Classification of radar echoes using fractal geometry

Azzaz, Nafissa; Haddad, Boualem

doi:10.1016/j.chaos.2017.03.017

Cited by 5 publications

(3 citation statements)

References 29 publications

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“… Radar: A system which bounces radio waves off of targets to determine various characteristics of the target, usually including position, size and velocity. Analysis of radar echoes can use many techniques, including fractal analysis, which classifies shapes seen by the complexity of their geometrical form (Azzaz, 2017 andCherouat, 2008) as shown in Figure 4. Practitioners of radar design, and developers of tools to support simulation and modeling of radar components and environments perceive radar design to be complex.…”

Section: Complex System Examplesmentioning

confidence: 99%

Appreciative Methods Applied to the Assessment of Complex Systems

Watson

Anway

McKinney

et al. 2019

INCOSE International Symp

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Complex systems have characteristics that challenge traditional systems engineering processes and methods. These characteristics have been defined in various ways. INCOSE has previously identified characteristics of complex systems and potential methods to deal with complexity in system development. The purpose of this paper is to provide definitions and describe distinguishing characteristics of complexity using example systems to illustrate approaches to assessing the extent of complexity. The paper applies Appreciative Inquiry to identify and assess complex system characteristics. The characteristics are used to examine several different examples of systems to illuminate areas of complexity. These examples range from seemingly simple systems to complicated systems to complex systems. Different tiers of complexity are identified as a result of the assessment. The paper also identified and introduces topics on managing complexity and the integrating system perspective that represent new directions for the engineering of complex systems. The Appreciative Inquiry approach provides a method for systems engineering practitioners to more readily identify complexity when they encounter it, and to deal more effectively with this complexity once it has been identified.

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Section: Complex System Examplesmentioning

confidence: 99%

Appreciative Methods Applied to the Assessment of Complex Systems

Watson

Anway

McKinney

et al. 2019

INCOSE International Symp

View full text Add to dashboard Cite

show abstract

“…The results show that the fractal theory performs well [12][13][14][15] in the modeling of various radar clutters. Aircraft and other human-made objects do not have fractal characteristics.…”

Section: Introductionmentioning

confidence: 95%

Aircraft Target Classification Method Based on Eemd and Multifractal

Hu¹,

Li²,

Zhang³

et al. 2021

PIER M

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Due to the limitation of low-resolution radar system and the influence of background clutter in the detection process, it is hard for low-resolution radars to classify and identify aircraft targets. To solve the above problems, a classification method for aircraft based on Ensemble Empirical Mode Decomposition (EEMD) and multifractal is proposed, in which the intrinsic modes are obtained by EEMD, and the waveform entropy in the Doppler domain is used to screen and reconstruct the intrinsic modes. The multifractal feature of the target echo data is extracted from the reconstructed signal, and then the aircraft target classification and recognition experiment is carried out with support vector machine. The experimental results show that the feature data extracted by ensemble empirical mode decomposition and multifractal analysis can be used for the classification and identification of civil aircraft and fighter aircraft, and the accuracy rate is about 98.5%, which is higher than that of timedomain multifractal method.

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“…2.2. GBL has been applied to measurements of soil moisture (Cumbrera et al 2012), radar echos (Azzaz and Haddad 2017), urban land cover (Myint and Lam 2005), ham quality (Valous et al 2009), ecosystem services (Roces-Díaz et al 2014), landscape evapotranspiration (Liu and Zhang 2010), deforestation (Pintilii et al 2017), urbanisation (Sung et al 2013), racial segregation (Sui and Wu 2006), biological tissues (Gould et al 2011;Shah et al 2016), and crystallisation (Velazquez-Camilo et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Computation of Lacunarity from Covariance of Spatial Binary Maps

Hingee

Baddeley

Caccetta

et al. 2019

JABES

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We consider a spatial binary coverage map (binary pixel image) which might represent the spatial pattern of the presence and absence of vegetation in a landscape. 'Lacunarity' is a generic term for the nature of gaps in the pattern: a popular choice of summary statistic is the 'gliding-box lacunarity' (GBL) curve. GBL is potentially useful for quantifying changes in vegetation patterns, but its application is hampered by a lack of interpretability and practical difficulties with missing data. In this paper we find a mathematical relationship between GBL and spatial covariance. This leads to new estimators of GBL that tolerate irregular spatial domains and missing data, thus overcoming major weaknesses of the traditional estimator. The relationship gives an explicit formula for GBL of models with known spatial covariance and enables us to predict the effect of changes in the pattern on GBL. Using variance reduction methods for spatial data, we obtain statistically efficient estimators of GBL. The techniques are demonstrated on simulated binary coverage maps and remotely sensed maps of local-scale disturbance and meso-scale fragmentation in Australian forests. Results show in some cases a fourfold reduction in mean integrated squared error and a twentyfold reduction in sensitivity to missing data.Supplementary materials accompanying the paper appear online and include a software implementation in the R language.

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Classification of radar echoes using fractal geometry

Cited by 5 publications

References 29 publications

Appreciative Methods Applied to the Assessment of Complex Systems

Appreciative Methods Applied to the Assessment of Complex Systems

Aircraft Target Classification Method Based on Eemd and Multifractal

Computation of Lacunarity from Covariance of Spatial Binary Maps

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