Distributed CA-CFAR and OS-CFAR Detectors Mentored by Biogeography Based Optimization Tool
| dc.contributor.author | Amel Gouri | |
| dc.date.accessioned | 2021-01-18T08:35:58Z | |
| dc.date.available | 2021-01-18T08:35:58Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | In this paper, distributed constant false alarm rate (CFAR) detection in homogeneous and heterogeneous Gaussian clutter using Biogeography Based Optimization (BBO) method is analyzed. For independent and dependent signals with known and unknown power, optimal thresholds of local detectors are computed simultaneously according to a preselected fusion rule. Based on the Neyman-Pearson type test, CFAR detection comparisons obtained by the genetic algorithm (GA) and the BBO tool are conducted. Simulation results show that this new scheme in some cases performs better than the GA method described in the open literature in terms of achieving fixed probabilities of false alarm and higher probabilities of detection | en_US |
| dc.identifier.uri | http://dspace.univ-msila.dz:8080//xmlui/handle/123456789/23353 | |
| dc.publisher | Université de M'sila | en_US |
| dc.subject | BBO, distributed CFAR detection, CACFAR, OS-CFAR, fusion rule | en_US |
| dc.title | Distributed CA-CFAR and OS-CFAR Detectors Mentored by Biogeography Based Optimization Tool | en_US |
| dc.type | Article | en_US |