Fractal characterisation of sea-scattered signals and detection of sea-surface targets

Lo, T.; Leung, Henry; Litva, J.; Haykin, S.

doi:10.1049/ip-f-2.1993.0034

Cited by 121 publications

(67 citation statements)

References 2 publications

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“…It was shown that the presence of a target changes the fractal dimension of the radar's returns and this variation could be used to detect the presence of a target. However, as stated in [2], the detection results are preliminary and are by no means conclusive at that stage of research. Salmasi and Modarres-Hashemi [3] demonstrated that the fractal detector has great capabilities in the rejection of coloured clutter.…”

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confidence: 88%

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Fractal-multiresolution based detection of targets within sea clutter

Pournejatian

Nayebi

2012

Electron. Lett.

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A wavelet transform focuses on localised signal structures with a zooming procedure that progressively reduces the scale parameter. On the other hand, fractal geometry has recently been applied to the analysis of high range resolution radar sea clutters. Using both concepts in designing a new detector, reveals considerable improvement in performance of target detection within sea clutter. In support of this argument, simulation results using real radar data samples are presented.Introduction: The concept of fractal geometry has been employed to model the roughness of sea surface and to investigate the scattering from rough surfaces [1]. Haykin et al.[2] applied fractal geometry theory to the analysis of real radar signals scattered from rough sea surfaces collected using IPIX radar. It was shown that the presence of a target changes the fractal dimension of the radar's returns and this variation could be used to detect the presence of a target. However, as stated in [2], the detection results are preliminary and are by no means conclusive at that stage of research. Salmasi and Modarres-Hashemi [3] demonstrated that the fractal detector has great capabilities in the rejection of coloured clutter. Nayebi and PourNejation [4] have not used the fractal dimension directly. Instead, correlation coefficients between the logarithm of box-counts and the scales in the time domain have been utilised. In our previous paper [5], an enhanced fractal based detection method was presented.In this Letter we propose a novel fractal based detector with multiresolution analysis, which takes into account the fractal dimension of the received signal. The role of this input is to provide finer analysis of the possible target echo embedded in a sea surface backscattered signal. Performance comparison of this detector and a fractal based [3] as well as energy detectors for real high resolution samples of sea clutter and noise shows significant improvement. The real data samples have been captured by IPIX radar [6].

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confidence: 88%

“…Haykin et al [2] applied fractal geometry theory to the analysis of real radar signals scattered from rough sea surfaces collected using IPIX radar. It was shown that the presence of a target changes the fractal dimension of the radar's returns and this variation could be used to detect the presence of a target.…”

mentioning

confidence: 99%

Fractal-multiresolution based detection of targets within sea clutter

Pournejatian

Nayebi

2012

Electron. Lett.

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“…For a complex signal, it is also convenient to make use of the Error Vector Magnitude (EVM) as an accurate measure of any distortion introduced by the adaptive scheme on the received signal [8]. …”

Section: Case (Vii): Error Vector Magnitude (Evm Beamforming Algorithmsmentioning

confidence: 99%

A New TURBO LMS Beamforming for Mobile Communication

Kumaraswamy¹,

Aaquib²,

Younus³

2010

IJCA

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Array processing involves manipulation of signals induced on various antenna elements. Its capabilities of steering nulls to reduce co-channel interferences and pointing independent beams towards various users, as well as its ability to provide estimate of directions of radiating sources, make it attractive to the mobile communications system designer.In this paper existing Least Mean Square (LMS) algorithm is modified to obtain lesser Mean Square Error (MSE) by using leaky factor in the weight updation.A novel approach named as TURBO LMS is proposed which reduces the Mean Square Error and increases the convergence speed by a large amount as compared to existing beamforming algorithms. Simulation of Beamforming algorithms namely Least Mean Square (LMS), Leaky Least Mean Square (LLMS) and novel TURBO LMS algorithm are done for various look directions and jammer configurations and their MSE characteristics are compared. MATLAB simulation shows that the proposed TURBO LMS algorithm has improved convergence as compared to existing beamforming algorithms. Performance of TURBO LMS algorithm is measured by varying number of array elements. KeywordsAdaptive array beamforming, LMS algorithm, TURBO LMS and Mean Square Error.

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“…In high sea state conditions single statistical distribution models cannot fully described sea clutter characteristics; generally it is difficult to obtain a satisfactory result. Sea ripple can be seen as corresponding to different roughness of surface, so some scholars have proposed the theory with fractal sea clutter nonlinear analysis and modeling [6]. Fractal characteristics of sea clutter has been described [7], multifractal and extended Fractal are often used to describe the sea-clutter [8].…”

Section: Introduction Of Sea Cluttermentioning

confidence: 99%

Sea clutter Target Recognition Based on Modular Neural Network

Yi¹,

Ji²,

Liu³

et al. 2016

Proceedings of the 6th International Conference on Electronic, Mechanical, Information and Management Society

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Abstract. Sea clutter is a highly nonlinear signal; it has a non-stationary nature both in time and space. Many scholars found that sea clutter data are chaotic. This indicates that the system has some internal rules, but it is difficult to make an analytic formula. The BP neural network with self-organizing fuzzy spatial mapping ability of artificial happens to be a powerful tool for such kind of problem. But it is not enough to get a better result for sea clutter target recognition use an only predict method. So we use modular integrated neural network method in this experiment. The results show that the modular integrated neural network can improve the recognition performance.

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Fractal characterisation of sea-scattered signals and detection of sea-surface targets

Cited by 121 publications

References 2 publications

Fractal-multiresolution based detection of targets within sea clutter

Fractal-multiresolution based detection of targets within sea clutter

A New TURBO LMS Beamforming for Mobile Communication

Sea clutter Target Recognition Based on Modular Neural Network

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