Performance Analysis on Three Methods for Chirp Signal Parameters Estimation Based on FRFT

2014 ◽  
Vol 989-994 ◽  
pp. 3942-3945 ◽  
Author(s):  
Yan Jun Wu ◽  
Gang Fu ◽  
Peng Yu
Keyword(s):  
Fractional Fourier Transform ◽  
Operation Time ◽  
Parameters Estimation ◽  
Chirp Signal ◽  
Central Frequency ◽  
Signal Energy ◽  
Low Snr ◽  
Detection Algorithms ◽  
Modulation Rate ◽  
Searching Method

Chirp signal has been used widely in radar signals, and the Fractional Fourier transform is one of the most effective tools to analyze Chirp signal. In this paper, the concept of FRFT and the estimation theory of Chirp signal are introduced firstly. Then, we study three Chirp signal detection algorithms based on character of Chirp signal energy concentrated in a certain FRFT domain. Finally, in order to test the estimation abilities of the frequency modulation rate and the central frequency of FRFT to Chirp signal, and compare the operation time of parameters estimation under different SNR of the three algorithms, we simulate performance of the Three methods, and the final simulation results show that the three method have remarkable capabilities of detecting Chirp signal with low SNR. In contrast, the two-searching method doesn’t need planar search, consumedly reducing the computation cost at the same precision.

Parameters Estimation and Waveform Reconstruction of Chirp Signal Based on FRFT

2014 ◽  
Vol 989-994 ◽  
pp. 3993-3996 ◽  
Author(s):  
Yan Jun Wu ◽  
Gang Fu ◽  
Fei Liu
Keyword(s):  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Signal Reconstruction ◽  
Parameters Estimation ◽  
The Other ◽  
Fourier Domain ◽  
Chirp Signal ◽  
The Fourier Transform ◽  
Definition Of ◽  
Noisy Background

The fractional Fourier transform (FRFT) is a generalization of the Fourier transform. The article first introduces the definition of FRFT transformation; then analyzed FRFT Chirp signal based on this humble proposed restoration Chirp signal in a noisy background in two ways: one is based on parameter estimation, and the other is based on the scores Fourier domain filtering to achieve signal reconstruction; Finally, simulation verify the feasibility of the above analysis.

Performance Analysis of LFM Signal Parameter Estimation through FrFT Transform

2012 ◽  
Vol 195-196 ◽  
pp. 80-83
Author(s):  
Ze Zhong Wang ◽  
Yu Huang ◽  
Feng Liu
Keyword(s):  
Fourier Transform ◽  
Parameter Estimation ◽  
Fractional Fourier Transform ◽  
Frequency Estimation ◽  
Parameters Estimation ◽  
Chirp Rate ◽  
Signal Parameter ◽  
Central Frequency ◽  
Simulation Results ◽  
Radar Signals

The fractional Fourier transform (FrFT) used to process linearly frequency modulated (LFM) signals is applied in the differentiation and parameters estimation of LFM radar signals. The performance of the LFM signals detection and parameters estimation through FrFT are studied when the signal-noise ratio (SNR) is low. The situation is set and the demonstration routine through simulations is made so that the performance on the chirp rate estimation and the central frequency estimation of LFM signals through FrFT is tested, the time used to obtain parameter estimation results through computer simulations is compared. The simulation results demonstrate that the method through FrFT could be used to differentiate LFM radar signals and to estimate parameters of LFM radar signals when the SNR is low.

scholarly journals Voice Activity Detection for Speech Enhancement Applications

10.14311/1251 ◽  
2010 ◽  
Vol 50 (4) ◽  
Author(s):  
E. Verteletskaya ◽  
K. Sakhnov
Keyword(s):  
Background Noise ◽  
Voice Activity Detection ◽  
Energy Ratio ◽  
Activity Detection ◽  
Noisy Environment ◽  
Signal Energy ◽  
Low Snr ◽  
Noise Robust ◽  
Voice Activity ◽  
Band Signal

This paper describes a study of noise-robust voice activity detection (VAD) utilizing the periodicity of the signal, full band signal energy and high band to low band signal energy ratio. Conventional VADs are sensitive to a variably noisy environment especially with low SNR, and also result in cutting off unvoiced regions of speech as well as random oscillating of output VAD decisions. To overcome these problems, the proposed algorithm first identifies voiced regions of speech and then differentiates unvoiced regions from silence or background noise using the energy ratio and total signal energy. The performance of the proposed VAD algorithm is tested on real speech signals. Comparisons confirm that the proposed VAD algorithm outperforms the conventional VAD algorithms, especially in the presence of background noise.

Study of Intelligent Optimization Methods Applied in the Fractional Fourier Transform

2012 ◽  
Vol 461 ◽  
pp. 323-328
Author(s):  
Han Qing Wang ◽  
Ping Bo Wang ◽  
Shu Zong Wang ◽  
Mao Lin Li
Keyword(s):  
Fourier Transform ◽  
Ant Colony Algorithm ◽  
Fractional Fourier Transform ◽  
Optimization Methods ◽  
Optimization Method ◽  
Extremum Seeking ◽  
Intelligent Optimization ◽  
Chaos Optimization ◽  
Computation Efficiency ◽  
Searching Method

In order to overcome the inefficiency shortcoming of traditional step-based searching method for extremum seeking in two-dimensional fractional Fourier domain, some typical intelligent optimization methods such as genetic algorithms, continuous ant colony algorithm, particle swarm optimization and chaos optimization method are introduced and applied successfully in fractional Fourier transform. The performances of the global optimization methods are compared with step-based method based on simulation. Results show that the COA optimization algorithm is much more preferable considering computation efficiency, precision and resolution in all the above mentioned optimization methods

scholarly journals Chirp Signal Denoising Based on Convolution Neural Network

2020 ◽  
Author(s):  
Ben Guangli ◽  
Xifeng Zheng ◽  
Yongcheng Wang ◽  
Xin Zhang ◽  
Ning Zhang
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Estimation Algorithm ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Signal Processing Algorithm ◽  
Low Snr ◽  
Result Show ◽  
Parameter Estimation Algorithm ◽  
Two Parameter

Abstract Many classical chirp signal processing algorithm may experience distinct performance decrease in noise circumstance. To address the problem, this paper proposes a deep learning based approach to filter noises in time domain. The proposed denoising convolutional neural network (DCNN) is trained to recover the original clean chirps from observation signals with noises. Following denosing, we employ two parameter estimation algorithm to DCNN output. Simulation result show that the proposed DCNN method improves the signal noise ratio (SNR) and parameter estimation accuracy to a great extent compared to the signals without denoising. And DCNN have a strong adaptability of low SNR input scenarios that never trained.

scholarly journals Parameters Estimation of LFMCW Signals Based on Periodic Fractional Fourier Transform

2014 ◽  
Vol 8 (5) ◽  
pp. 143
Author(s):  
Ying Liu ◽  
Dianren Chen ◽  
Lei Chen
Keyword(s):  
Fourier Transform ◽  
Continuous Wave ◽  
Fractional Fourier Transform ◽  
Estimation Method ◽  
Observation Time ◽  
Parameters Estimation ◽  
Estimation Accuracy ◽  
True Parameter ◽  
Wave Radar ◽  
Output Snr

When using fractional Fourier transform (FrFT) to detect and estimate linear frequency-modulated continuous wave radar signals, two problems will appear: multiple peaks occur in FRFT image and the output SNR at the true parameter values does not increase when the observation time is longer than the signals period. A multiple period LFMCW signals parameters estimation method based on period FRFT (PFRFT) is studied in this paper. The PFRFT formula of multiple period LFMCW signals is given. The relationship among PFRFT output SNR, observation time and sample signals SNR is analyzed. The estimation accuracy formula of PFRFT is derived. At last, numerical simulation shows the effectiveness of the algorithm and PFRFT is superior to FRFT for estimating a multiple periods LFMCW signals.  

A New Kind of FRFT Analysis Method for Multi-Component LFM Signals

2011 ◽  
Vol 204-210 ◽  
pp. 973-978
Author(s):  
Qiang Guo ◽  
Ya Jun Li ◽  
Chang Hong Wang
Keyword(s):  
Feature Extraction ◽  
Frequency Analysis ◽  
Signal Analysis ◽  
Fractional Fourier Transform ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Low Snr ◽  
The Time Domain ◽  
Signal Analysis Method

To effectively detect and recognize multi-component Linear Frequency-Modulated (LFM) emitter signals, a multi-component LFM emitter signal analysis method based on the complex Independent Component Analysis(ICA) which was combined with the Fractional Fourier Transform(FRFT) was proposed. The idea which was adopted to this method was the time-domain separation and then time-frequency analysis, and in the low SNR cases, the problem which is generally plagued by noised of feature extraction of multi-component LFM signal based on FRFT is overcame. Compared to the traditional method of time-frequency analysis, the computer simulation results show that the proposed method for the multi-component LFM signal separation and feature extraction was better.

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