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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/1111
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| DC Field | Value | Language |
|---|
| contributor.author | Sailaja, Ananki | - |
| contributor.author | Sahoo, Ajit Kumar | - |
| contributor.author | Panda, G | - |
| contributor.author | Baghel, Vikas | - |
| date.accessioned | 2009-12-23T08:29:05Z | - |
| date.available | 2009-12-23T08:29:05Z | - |
| date.issued | 2009 | - |
| identifier.citation | Indicon 2009, 18-20, December 2009, Gujarat, India | en |
| identifier.uri | http://hdl.handle.net/2080/1111 | - |
| description | Copyright for the paper belongs to Proceedings Publisher | en |
| description.abstract | Matched filtering of biphase coded radar signals
create unwanted sidelobes which may mask some of the desired
information. This paper presents a new approach for pulse
compression using recurrent neural network (RNN). The 13-bit
and 35-bit barker codes are used as input signal codes to RNN.
The pulse radar detection system is simulated using RNN. The
results of the simulation are compared with the results obtained
from the simulation of pulse radar detection using Multilayer
Perceptron (MLP) network. The number of input layer neurons
is same as the length of the signal code and three hidden neurons
are taken in the present systems. The Simulation results show
that RNN yields better signal-to-sidelobe ratio (SSR) and doppler
shift performance than neural network (NN) and some traditional
algorithms like auto correlation function (ACF) algorithm. It
is also observed that RNN based system converges faster as
compared to the MLP based system. Hence the proposed RNN
provides an efficient means for pulse radar detection. | en |
| format.extent | 235609 bytes | - |
| format.mimetype | application/pdf | - |
| language.iso | en | - |
| publisher | DA-IICT | en |
| subject | RNN | en |
| subject | Pulse Compression | en |
| subject | ACF | en |
| subject | SSR | en |
| subject | biphase code | en |
| title | A Recurrent Neural Network Approach to Pulse Radar Detection | en |
| type | Article | en |
| Appears in Collections: | Conference Papers
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Files in This Item:
| File |
Description |
Size | Format |
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| aksfin1.pdf | | 230Kb | Adobe PDF | View/Open |
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