The two-dimensional adaptive LMS (TDLMS) algorithm

Hadhoud, Mohiy M.; Thomas, D.W.P.

doi:10.1109/31.1775

Cited by 338 publications

(115 citation statements)

References 13 publications

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“…LMS algorithm for Uniform Planar Antenna Array (UPA) The two dimensional LMS algorithm[6] [7] gives the optimum weights in two dimensional matrix. The antenna array of size MxN receive the incoming signal x(t) and weights of size MxN are multiplied and summed to give the filter output y(t) and the error signal is evaluated for every iteration.…”

Section: Least Mean Square (Lms) Algorithmmentioning

confidence: 99%

Design of Planar Smart Antenna Using LMS Algorithm

2017

IJAERD

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Section: Least Mean Square (Lms) Algorithmmentioning

confidence: 99%

Design of Planar Smart Antenna Using LMS Algorithm

2017

IJAERD

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“…In case ofx =ȳ, LR is 1. In the second component, σ 2 x and σ 2 y represents the estimation of the contrast and means the similarity of CR between the filter window and the background. In case of σ 2 x = σ 2 y , CR is 1.…”

Section: Step-size Indexmentioning

confidence: 99%

“…Hadhoud et al [2] proposed a two-dimensional LMS filter, an extension of the well-known LMS adaptive algorithm. Soni et al [3] studied the performance of TDLMS filters as pre-whitening filer for the detection of small objects.…”

Section: Introductionmentioning

confidence: 99%

A novel Two-Dimensional LMS (TDLMS) using sub-sampling mask and step-size index for small target detection

Bae

Kim

Ahn

et al. 2010

IEICE Electron. Express

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Abstract:In this paper, we propose a novel TDLMS filter using a sub-sampling mask and step-size index for small target detection in infra-red (IR) imagery. The concept of the proposed TDLMS is to utilize the sub-sampling mask of the weight-matrix having the structure reducing effects of the target pixels in order to predict the background exactly. And the nonlinear step size using the luminance rate (LR) and contrast rate (CR) is used and then background is predicted finally by using the two-dimensional Gaussian distance (TDGD). Experimental results show that the proposed method exhibits higher detection rates in comparison to the conventional TDLMS filter. Keywords: small target detection, TDLMS filter, IR Classification: Microwave and millimeter wave devices, circuits, and systems [5] Y. Cao, R. Liu, and J. Yang, "Small target detection using twodimensional least mean square (TDLMS) filter based on neighbor analysis," Int. References

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“…In 1988, a researcher has extended 1-DLMS to 2-DLMS for estimation of nonstationary images [11]. This was the major breakthrough in the field of 2-DAF but, performance of this 2-DLMS was found to be constrained due to its very low convergence speed and high mean square error (MSE).…”

Section: Introductionmentioning

confidence: 99%

A Novel Two Dimensional Adaptive Filtering Algorithm for Image De-Noising via Fractional Gradient

Rizvi¹,

Ahmad²,

Zubair³

et al. 2017

IJSEIA

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In the recent decade it has been witnessed that raster images are the primary source of information for numerous applications such as bio-medical, law enforcement, geographical information system (GIS), photography, astronomy, etc. Primarily, the quality of raster images compromises due to the surrounding factors of these applications. Because, it is very difficult to control surrounding parameter (light, motion, distance) while acquiring images. Therefore, the image acquisition in these applications is very much prone to the noise. In the literature, researchers have targeted this issue and have already devised classical image filters for image de-noising. Afterwards, in the recent years the performance of classical filtering was further improved by employing two dimensional adaptive filters (2-DAF) for image de-noising and enhancement. In the literature, researchers have reported the performance comparisons of various 2-DAF specifically for image restoration, enhancement, estimation, and de-noising. In this paper an extended version of one dimensional fractional least mean square (1-DFLMS) to two dimensional fractional least mean square (2-DFLMS) is presented. Moreover the performance of the proposed algorithm has been rigorously compared with the existing and most employed 2-DAF algorithm namely, two dimensional least mean square (2-DLMS), two dimensional variable step size least mean square (2-DVSSLMS). The simulation results illustrate the notable performance edge of the proposed algorithm with the existing approaches.Keywords: Image de-noising, two-dimensional adaptive filtering, least mean square (LMS), variable step size least mean square (VSSLMS), fractional least mean square (FLMS)

show abstract

The two-dimensional adaptive LMS (TDLMS) algorithm

Cited by 338 publications

References 13 publications

Design of Planar Smart Antenna Using LMS Algorithm

Design of Planar Smart Antenna Using LMS Algorithm

A novel Two-Dimensional LMS (TDLMS) using sub-sampling mask and step-size index for small target detection

A Novel Two Dimensional Adaptive Filtering Algorithm for Image De-Noising via Fractional Gradient

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