Optimization of coded singals based on wavelet neural network

Ahmed, Mustafa Sami (2015) Optimization of coded singals based on wavelet neural network. Masters thesis, Universiti Tun Hussein Onn Malaysia.

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Abstract

Pulse compression technique is used in many modern radar signal processing systems to achieve the range accuracy and resolution of a narrow pulse while retaining the detection capability of a long pulse. It is important for improving range resolution for target. Matched filtering of binary phase coded radar signals create undesirable sidelobes, which may mask important information. The application of neural networks for pulse compression has been explored in the past. Nonetheless, there is still need for improvement in pulse compression to improve the range resolution for target. A novel approach for pulse compression using Feed-forward Wavelet Neural Network (WNN) was proposed, using one input layer and output layer and one hidden layer that consists three neurons. Each hidden layer uses Morlet function as activation function. WNN is a new class of network that combines the classic sigmoid neural network and wavelet analysis. We performed a simulation to evaluate the effectiveness of the proposed method. The simulation results demonstrated great approximation ability of WNN and its ability in prediction and system modeling. We performed evaluation using 13-bit, 35-bit and 69-bit Barker codes as signal codes to WNN. When compared with other existing methods, WNN yields better PSR, low Mean Square Error (MSE), less noise, range resolution ability and Doppler shift performance than the previous and some traditional algorithms like auto correlation function (ACF) algorithm.

Item Type:Thesis (Masters)
Subjects:Q Science > QA Mathematics > QA76 Computer software
ID Code:7552
Deposited By:Normajihan Abd. Rahman
Deposited On:03 Mar 2016 15:19
Last Modified:03 Mar 2016 15:19

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