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URN etd-0326104-020930 Statistics This thesis had been viewed 3008 times. Download 650 times. Author Chuan-Hsiu Tseng Author's Email Address firstname.lastname@example.org Department Communication Engineering Year 2003 Semester 1 Degree Master Type of Document Master's Thesis Language English Page Count 208 Title The Practical Aspect of Wavelet Basis in Analysis of Period and Characteristics of Radar Signal Keyword wavelet radar radar wavelet Abstract Radar Signal Identification is also termed Specific Emitter Identification (SEI), or Unique Signal Identification (USI). The discussions related to this practical aspect of technique in early stages, the threaten parameters used included Frequency, Pulse Repetition Frequency (PRF), Pulse width (PW), Scan type, Sweep time, Scan rate, Direction Finding (DF) etc.
Currently analysis could not satisfy army tactical requirement when two radar’s signal be received with pretty similar parameters, especially identify two same model radar’s emitter. A new ideal based on wavelet transform offers different method to support and enhance the analysis ability. By changing time domain’s scale to deconstruct the signal, and then observe and identify radar signal in a new angle with wavelet transformed particularity. Compared the results that reconstructed by new analysis architecture, it indicate the better solution we proposed that will help radar signal identification.
The main purpose of the thesis is to construct a different analyzed model for radar signal analysis which can obtain the best benefit in improving the performance of current system under the most economical situation. Due to the capability of personal computer is far ahead improved than several years ago; its price cost is also far less than the workstation which worked with UNIX-OS. In a presupposition of not to change the operating method in hand and to put in too much reformation cost, we join the wavelet transform into analysis structure in existence to strengthen the ability in identifying radar signals. The experiment was proved by simulating and real signals, that wavelet transform may unroll the background features and signal’s characters in the majority of signals.
Advisor Committee Chau-Yun Hsu - advisor
Dau-Chyrh Chang - co-chair
none - co-chair
Files Date of Defense 2004-02-24 Date of Submission 2004-03-26