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568results about How to "Heavy calculation" patented technology

Iterative least square method-based MIMO (multiple input multiple output) radar DOA (direction-of-arrival) estimation method

The invention discloses an iterative least square method-based MIMO (multiple input multiple output) radar DOA (direction-of-arrival) estimation method, which is characterized in that receiving and transmitting array response matrixes on which dimension-reduced processing is performed are solved by using an iterative least square method. The iterative least square method-based MIMO radar DOA estimation method comprises the following steps: firstly, performing the dimension-reduced processing on echo data matrixes of multiple radar transmitted pulses and the receiving and transmitting array response matrixes; then, establishing cost functions under the least square condition, and solving the cost functions by utilizing a gradient descent-based iterative method; finally, estimating the direction of a target by utilizing known receiving and transmitting array manifolds. Compared with a traditional monostatic MIMO radar array DOA estimation method, the iterative least square method-based MIMO radar DOA estimation method disclosed by the invention directly obtains the DOA estimation of the target, and does not need to perform spectrum peak search. Noise is effectively suppressed by adopting the dimension-reduced processing, and the estimation accuracy under low signal to noise ratio is improved; the estimation, the inversion and the eigenvalue decomposition operation of high-dimensional data covariance matrixes are avoided; the defects that the calculated amount is high and the needed sample number is large when the traditional array DOA estimation method is applied to a monostatic MIMO radar are overcome.
Owner:XIDIAN UNIV

Clustering and reclassifying face recognition method

The invention discloses a clustering and reclassifying face recognition method, which comprises the steps of acquiring a training sample; carrying out equalization processing on the training sample; carrying out Gabor texture feature extraction on face images, and acquiring a feature vector corresponding to each face image after feature extraction; carrying out dimension reduction on acquired Gabor texture features of each face image to acquire feature vectors after dimension reduction; carrying out a clustering operation until distance convergence so as to complete clustering; classifying all of the clustered feature vectors to acquire a plurality of subclasses, calculating to determine each vector mean value, and calculating to acquire a within-class distance and an among-class distance; carrying out feature extraction and preprocessing on face images of a target to be recognized, acquiring a feature vector after projection transformation, and calculating the distance between the acquired feature vector and the feature vectors in each subclass sequentially so as to acquire the similarity; and determining identity information of the target to be recognized. The method disclosed by the invention can shorten the among-class distance so as to reduce an error in the acquisition process, and the accuracy of face recognition is improved.
Owner:NANJING UNIV OF POSTS & TELECOMM

Large-scale time-lag electric system characteristic value calculation method based on EIGD

Te invention discloses a large-scale time-lag electric system characteristic value calculation method based on EIGD. The method includes the steps of establishing a time-lag electric system model, converting the formula of the time-lag electric system model into the abstract cauchy problem, converting the characteristic value of the time-lag electric system model into the characteristic value of an infinitesimal generator of the calculated and converted formula, conducting discretization on the infinitesimal generator to obtain an approximate matrix of the infinitesimal generator, obtaining the approximate matrix where displacement processing is conducted by conducting displacement processing on the approximate matrix, obtaining an inverse matrix after inverse conversion is conducted on the approximate matrix where displacement processing is conducted, converting the partial characteristic value required to be calculated into the partial characteristic value with the maximum module value, calculating the partial characteristic value, with the maximum module value, of the inverse matrix through the Arnoldi algorithm, and obtaining the characteristic value lambda of the approximate matrix. Verification is conducted through the Newton iteration method, and the accurate characteristic value and the accurate characteristic vector of a time-lag electric system are obtained through calculation.
Owner:SHANDONG UNIV

Mobile phone information based section speed calculation method

The invention relates to a mobile phone information based section speed calculation method. The method is used for calculating the average speed information of a vehicle at each section through the analysis and the treatment of mobile phone information and the combination of traffic road position and direction. The key points of calculating the section average speed lies in massive data processing and precise positioning of a mobile phone user in a movement process. The problems are solved through two-stage three-time map matching in the invention. The map matching at the first stage comprises the following step of: matching Cell with sections, and the map matching at the second stage comprises the following steps of: establishing a subordination relationship of mobile phone data points and the sections through matching user data points with the sections two times, finally calculating the traffic speed of each user on the sections by using the Cell position in which continuous data points are located and the time, removing part of abnormal speeds in the traffic speeds, summing the traffic speeds and averaging the sum to obtain the average speed of each road. In the invention, the large amount of calculation caused by directly matching each data point with the sections is avoided, and the calculation efficiency is improved. The accuracy of the mobile phone data and the sections is improved through a proximity principle and a direction consistency principle.
Owner:BEIJING UNIV OF TECH +1

Binocular vision-based unmanned aerial vehicle aerial autonomous refueling fast docking navigation method

InactiveCN106934809AAccurately determineFeature points are accurate and usefulImage enhancementImage analysisMachine visionImaging processing
The invention relates to a binocular vision-based unmanned aerial vehicle aerial autonomous refueling fast docking navigation method and belongs to the machine vision and image processing field. The method includes the following steps that: a binocular camera system with an optical filter shoots a refueling taper sleeve with an optical marker lamp, so that a left image and a right image are obtained; gray processing, binarization and median-value filtering are performed on the left image and the right image sequentially, all communicated regions in the left image and the right image are searched and marked through adopting a regional growth method, and the centroids of each of the communicated regions are calculated and are adopted as feature points; improved haar wavelet transform is adopted to describe all the feature points, and the description vectors of all the feature points are obtained, the feature points of the left image and the right image are matched through adopting the minimum Euclidean distance method, so that the feature point pars of the left image and the right image are obtained; and the three-dimensional coordinates of the feature points are calculated by using a binocular vision principle, and the least squares method is adopted to perform spatial circle fitting on the three-dimensional coordinates, and the end surface circle curve of the refueling taper sleeve is obtained, and the center, normal vector and radius of the circle are obtained through calculation.
Owner:XIAMEN UNIV
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