The Performance of Decentralized CFAR Detection Using Biogeography Based Optimization

Gouri, Amel; Mezache, Amar; Oudira, Houcine

doi:10.1109/cist.2018.8596583

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…The result of hypotheses testing according to Equation (7) for each range bin in a period can be successively stored in the target DO matrix expressed as [14] where h represents the result of hypotheses testing according to Equation ( 5), r = 1, 2, … R represents the individual range bin, and p = 1, 2, … P represents periods of received radar signals.…”

Section: Cell-average Constant False Alarm Rate Detectorsmentioning

confidence: 99%

“…Distributed CFAR detection in homogeneous and heterogeneous Gaussian clutter using the Biogeography Based Optimisation (BBO) method was introduced and analysed by the authors in Ref. [7]. For independent and dependent signals with known and unknown power, optimal thresholds of local detectors were computed simultaneously according to a preselected fusion rule.…”

Section: Introductionmentioning

confidence: 99%

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An improved radar clutter suppression by simple neural network

Perďoch,

Gažovová,

Pacek

2023

IET Radar Sonar & Navi

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The presented paper is further focused on the presentation and subsequent assessment of utilising a proposed Neural Network (NN) with simple architecture in the role of a signal preprocessing algorithm for the Constant False Alarm Rate detector and the fixed threshold detector applied on a Range‐Doppler (RD) map with the aim of radar clutter impact reduction and minimisation of processing time. Based on a comparison of all tested algorithm results, it is possible to state that utilising the proposed NN with simple architecture led to reducing the impact of radar clutter when detecting radar targets on RD maps created from provided datasets. Comparing the mean processing time tmean values of all tested algorithms, the authors can state that employing the proposed NN in combination with the fixed threshold detector led to a significant improvement in the computation time needed for processing one RD map while preserving the suppression of radar clutter and detection of the radar targets.

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Section: Cell-average Constant False Alarm Rate Detectorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An improved radar clutter suppression by simple neural network

Perďoch,

Gažovová,

Pacek

2023

IET Radar Sonar & Navi

View full text Add to dashboard Cite

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Thresholding optimization of global navigation satellite system acquisition with constant false alarm rate detection using metaheuristic techniques

Hassani,

Toumi,

Benkrinah

et al. 2024

Int J Communication

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SummaryIn this paper, the enhancement of global navigation satellite system (GNSS) adaptive acquisition using metaheuristic optimization techniques is proposed. The principal goal of this work is to optimize the cell averaging constant false alarm rate (CA‐CFAR) thresholding in Rayleigh fading channels. In GNSS acquisition, pilot and data blocks may have different thresholds. Thus, the optimization will focus on two scaling factors ( and ). Two fusion rules have been used here (“AND” and “OR”). Due to their performances in different optimization problems, metaheuristics have been chosen to be our tool for solving this kind of problem. Simulation results show that the optimized thresholds have an important influence on the performance of the acquisition system.

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The Performance of Decentralized CFAR Detection Using Biogeography Based Optimization

Cited by 2 publications

References 6 publications

An improved radar clutter suppression by simple neural network

An improved radar clutter suppression by simple neural network

Thresholding optimization of global navigation satellite system acquisition with constant false alarm rate detection using metaheuristic techniques

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