Detection and Parameter Estimation for Chirp Signal Based on Fractional Fourier Transform

2014 ◽  
Vol 599-601 ◽  
pp. 1474-1477
Author(s):  
Xin Chen ◽  
Min Tao ◽  
Tian Tang Pan ◽  
Yan Li
Keyword(s):  
Parameter Estimation ◽  
Fractional Fourier Transform ◽  
Estimation Algorithm ◽  
Wave Parameter ◽  
Estimation Accuracy ◽  
Radar Signal ◽  
Chirp Signal ◽  
Computation Complexity ◽  
Maximum Value ◽  
Radar Echo

The Chirp signal has been used widely in radar signal, radar echo wave can established to be Chirp model. The estimation of radar echo wave parameter is a important task in radar signal processing. In this paper, we introduced three theories and algorithms of detection and estimation of Chirp signal: 2D peak searching algorithm, two steps searching of maximum value algorithm and pre-estimation algorithm firstly. The parameter estimation precision and computation complexity in low SNR was simulated for these three algorithms. The final simulation indicate that the two steps searching algorithm of maximum value take on nice estimation accuracy and low computation complexity in contrast.

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.

Analysis of Parameter Estimation Using Pei Algorithm of FRFT

2014 ◽  
Vol 577 ◽  
pp. 758-761
Author(s):  
Bing Deng
Keyword(s):  
Fourier Transform ◽  
Parameter Estimation ◽  
Fractional Fourier Transform ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Factors Influencing

Parameter estimation of chirp signal is analyzed using Pei algorithm of FRFT (Fractional Fourier Transform). Firstly, the model of parameter estimation has been made. Secondly, the factors influencing the estimation accuracy have been analyzed. Finally, the simulation has been made to verify the conclusions.

scholarly journals A Frequency-Time Algorithm of Parameter Estimation for Sinusoidal Signal in Noise

2021 ◽  
Vol 21 (1) ◽  
pp. 33-38
Author(s):  
Peng Chen ◽  
Qin Chen ◽  
Zhijun Xie ◽  
Xiaohui Chen ◽  
Shaomei Zhao
Keyword(s):  
Parameter Estimation ◽  
Time Domain ◽  
Initial Phase ◽  
Linear Prediction ◽  
Estimation Algorithm ◽  
Time Algorithm ◽  
Sinusoidal Signal ◽  
Estimation Accuracy ◽  
Computationally Efficient ◽  
Parameter Estimation Algorithm

Abstract In this paper, a computationally efficient and high precision parameter estimation algorithm with frequency-time combination is proposed to improve the estimation performance for sinusoidal signal in noise, which takes the advantages of frequency- and time-domain algorithms. The noise influence is suppressed through spectrum analysis to get coarse frequency, and the fine frequency is obtained by denoising filtering and using linear prediction property. Then, estimation values of the amplitude and initial phase are obtained. The numerical results indicate that the proposed algorithm makes up for the shortcomings of frequency- and time-domain algorithms and improves the anti-interference performance and parameter estimation accuracy for sinusoidal signal.

A Fast and Efficient Recursive Parameter Estimation Algorithm in Time Series Analysis

2015 ◽  
Vol 738-739 ◽  
pp. 423-429
Author(s):  
Jian Jun Zhang
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Time Series Analysis ◽  
Time Complexity ◽  
Computation Time ◽  
Estimation Algorithm ◽  
Theoretical Research ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Series Analysis

High calculation precision and speed of the model parameter estimation has become the theoretical research emphasis and the key link of the applications of the time series analysis based methods. Aiming at the problem that some of the previous parameter estimation methods exist the weakness of stronger constraints, higher time complexity, lower precision of the whole recurrence process and insufficient online diagnosis power, this paper proposes an approach which repeatedly uses the auto-covariance function and the autocorrelation function throughout the recurrent process while guaranteeing not to increase the time complexity of the proposed algorithm and, hence improve the calculation speed and accuracy of parameter estimation simultaneously. As compared to related work, it has lower time complexity, shorter computation time and higher parameter estimation accuracy. The fault diagnosis steps based on the proposed parameter estimation approach are also suggested.

scholarly journals Compressed Sensing Reconstruction of Radar Echo Signal Based on Fractional Fourier Transform and Improved Fast Iterative Shrinkage-Thresholding Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Rui Zhang ◽  
Chen Meng ◽  
Cheng Wang ◽  
Qiang Wang
Keyword(s):  
Fourier Transform ◽  
Compressed Sensing ◽  
Fractional Fourier Transform ◽  
Radar Signal ◽  
Reconstruction Algorithms ◽  
Echo Signal ◽  
Time Frequency ◽  
Iterative Shrinkage ◽  
Radar Echo ◽  
Iterative Shrinkage Thresholding

The compressed sensing theory, which has received great attention in the field of radar technology, can effectively reduce the data rate of high-resolution radar imaging systems and solve the problem of collecting, storing, and transmitting large amounts of data in radar systems. Through the study of radar signal processing theory, it can be found that the echo of radar LFM transmit signal has sparse characteristics in the distance upward; based on this, we can consider using the theory of compressed sensing in the processing of radar echo to optimize the processing. In this paper, a fast iterative shrinkage-thresholding reconstruction algorithm based on protection coefficients is proposed. Under the new scheme, firstly, the LFM echo signal’s good sparse representation is obtained by using the time-frequency sparse characteristics of the LFM echo signal under the fractional Fourier transform; all reconstruction coefficients are analyzed in the iterative process. Then, the coefficients related to the feature will be protected from threshold shrinkage to reduce information loss. Finally, the effectiveness of the proposed method is verified through simulation experiments and application example analysis. The experimental results show that the reconstruction error of this method is lower and the reconstruction effect is better compared with the existing reconstruction algorithms.

Detection of Chirp Signal with Time-Varying Amplitude Based on FRFT

2014 ◽  
Vol 989-994 ◽  
pp. 4046-4049
Author(s):  
Yan Jun Wu ◽  
Ren Long Li ◽  
Xiao Wang
Keyword(s):  
Parameter Estimation ◽  
Fractional Fourier Transform ◽  
Modulation Frequency ◽  
Gaussian Function ◽  
Angular Frequency ◽  
Estimation Methods ◽  
Time Varying ◽  
Chirp Signal ◽  
The Mean ◽  
General Method

The general method time-varying amplitude linear FM signal parameter estimation, the proposed parameter fractional Fourier transform for time-varying estimates of the magnitude of the chirp signal, and the related issues of a more in-depth research. Study the time-varying amplitude of the initial phase chirp signal, the initial angular frequency, modulation frequency and amplitude information extraction and estimation methods, and the magnitude of the Gaussian function varies with the magnitude of random variation and chirp signal for the object properties (parameters on parameter estimation estimate the mean square error) were simulated.

scholarly journals Time-Domain Joint Parameter Estimation of Chirp Signal Based on SVR

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Xueqian Liu ◽  
Hongyi Yu
Keyword(s):  
Parameter Estimation ◽  
Instantaneous Frequency ◽  
Initial Phase ◽  
Estimation Algorithm ◽  
Phase Unwrapping ◽  
Joint Estimation ◽  
Support Vector ◽  
Chirp Signal ◽  
Low Frequencies ◽  
The Impact

Parameter estimation of chirp signal, such as instantaneous frequency (IF), instantaneous frequency rate (IFR), and initial phase (IP), arises in many applications of signal processing. During the phase-based parameter estimation, a phase unwrapping process is needed to recover the phase information correctly and impact the estimation performance remarkably. Therefore, we introduce support vector regression (SVR) to predict the variation trend of instantaneous phase and unwrap phases efficiently. Even though with that being the case, errors still exist in phase unwrapping process because of its ambiguous phase characteristic. Furthermore, we propose an SVR-based joint estimation algorithm and make it immune to these error phases by means of setting the SVR's parameters properly. Our results show that, compared with the other three algorithms of chirp signal, not only does the proposed one maintain quality capabilities at low frequencies, but also improves accuracy at high frequencies and decreases the impact with the initial phase.

scholarly journals Parameter Estimation Algorithm for the Exponential Signal by the Enhanced DFT Approach

2014 ◽  
Vol 14 (3) ◽  
pp. 126-135 ◽  
Author(s):  
Qian Wang ◽  
Xiao Yan ◽  
Kaiyu Qin
Keyword(s):  
Parameter Estimation ◽  
Signal Sequence ◽  
Estimation Algorithm ◽  
Estimation Accuracy ◽  
Spectral Leakage ◽  
Parameter Estimation Algorithm ◽  
Signal Sample ◽  
Simulation Results ◽  
Sample Points

Abstract Based on enhanced interpolation DFT, a novel parameter estimation algorithm for the exponential signal is presented. The proposed two-step solution consists of a preprocessing unit which constructs a new signal sequence by continuously cycle shifting sample points and summing up N buffered exponential signal sample sequences, then an interpolation DFT engine to obtain accurate parameter estimation of the exponential signal based on the signal sequence generated by the preprocessing unit. Compared to previous works, owing to the combination of the pretreatment and the interpolation DFT analysis, the tedious iteration for spectral leakage elimination can be removed, and remarkable improvements are achieved in terms of estimation accuracy and speed. The simulation results verify the effectiveness of the proposed algorithm.

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