Design of Radial Basis Neural Network Filter for Pulse Compression and Sidelobe Suppression in High Resolution Radar

Reddy, Y. Mallikarjuna; Pasha, I. A.; Vathsal, S.

doi:10.1109/irs.2006.4338140

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Cited by 4 publications

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“…The SSR is largely the same for different power returns from targets. Previously, RBFNNs have been considered for the development of robust noise-tolerant networks [8]- [10]. In contrast, our SVM is able to provide a substantial noise tolerance, enabling the target detection at not only various SNRs but also for various powers of radar returns.…”

Section: Simulation Resultsmentioning

confidence: 99%

A Novel Application of Support Vector Machines to Detect Targets

Padaki¹,

George

2010

2010 Second International Conference on Computational Intelligence, Modelling and Simulation

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In this paper we develop a novel pulse radar detection scheme using Support Vector Machines (SVMs). SVMs are a powerful tool for pattern classification. Exploiting this, we design a SVM for pulse radar detection of targets embedded in noise. An adaptive pre-processing stage is included when the echoes are very weak. It is observed that the signal-to-sidelobe ratio obtained is much higher compared to neural network based pulse radar detection schemes. We further examine the noise tolerance, range resolution ability and Doppler tolerance of the new algorithm for pulse compression, and compare it with the ones available in literature.

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Section: Simulation Resultsmentioning

confidence: 99%