Performance Analysis of Continuous Wave and Pulse Radar Based on Noise Reduction
The use of radar techniques to detect, locate, and identify objects is of considerable in recent years. Various types of radars, including Continuous wave (CW) and Pulse radar, have been developed. These two types of radars are very similar with the excep
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Abstract. The use of radar techniques to detect, locate, and identify objects is of considerable in recent years. Various types of radars, including Continuous wave (CW) and Pulse radar, have been developed. These two types of radars are very similar with the exception that the pulse radar has a relatively high bandwidth receiver and the CW system has a relatively narrow bandwidth receiver. In this paper, we compare the performance of Continuous Wave Radar and Pulse Radar signals to reduce the noise. This paper analyzes the noise reduction algorithms of Continuous wave (CW) and Pulse radar under the heading of signal to noise ratio (SNR). The simulation results indicate that Pulse radar with Matched filter has strong anti-noise ability while Continuous Wave Radar with Wavelet instead of Matched filter. Keywords: Continuous Wave Radar, Pulse Radar, Signal to Noise Ratio, Matched Filter, Wavelets.
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Introduction
The use of radar techniques to detect, locate, and identify objects is of considerable in recent years. Various types of radars, including Continuous wave (CW) and Pulse radar, have been developed.. There are significant differences between Continuous Wave Radar and Pulse Radar in term of noise reduction of received signal. In order to improve the ratio between the echo and the noise, only three things can do: reduce the inherent noise, illuminate the target with more energy and use as little bandwidth as practical. Reduction of the inherent noise is the most suitable among these three things. Weak signal detection is a basic and important problems in radar systems. Traditionally, signals (encompassing desired signals as well as interfering signals) can be classified as deterministic signals, which waveforms defined precisely for all instants of time and stochastic processes, which is defined by an underlying probability distribution [1]. These two broadly defined classes overlook another important class of signals, known as chaotic signals which have very irregular waveform; but are generated by a deterministic mechanism [2]. A chaotic signal share attributes with both deterministic signals and stochastic processes [1]. *
Corresponding author.
James J. (Jong Hyuk) Park et al. (eds.), Future Information Technology, Lecture Notes in Electrical Engineering 309, DOI: 10.1007/978-3-642-55038-6_125, © Springer-Verlag Berlin Heidelberg 2014
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M.S. Islam and U. Chong
Chaos is the very complicated behavior of a low-order dynamical system, because it is both nonlinear and deterministic [1]. It demonstrates a strong notion, permitting the use of a simple deterministic system to illustrate highly irregular fluctuations exhibited by physical phenomena encountered in nature. Recently, some engineering applications of chaos have been reported in literature [3-6]. These can be grouped under two broadly defined categories [3, 4]. One group is synthesis of chaotic signals which includes signal masking and spread-spectrum communications. Another is analysis of chaotic signals. It exploits the fact that some phy
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