Parameter estimation of LFM signals

Linear frequency modulated (LFM) signal is widely used in many areas including sonar, radar and communication system. Many methods have been proposed in revealing the joint time-frequency characteristics of the LFM, such as, short-time Fourier Transform (STFT), Wigner-Ville distribution (WVD) and Ra...

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Main Author: Cai, Zhemin
Other Authors: Bi Guoan
Format: Final Year Project
Language:English
Published: 2018
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Online Access:http://hdl.handle.net/10356/75063
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-750632023-07-07T16:06:13Z Parameter estimation of LFM signals Cai, Zhemin Bi Guoan School of Electrical and Electronic Engineering DRNTU::Engineering Linear frequency modulated (LFM) signal is widely used in many areas including sonar, radar and communication system. Many methods have been proposed in revealing the joint time-frequency characteristics of the LFM, such as, short-time Fourier Transform (STFT), Wigner-Ville distribution (WVD) and Radon-Wigner transform (RWT). However, these methods have some drawbacks of low time and frequency resolution, interference of cross terms and limited accuracy. A new method was introduced called Lv’s distribution (LVD) which is powerful tool for LFM signal estimation and detection. LVD is able to provide higher concentration of auto terms compared with WVD and has higher time and frequency resolution than STFT. However, some of the properties are not well studied and thorough comparisons with STFT are needed. This thesis studies the principles of LVD including the derivation and analysis of some of the important properties. And a thorough analysis and comparison of the LVD with STFT are made from both the theoretical and simulation perspectives, where the relationship and differences between the two methods are revealed. Numerical simulations are conducted to verify the theoretical derivations and analysis of LVD. The obtained results by using a multi-component LFM signal show that the parameter estimations of LVD are accurate even under low signal-to-noise ratio (SNR) environment and resolution given by LVD is much higher than STFT. Bachelor of Engineering 2018-05-28T03:21:15Z 2018-05-28T03:21:15Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/75063 en Nanyang Technological University 48 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Cai, Zhemin
Parameter estimation of LFM signals
description Linear frequency modulated (LFM) signal is widely used in many areas including sonar, radar and communication system. Many methods have been proposed in revealing the joint time-frequency characteristics of the LFM, such as, short-time Fourier Transform (STFT), Wigner-Ville distribution (WVD) and Radon-Wigner transform (RWT). However, these methods have some drawbacks of low time and frequency resolution, interference of cross terms and limited accuracy. A new method was introduced called Lv’s distribution (LVD) which is powerful tool for LFM signal estimation and detection. LVD is able to provide higher concentration of auto terms compared with WVD and has higher time and frequency resolution than STFT. However, some of the properties are not well studied and thorough comparisons with STFT are needed. This thesis studies the principles of LVD including the derivation and analysis of some of the important properties. And a thorough analysis and comparison of the LVD with STFT are made from both the theoretical and simulation perspectives, where the relationship and differences between the two methods are revealed. Numerical simulations are conducted to verify the theoretical derivations and analysis of LVD. The obtained results by using a multi-component LFM signal show that the parameter estimations of LVD are accurate even under low signal-to-noise ratio (SNR) environment and resolution given by LVD is much higher than STFT.
author2 Bi Guoan
author_facet Bi Guoan
Cai, Zhemin
format Final Year Project
author Cai, Zhemin
author_sort Cai, Zhemin
title Parameter estimation of LFM signals
title_short Parameter estimation of LFM signals
title_full Parameter estimation of LFM signals
title_fullStr Parameter estimation of LFM signals
title_full_unstemmed Parameter estimation of LFM signals
title_sort parameter estimation of lfm signals
publishDate 2018
url http://hdl.handle.net/10356/75063
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