A robust neural network based pulse radar detection for weak signals

Padaki, Aditya V.; George, Koshy

doi:10.1109/radar.2010.5494415

2010 IEEE Radar Conference 2010

DOI: 10.1109/radar.2010.5494415

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A robust neural network based pulse radar detection for weak signals

Aditya V. Padaki

Koshy George

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2010

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Cited by 1 publication

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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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A Novel Application of Support Vector Machines to Detect Targets

Padaki¹,

George

2010

2010 Second International Conference on Computational Intelligence, Modelling and Simulation

Self Cite

View full text Add to dashboard Cite

show abstract

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A robust neural network based pulse radar detection for weak signals

Cited by 1 publication

References 15 publications

A Novel Application of Support Vector Machines to Detect Targets

A Novel Application of Support Vector Machines to Detect Targets

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