emitter localization
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2022 ◽  
Vol 20 (4) ◽  
pp. 537-544
Author(s):  
William De Carvalho Rodrigues ◽  
Jose Antonio Apolinario

2021 ◽  
Author(s):  
Md Abdullah Al Imran ◽  
Eray Arik ◽  
Yaser Dalveren ◽  
Mehmet Baris Tabakcioglu ◽  
Ali Kara

Abstract This study aims to evaluate the accuracy of a method proposed for passive localization of radar emitters around irregular terrains with a single receiver in Electronic Support Measures (ESM) systems. Previously, only the theoretical development of the localization method was targeted by the authors. In fact, this could be a serious concern in practice since there is no evidence about its accuracy under the real data gathered from realistic scenarios. Therefore, firstly, an accurate ray-tracing algorithm is adapted to the method in order to enable its implementation in practice. Then, scenarios are determined based on the geographic information system (GIS) map generated to collect high resolution digital terrain elevation data (DTED) as well as realistic localization problems for radar emitters. Next, the improved method is tested with simulations, and thus, its performance is verified for practical implementation in Electronic Warfare (EW) context for the first time in the literature. Lastly, based on the simulation results, the performance bounds of the method are also discussed.


ACS Nano ◽  
2021 ◽  
Author(s):  
James Callum Stewart ◽  
Ye Fan ◽  
John S. H. Danial ◽  
Alexander Goetz ◽  
Adarsh S. Prasad ◽  
...  

2020 ◽  
Author(s):  
Anish Mukherjee

The quality of super-resolution images largely depends on the performance of the emitter localization algorithm used to localize point sources. In this article, an overview of the various techniques which are used to localize point sources in single-molecule localization microscopy are discussed and their performances are compared. This overview can help readers to select a localization technique for their application. Also, an overview is presented about the emergence of deep learning methods that are becoming popular in various stages of single-molecule localization microscopy. The state of the art deep learning approaches are compared to the traditional approaches and the trade-offs of selecting an algorithm for localization are discussed.


Author(s):  
John A. Sauter ◽  
Kellen Bixler ◽  
Sarah Kitchen ◽  
Richard Chase

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