Intelligent Use of CFAR Algorithms

Antonik, Paul; Bowles, B.; Capraro, Gerard T.; Hennington, L. D.; Koscielny, Albert J.

doi:10.21236/ada267755

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…Conversely, if Δ r G ° > 0, then K < 1 (i.e., the process is reactant favored). This determination does not depend on the isothermal conditions ( p = constant or V = constant) under which K is calculated, , as suggested by Antonik …”

Section: Spontaneity Equilibrium and The Meaning Of δG δR G δG° And δ...mentioning

confidence: 72%

“…This determination does not depend on the isothermal conditions (p = constant or V = constant) under which K is calculated, 73,74 as suggested by Antonik. 75 Δ r G differs from Δ r G°, for at a given temperature the value of Δ r G°is fixed, but the value of Δ r G is determined by two terms: Δ r G°and a concentration-dependent term. These two terms can be joined into only one term, which depends on the quantity Q/ K. That is, making use of eqs 13 and 14, the following equation can be obtained…”

Section: ' Methodologymentioning

confidence: 99%

See 1 more Smart Citation

First-Year University Chemistry Textbooks’ Misrepresentation of Gibbs Energy

Quílez¹

2011

J. Chem. Educ.

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This study analyzes the misrepresentation of Gibbs energy by college chemistry textbooks. The article reports the way first-year university chemistry textbooks handle the concepts of spontaneity and equilibrium. Problems with terminology are found; confusion arises in the meaning given to ΔG, Δ r G, ΔG°, and Δ r G°, which results in many textbooks not differentiating between ΔG and Δ r G. Also, there is confusion over when standard conditions apply and when they do not. A problem with the proper use of units is also found. Finally, it is suggested that most of these difficult concepts could be removed from the first-year university chemistry syllabus because (i) an accurate presentation of Gibbs energy would be far beyond an introductory chemistry level and (ii) current attempts to introduce those difficult concepts in first-year university chemistry courses are usually full of misleading formulations.

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Section: Spontaneity Equilibrium and The Meaning Of δG δR G δG° And δ...mentioning

confidence: 72%

Section: ' Methodologymentioning

confidence: 99%

First-Year University Chemistry Textbooks’ Misrepresentation of Gibbs Energy

Quílez¹

2011

J. Chem. Educ.

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“…Many CFAR detection algorithms that are resistant to inhomogeneous clutter have been proposed, for instance, see [2, 3] for a comparative study. In the design and assessment of those CFAR algorithms, the following statistical distribution function, known as the ɛ ‐contaminated model, is assumed [4]

f = false(1 - ε false) g + ε h, 0 < ε < 1

in which For CFAR schemes working on distributions with one parameter: g has exponential distribution, the only‐one‐parameter of which governs the power of the samples in those resolution cells where only thermal noise exists; and h is another exponential distribution, the only‐one‐parameter of which governs the high power of target‐like samples in those resolution cells where there are target and/or clutter returns [2].…”

Section: Introductionmentioning

confidence: 99%

Modelling of inhomogeneity in radar clutter using Weibull mixture densities

Cao

2014

IET Radar, Sonar & Navigation

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In this study, it is demonstrated that inhomogeneity observed in a variety of radar experimental data can be modelled as a finite mixture of Weibull densities. An algorithm for identifying the parameters of each Weibull component in the mixture is developed. The proposed mixture identification algorithm is then applied to three representative sets of radar experimental data, and the quality of the statistical fit is assessed using the p-value, which is estimated based on the Kolmogorov-Smirnoff distance. An application of the Weibull mixture identification algorithm to detect an extended ground target is demonstrated. It is shown that based on the proposed Weibull mixture model, an optimal constant false-alarm rate detector can be implemented even in the presence of inhomogeneity.

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“…This non-homogeneity is caused by either an extended clutter edge or the presence of interfering targets. In order to adapt to the presence of multiple targets and impulsive clutter within the reference window, many modifications of the conventional CA-CFAR have been proposed in the literature [4][5][6]. Tsakalides et al [7] studied the design and performance of CFAR processors for the case of alpha-stable measurements.…”

Section: Introductionmentioning

confidence: 99%

Robust radar detection of CA, GO and SO CFAR in Pearson measurements based on a non linear compression procedure for clutter reduction

Messali

Soltani

Sahmoudi

2007

SIViP

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This paper deals with the constant false alarm rate (CFAR) radar detection of targets embedded in Pearson distributed clutter. We develop new CFAR detection algorithms-notably cell averaging (CA), greatest of selection (GO) and smallest of selection SO-CFAR operating in Pearson measurements based on a non-linear compression method for spiky clutter reduction. The technique is similar to that used in non uniform quantization where a different law is used. It consists of compressing the output square law detector noisy signal with respect to a non-linear law in order to reduce the effect of impulsive noise level. Thus, it can be used as a pre-processing step to improve the performance of automatic target detection especially in lower generalised signal-to-noise ratio (GSNR). The performance characteristics of the proposed CFAR detectors are presented for different values of the compression parameter. We demonstrate, via simulation results, that the pre-processed compression procedure is computationally efficient and can significantly enhance detection performance.

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Intelligent Use of CFAR Algorithms

Cited by 6 publications

References 14 publications

First-Year University Chemistry Textbooks’ Misrepresentation of Gibbs Energy

First-Year University Chemistry Textbooks’ Misrepresentation of Gibbs Energy

Modelling of inhomogeneity in radar clutter using Weibull mixture densities

Robust radar detection of CA, GO and SO CFAR in Pearson measurements based on a non linear compression procedure for clutter reduction

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