Non-Rayleigh Sea Clutter: Properties and Detection of Targets

Trunk, G. V.

doi:10.21236/ada028875

Cited by 20 publications

(9 citation statements)

References 13 publications

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“…As a result of the complicated form of the CDF for targets in most non-Gaussian backgrounds, an iterative search is required. Unfortunately, these CDFs do not usually have simple analytical forms; it is common to see them described as integrals, infinite summations, and even Hankel transforms of characteristic functions [17], [20]- [22], [23,Sect. 8.5].…”

Section: Quantitymentioning

confidence: 99%

Detection-Threshold Approximation for Non-Gaussian Backgrounds

Abraham

2010

IEEE J. Oceanic Eng.

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Abstract-The detection threshold (DT) term in the sonar equation describes the signal-to-noise ratio (SNR) required to achieve a specified probability of detection (P d ) for a given probability of false alarm (P f a ). Direct evaluation of DT requires obtaining the detector threshold (h) as a function of P f a and then using h while inverting the often complicated relationship between SNR and P d . However, easily evaluated approximations to DT exist when the background additive noise or reverberation is Gaussian (i.e., has a Rayleigh-distributed envelope). While these approximations are extremely accurate for Gaussian backgrounds, they are erroneously low when the background has a heavy-tailed probability density function. In this paper it is shown that by obtaining h appropriately from the non-Gaussian background while approximating P d for a target in the non-Gaussian background by that for a Gaussian background, the easily evaluated approximations to DT extend to non-Gaussian backgrounds with minimal loss in accuracy. Both fluctuating and non-fluctuating targets are considered in Weibulland K-distributed backgrounds. While the P d approximation for fluctuating targets is very accurate, it is coarser for nonfluctuating targets, necessitating a correction factor to the DT approximations.

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Section: Quantitymentioning

confidence: 99%

Detection-Threshold Approximation for Non-Gaussian Backgrounds

Abraham

2010

IEEE J. Oceanic Eng.

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“…There have been a great number of experimental and theoretical investigations of signals scattered from slightly rough surfaces such as the sea surface [5][6][7], and also analysis of the energy spectra of signals reflected by vegetation [8,9]. This paper adds to this body of work with measurements of clutter with a D-band radar for different types of vegetation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive moving target indication in a windblown clutter environment

Goncharenko

Farquharson

Gorobets³

et al. 2014

IEEE Trans. Aerosp. Electron. Syst.

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The statistical properties of windblown grass and forest clutter, obtained using high-resolution microwave Doppler radar, are analyzed and shown to be nonstationary. In particular, we show that the bandwidth of the Doppler signal depends linearly on the total power of the signal for both types of vegetation. Also, variations in the mean power and spectrum bandwidth of the clutter are similar to the analysis time of existing radars. The model of an adaptive moving target indication (MTI) notch filter is presented and evaluated. It is shown that adaptive filtering significantly improves the efficiency of an MTI system.

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“…The GMD has found utility in modeling radar clutter [1- 6,18]. A Rayleigh mixture distribution (RMD), which is closely associated with the GMD, is often used to model the amplitude characteristics of radar clutter [1][2][3][4][5][6]. Such common amplitude clutter models as Weibull, K, and contaminated Rayleigh can be represented as RMD's [1,6].…”

Section: Introductionmentioning

confidence: 99%

Detection Performance of Signals in Dependent Noise From a Gaussian Mixture Uncertainty Class.

Gerlach¹

1998

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Non-Rayleigh Sea Clutter: Properties and Detection of Targets

Cited by 20 publications

References 13 publications

Detection-Threshold Approximation for Non-Gaussian Backgrounds

Detection-Threshold Approximation for Non-Gaussian Backgrounds

Adaptive moving target indication in a windblown clutter environment

Detection Performance of Signals in Dependent Noise From a Gaussian Mixture Uncertainty Class.

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