scholarly journals Total least square clutter cancellation in passive radar

2019 ◽  
Vol 2019 (21) ◽  
pp. 7687-7689
Author(s):  
Haihuan Wang ◽  
Xiaoyong Lyu ◽  
Long Ma
Keyword(s):  
Least Square ◽  
Passive Radar ◽  
Total Least Square

scholarly journals A Sort of New Improved Algorithm For Total Least Square

2015 ◽  
Vol 9 (1) ◽  
pp. 238-247
Author(s):  
Deng Yonghe
Keyword(s):  
Least Square ◽  
New Method ◽  
Unknown Parameters ◽  
Error Equation ◽  
Virtual Observation ◽  
Condition Equation ◽  
Total Least Square ◽  
Improved Algorithm

Aim to blemish of total least square algorithm based on error equation of virtual observation, this paper put forward and deduced a sort of new improved algorithm which selects essential unknown parameters among designing matrix, and then, doesn’t consider condition equation of unknown parameters among designing matrix. So, this paper perfected and enriched algorithm, and sometimes, new method of this paper is better. Finally, the results of examples showed that new mothod is viable and valid.

Modified gradient algorithm for total least square filtering

2006 ◽  
Vol 70 (1-3) ◽  
pp. 568-576 ◽  
Author(s):  
Xiangyu Kong ◽  
Chongzhao Han ◽  
Ruixuan Wei
Keyword(s):  
Least Square ◽  
Gradient Algorithm ◽  
Total Least Square

Penerbitan Maklumat Tiga Dimensi Sebuah Lukisan Garisan Menggunakan Kaedah Jumlah Kuasa Dua Terkecil

2012 ◽  
Author(s):  
Habibollah Haron ◽  
Dzulkifli Mohamed
Keyword(s):  
Linear System ◽  
Line Drawing ◽  
Least Square Method ◽  
Least Square ◽  
Geometric Information ◽  
Ordinary Least Square ◽  
Total Least Square ◽  
Regular Line ◽  
Space Image

Pengekstrakan lukisan garisan melibatkan proses menukar lukisan garisan tidak sekata kepada lukisan sekata, mendapatkan entiti asas simpang, garisan serta kawasan dan seterusnya mendapatkan maklumat tiga dimensi lukisan. Proses penukaran lukisan tidak sekata akan menghasilkan maklumat dua dimensi lukisan dan maklumat geometri dua dimensi lukisan iaitu simpang, garisan dan kawasan masing-masing yang mewakili unjuran bucu, pinggir dan permukaan dalam tiga dimensi. Berdasarkan maklumat geometri dua dimensi ini, jenis kenalaran imej ditentukan untuk membentuk set sistem linear lebih tentu. Grimstead menggunakan empat kenalaran imej dan menggunakan lelaran kuasa dua terkecil untuk menyelesaikan sistem linear lebih tentu yang dihasilkan. Kertas kerja ini akan mencadangkan kaedah jumlah kuasa dua terkecil untuk menyelesaikan sistem linear lebih tentu yang dibentuk oleh dua kenalaran imej. Perbandingan dengan kaedah Grimstead akan ditunjukkan dan penerangan akan dibantu oleh kes kajian dan paparan output. Kata kunci: Terjemahan garisan; Jumlah Kuasa Dua Terkecil; ruang gradien; kenalaran imej; sistem linear Line drawing interpretation involves process of converting irregular line drawing to regular line drawing. The converting process produces junctions, lines and regions that are two-dimensional projection of vertices, edges, and faces of a solid model respectively. Based on the geometric information obtained, image regularities are determined and a over-determined sets of linear systems is developed. Grimstead used the three image regularities in the linear system and iterative ordinary least square to solve them. The paper is intended to propose Total Least Square method in solving over-determined sets of linear system of image regularities of a line drawing. Two image regularities have been used. The solutions obtained are visualized with the help of MATLAB tool. Case study is given to assist the explaination. Key words: Line Interpretation; Total Least Square Method; Gradient Space; Image Regularities; Linear System

Comparing of Coordinate Transformation Based on Total Least Squares and Least Squares

2014 ◽  
Vol 522-524 ◽  
pp. 1211-1214
Author(s):  
Qing Wu Meng ◽  
Lu Meng
Keyword(s):  
Least Squares ◽  
Coordinate Transformation ◽  
Least Square Method ◽  
Coefficient Matrix ◽  
Total Least Squares ◽  
Least Square ◽  
Experimental Results ◽  
Transformation Models ◽  
Total Least Square

The coordinate transformation models based on least square method and total least square are built and discussed. The least square model only includes the errors of observation vectors, the total least square model simultaneously takes into consideration to the errors of observation vectors and the errors of coefficient matrix. The both models are verified and compared in experiment. The experimental results showed that the model of total least square is more in line with actual, and more reasonable than by least square theoretically, and the coordinate transformation solution result of total least square with least square is more near.

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