Abstract
In this paper, a nonlinear prediction (NLP) method is proposed as an alternative to the conventional linear prediction (LP) method for clutter cancellation. Because of the nonlinearity and non-Gaussianity of a clutter process, a nonlinear predictor is therefore needed to suppress clutter optimally. A memory-based predictor which uses a table look-up strategy to perform NLP is used in this work. The advantages of the memory-based approach are fast learning, algorithmic simplicity, robustness and suitability for parallel implementation. The memory-based predictor is then used as an adaptive detector for small surface target detection embedded in clutter. The effectiveness of the new method is demonstrated using real sea clutter data, and the results show improvement when compared with the conventional LP techniques.
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.
