Design and analysis of fractal detector for high resolution radars

Salmasi, Mehrdad; Modarres-Hashemi, Mahmoud

doi:10.1016/j.chaos.2007.10.008

Cited by 15 publications

(18 citation statements)

References 28 publications

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“…From Fig. 4, it is noticed that the scaling relations are improved and fairly linear in the range of 2-2 11 . Data with different polarisations have the same linear trend and the Hurst exponent can be calculated by least square linear fitting about 0.65-0.7 for dataset_17 and 0.65-0.68 for dataset_31.…”

Section: Sea Clutter Data Treated As 'Increment'mentioning

confidence: 87%

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Detection of low observable moving target in sea clutter via fractal characteristics in fractional Fourier transform domain

Chen

Guan

et al. 2013

IET Radar, Sonar & Navigation

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Effective detection of low observable moving target at sea is important for remote sensing and radar signal processing. The non-Gaussian property of sea clutter and lack of accurate model make the detection difficult for statistics based detectors. Also the fractal techniques in time domain cannot achieve high detection probability in heavy sea clutter. To help solve the problems, fractal characteristics of IPIX datasets in fractional Fourier transform (FRFT) domain are analysed making use of the fluctuation of FRFT amplitudes and moving target detection algorithms are proposed based on the fractal characteristics in FRFT domain. Firstly, fractal model in FRFT domain is established with fractional Brownian motion model and two judgment and extraction methods are employed for calculating the fractal characteristics in FRFT domain. It is found that sea clutter of different polarisations exhibit fractal behaviours in FRFT domain, that is, self-similarity property, within its corresponding scale-invariant interval. Then, we find that four specific fractal statistics in the best FRFT domain can provide valuable information for developing simple and effective detectors. Finally, traditional amplitude detector and Hurst exponent detector in time domain are compared and the results prove the superior detection ability of low observable moving target without complex computations.

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Section: Sea Clutter Data Treated As 'Increment'mentioning

confidence: 87%

“…To gain deeper understanding of the nature of sea clutter, the concept of fractal has been employed for describing the roughness of sea surface and the scattering from rough surface [11, 12]. It is shown that the sea surface has a fractal nature and is more irregular than the target signal.…”

Section: Introductionmentioning

confidence: 99%

Detection of low observable moving target in sea clutter via fractal characteristics in fractional Fourier transform domain

Chen

Guan

et al. 2013

IET Radar, Sonar & Navigation

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“…Therefore, we can make a distinction between the natural background and man-made objects based on the strength of the fractal characteristics. Man-made target detection from the SAR image is an important study in the area of military application [7].But, the fractal dimension of targets are usually close to their backgrounds, in these cases, it is very hard to distinguish targets .Many researchers have done a lot of work about detection interesting area via the use of fractal image encoding [2][3][4][5][6][14][15]. However, if only fractal dimension is used, the background and the object will be confusing.…”

Section: Introductionmentioning

confidence: 99%

Target Detection in SAR Image Based-on Wavelet Transform and Fractal Feature

Chen

2009

2009 2nd International Congress on Image and Signal Processing

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This paper introduces a small target detection algorithm combined with wavelet transform and high-order fractal feature for SAR image. The new algorithm is composed of three steps :(1) decompose the original image using wavelet transform and (2) extract the wavelet coefficients in low frequency and calculate its high-order fractal parameter to judge the target region, and (3) inverse wavelet transform to obtain the result image. Results are presented for two fractal methods (highorder fractal feature and fitting error).Our analyses show that the effect of using high-order fractal feature is better than fitting error. Based on the experiment result available to us, the small target is detected exactly from the SAR image.

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“…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. Fractal-multiresolution detector: The notion of dimension is central to fractal geometry.…”

mentioning

confidence: 97%

“…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.…”

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confidence: 99%

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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Design and analysis of fractal detector for high resolution radars

Cited by 15 publications

References 28 publications

Detection of low observable moving target in sea clutter via fractal characteristics in fractional Fourier transform domain

Detection of low observable moving target in sea clutter via fractal characteristics in fractional Fourier transform domain

Target Detection in SAR Image Based-on Wavelet Transform and Fractal Feature

Fractal-multiresolution based detection of targets within sea clutter

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