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Radial basis function neural network for pulse radar detection

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3 Author(s)
Khairnar, D.G. ; SPANN Lab., Indian Inst. of Technol., Bombay ; Merchant, S.N. ; Desai, U.B.

A new approach using a radial basis function network (RBFN) for pulse compression is proposed. In the study, networks using 13-element Barker code, 35-element Barker code and 21-bit optimal sequences have been implemented. In training these networks, the RBFN-based learning algorithm was used. Simulation results show that RBFN approach has significant improvement in error convergence speed (very low training error), superior signal-to-sidelobe ratios, good noise rejection performance, improved misalignment performance, good range resolution ability and improved Doppler shift performance compared to other neural network approaches such as back-propagation, extended Kalman filter and autocorrelation function based learning algorithms. The proposed neural network approach provides a robust mean for pulse radar tracking

Published in:

Radar, Sonar & Navigation, IET  (Volume:1 ,  Issue: 1 )