LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Methodology to determine window length for unknown target detection in electronic warfare system

Photo from wikipedia

To detect threat signals in electronic warfare support systems, a detector that uses a plurality of windows with various sizes should be designed such that the length of all the… Click to show full abstract

To detect threat signals in electronic warfare support systems, a detector that uses a plurality of windows with various sizes should be designed such that the length of all the signal sources can be considered. Since a large number of these windows cause excessive computational complexity, the number of windows of the detector is reduced by using a small number of representative windows. In this case, since a window is dedicated to the unknown signal of a certain interval, deterioration of the detection performance is inevitable owing to the inconsistency between the lengths of the received signal and the window size. Hence, the deterioration of the detection performance should be minimised by analysing the relation between the lengths of a window and a signal. However, the conventional analysis methods of detection performance are not suitable because they are based on the premise that the lengths of the signal and window are consistent with each other. The authors propose a novel analysis method using processing gain to overcome this limitation, which can be applied irrespective of the inconsistency between the lengths of a window and a signal. Based on this analysis, they present a method to obtain an optimal window length that minimises degradation of the detection performance and subsequently verify the result using simulation.

Keywords: window; methodology; electronic warfare; window length; detection performance

Journal Title: Electronics Letters
Year Published: 2017

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.