Research Interests: Computer Science and IEEE
Pulse compression is a significant aspect for improving the radar detection and range resolution. To improve the range detection, the pulse width is increased to overcome the transmitter maximum peak power limitations. However, pulse... more
Pulse compression is a significant aspect for improving the radar detection and range resolution. To improve the range detection, the pulse width is increased to overcome the transmitter maximum peak power limitations. However, pulse compression is accompanied with time sidelobes that can mask the small targets. The Wavelet Neural Network (WNN) is a new technique used for pulse compression sidelobe reduction. In this paper, Morlet function is applied as an activation function for WNN and the backpropagation (BP) is implemented for training the networks. The WNN is applied based on PAT and Px polyphase codes. The performance of WNN is evaluated in terms of Signal to Noise Ratio (SNR) and the computational complexity. The simulation results indicate that the WNN has higher Peak Sidelobe Level (PSL) than the Auto Correlation Function (ACF) with more than 100 dB and higher PSL than the Neural Network (NN) with more than 100 dB.