Open AccessNovel Implementations of Clutter and Target Discrimination Using Threshold Skewness Method
Author(s) -
Thottempudi Pardhu,
Vijay Kumar
Publication year - 2021
Publication title -
traitement du signal/ts. traitement du signal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.279
H-Index - 11
eISSN - 1958-5608
pISSN - 0765-0019
DOI - 10.18280/ts.380418
Subject(s) - clutter , bin , kurtosis , singular value decomposition , skewness , computer science , artificial intelligence , range (aeronautics) , stationary target indication , moving target indication , constant false alarm rate , tracking (education) , statistical power , algorithm , pattern recognition (psychology) , computer vision , mathematics , radar , radar imaging , statistics , engineering , bistatic radar , pulse doppler radar , telecommunications , psychology , pedagogy , aerospace engineering
Now a day’s defence applications associated to novel, army and military war fields are required wall imaging discrimination. As of now many wall-imaging techniques are designed but cannot discriminate the target and clutter with accurate working. Therefore, a novel advance wall image tracking method is required for differentiate the clutter and human target. In this research work single value decomposition technique is used to estimate the range bin behind the wall target. In order to track the target and clutter single-value-decomposition (SVD) is not sufficient, so that along this SVD, threshold skewness (TS) method has been presented. Combination of SVD-TS giving the accurate long range-bin sensing and directed the human’s targets. SVD-TS method is a statistical scheme, which can realise the amplitude ranges through large number of range-bin scans. This technique improves the accuracy by 98.6%, skewness by 8%, and normalised power by 98.9%. These SVD-TS method is more efficient and compete with existed techniques.