VOL. 11, NO. 1, JANUARY 2016 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences ©2006-2016 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com 502 SIDELOBE REDUCTION USING WAVELET NEURAL NETWORK FOR BINARY CODED PULSE COMPRESSION Musatafa Sami Ahmed 1 , Nor Shahida Mohd Shah 1 and Salihu Ibrahim Anka 2 1 Faculty of Electrical and Electronics Engineering, UniversitiTun Hussein Onn Malaysia (UTHM), Johor, Malaysia 2 Faculty of Computer Science and Information Technology, UniversitiTun Hussein Onn Malaysia (UTHM), Parit Raja, BatuPahat, Johor, Malaysia E-Mail: mustafa_sami87@yahoo.com ABSTRACT Pulse compression technique is a popular technique used for improving waveform in radar systems. Series of undesirable sidelobes usually accompany the technique that may mask small targets or create false targets. This paper proposed a new approach for pulse compression using Feed-forward Wavelet Neural Network (WNN) with one input layer, one output layer and one hidden layer that consists of three neurons. Networks of 13-bit Barker code and 69-bit Barker code were used for the implementation. WNN-based back-propagation (BP) learning algorithm was used in training the networks. These networks used Morlet and sigmoid activation functions in hidden and output layer respectively. The simulation results from the proposed method shows better performance in sidelobe reduction where more than 100 dB output peak sidelobe level (PSL) is achieved, compared to autocorrelation function (ACF). Furthermore, the results show that WNN approach has significant improvement in noise reduction performance and Doppler shift performance compared to Recurrent Neural Network (RNN) and Multi-Layer Perceptron (MLP). Keywords: wavelet neural network (WNN), pulse compression, barker code. INTRODUCTION Pulse compression plays an important role in improving range resolution. Two important factors are considered in radar waveform design; range resolution and maximum range detection. Range resolution is the capability of the radar to separate closely spaced targets, which is related to the waveform pulse width, while maximum range detection is the ability of the radar to detect the farthest target and it is related to the transmitted energy. The narrower the pulse width the better is the range resolution. However, if the pulse width is reduced the amount of energy in the pulse is decreased and hence the maximum range detection gets low. To overcome this limitation, pulse compression mechanism is utilized in radar systems [1]. Pulse compression technique enables radar to get the resolution of short pulse and simultaneously to obtain high energy and that can be achieved by internal modulation of the long pulse [2]. However, this technique has a drawback which generating sidelobe level. If there is a multi-targets environment the sidelobe of one large target may appear or mask small target in another range [3]. The advantages and limitations of pulse compression are discussed in Skolnik [4]. Several techniques were proposed to overcome these limitations, such as mismatched filter [5-7], transversal filter [8], neural network [9-12], fuzzy neural network [13] and genetic algorithm [14]. The mismatched filter and transversal filter techniques can reduce sidelobe by using pules compression filter. However, the limitation of these techniques is that the application of hardware filter increases computational burden and limits real time possibilities. Padaki and George [12] developed both Feedforward Neural Network (FFNN) and Radial Basis Function (RBF). They compared the performance of networks target detection and demonstrated that the feedforward neural network offers better performance. In addition, the RBF is more complex than FFNN. The approach in [13] integrates neural fuzzy network to deal with pulse compression. This approach has advantages in noise reduction performance, range resolution ability, and Doppler tolerance. However, this approach has limitation of computational complexity. Baghel and Panda presented a hybrid model for suppressing sidelobes [15]. The model was designed by combining a matched filter (MF) and a Radial function (RF). The performance of the model is better compared to other techniques such as MLANN and RBFNN. Zhang and Benveniste [16] proposed Wavelet Neural Networks (WNN) as an alternative way for feedforward neural networks that improved the limitations of neural networks and wavelet analysis while it has the advantages and best performance of both of these methods. Chen, et al. [17] implemented WNN in time series prediction and system modeling based on multiresolution learning and the experimental results revealed that WNN has a significant approximation capability and suitability in modelling and prediction. Therefore, it can be a powerful tool in digital signal processing. We have conducted a study and discussed available methods applied in sidelobe reduction, to the best of our knowledge, there no method for sidelobe reduction using WNN. Thus, we presents our study on the