art, the snapshot number is insufficient and the target detection performance under the correlated noise background is lowered are overcome, and a random matrix model of observation data is established by echo signals under the correlated noise background; the ratio of the maximum characteristic value and the minimum characteristic value of an echo data covariance matrix is calculated to be used as the detection statistical magnitude; the freedom probability theory and Stieltjes conversion are used for deriving a threshold value expression of target detection under the correlated noise background; the threshold value is used as a judgment threshold for detecting a target. Simulation experiments show that the method is suitable for blind detection under the condition that a noise variance and a target scattering matrix are unknown, and the robustness of target detection under the correlated noise environment is obviously improved.

The present invention provides a method for distinguishing nonlinear subspace of one-dimensional distance image which belongs to field of radar target recognition. Target one-dimensional distance image is transferred nonlinearly and mapped to high-dimensional characteristic space, nonlinear regular sub-image characteristics of each kind of target is obtained by characteristic transform matrix of high-dimensional characteristic space to form nonlinear sub-image space of each kinds of target, when the radar one-dimensional distance image of target is input, the sort of one-dimensional distance image is determined according to Euclidean distance between nonlinear regular sub-image and nonlinear sub-image space. Step: radar one-dimensional distance image training vector of target is determined; between-class scatter matrix sB after nonlinear transform and kernel function are determined; nonzero eigenvalue of C and corresponding eigenvector are determined; kind-in scatter matrix Q is determined; nonzero eigenvalue of Q and corresponding eigenvector are determined; all training one-dimensional distance image nonlinear regular sub-image of each kind target are determined; nonlinear sub-image space of each kind of target is determined; input one-dimensional distance image nonlinear regular sub-image of target is dertermined; Euclidean distance between nonlinear regular sub-image and nonlinear sub-image space is determined; sort number of input target one-dimensional distance image is determined.

The invention discloses a method for extracting the features of the one-dimensional range profile of a radar true/false target in a maximum margin subspace, which belongs to the technical field of radar target recognition. First, a maximum margin subspace matrix W is trained based on a training sample set about the one-dimensional range profile of a radar target. Then, during training, an objective equation for solving W is constructed based on a between-class scatter matrix SB and a within-class scatter matrix SW of a training sample xij. Finally, based on the trained matrix W, the one-dimensional range profile of a radar true/false target of which the sub image features are to be extracted is mapped to the matrix W to get sub image features. By increasing the between-class scatter distance and reducing the within-class scatter distance, the between-class separation interval reaches the maximum, and between-class overlap is reduced. Moreover, the dimension of the feature vector obtained by the method is not restricted by the number of classes, a more favorable feature dimension can be obtained, and therefore, the efficiency of target recognition is improved.

The invention discloses a tea infrared spectrum classification method of fuzzy discrimination clustering. A linear discrimination analyzing method is employed to extract the identification information of 14-dimensional training sample data, and 14-dimensional test sample data is projected to a discrimination vector to obtain the two-dimensional test sample data. The two-dimensional test sample data are subjected to fuzzy C-means clustering. A fuzzy interclass scattering matrix is calculated according to an initial clustering center, and the fuzzy total scattering matrix is calculated. An eigenvector is calculated according to the fuzzy interclass scattering matrix and the fuzzy total scattering matrix. A clustering central value is calculated in a characteristic space through the fuzzy membership function value. The average value of each 14-dimensional training sample is calculated respectively, and the Euclidean distance of the average values of the clustering central value and the training samples of the test samples. If the Euclidean distance from the clustering central value to the training samples is minimal, the tea belonging to the clustering central value is of the same type with the tea of the training samples, thereby realizing correct classification of different tea types.

The invention discloses a fault diagnosis method based on nested iterative Fisher discriminant analysis, which overcomes the problem of singularity of a within-class scatter matrix, the problem of number limit of discriminant components and a linear correlation problem of the discriminant components in the conventional method, fully explores potential information contained in process data and can be used for effectively differentiating the process data in different types. The method is simple and easy to implement, the performance of online fault diagnosis is greatly improved, the reliability and credibility of actual online fault diagnosis are enhanced, and faults can be accurately repaired by industrial engineers, so that safe and reliable operation of actual production and high quality pursuit of the product can be guaranteed.

The invention provides a method for estimating a Faraday rotation angle (FRA) in satellite borne complete polarization synthetic aperture radar (SAR) data. The technical scheme comprises that the method comprises the steps of using measurement vector quantity of a satellite borne complete polarization SAR scattering matrix as input, (1) obtaining a polarimetric covariance matrix (PCM), obtaining the corresponding PCM of the measurement vector quantity according to the measurement vector quantity of the scattering matrix, (2) constructing a complex number for estimating the FRA, constructing the complex number relevant to the FRA through a real part of a non-diagonal element of the corresponding PCM of the measurement vector quantity and a diagonal element of the corresponding PCM of the measurement vector quantity, (3) estimating the FRA, and achieving the FRA estimation through the fact that the operations of angle taking and angle halving are carried on the complex number constructed in the step (2). The method for estimating the FRA of the satellite borne complete polarization SAR data is essentially free of influences of unbalanced system phases and steady in property.