Abstract
Several forms of sequential hypothesis testing algorithms are described and their performance as classification algorithms for automatic target recognition is evaluated and compared. Several forms of parameteric algorithms, as well as a sequential form of a useful nonparametric algorithm are considered. The primary focus is the design of algorithms for automatic target recognition that produce maximally reliable decisions while requiring, on the average, a minimum number of backscatter measurements. The tradeoffs between the average number of required measurements and the error performance of the resulting algorithms are compared by means of Monte-Carlo simulation studies.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Aerospace and Electronic Systems
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
