A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

被引：9
作者：
Ben, Guangli [1 ]
Zheng, Xifeng [1 ]
Wang, Yongcheng [1 ]
Zhang, Ning [1 ,2 ]
Zhang, Xin [1 ,2 ]
机构：
[1] Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun 130033, Peoples R China
[2] Univ Chinese Acad Sci, Coll Mat Sci & Optoelect Technol, Beijing 100049, Peoples R China
来源：
APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 02期
关键词：
chirp parameter estimation; signal denoising; time-frequency analysis; maximum likelihood; FRACTIONAL FOURIER-TRANSFORM; LFM SIGNALS; FREQUENCY;
D O I：
10.3390/app11020673
中图分类号：
O6 [化学];
学科分类号：
0703 ;
摘要：
A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.
引用
收藏
页码：1 / 11
页数：11
相关论文
共 38 条
[11]  
Chen XL, 2019, ASIAPAC SIGN INFO PR, P753, DOI 10.1109/APSIPAASC47483.2019.9023016
[12]   Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration [J].
Chen, Yunjin ;
Pock, Thomas .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017, 39 (06) :1256-1272
[13]  
Czarnecki K, 2013, 2013 36TH INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS AND SIGNAL PROCESSING (TSP), P704, DOI 10.1109/TSP.2013.6614028
[14]  
Ding YF, 2016, I C COMM SOFTW NET, P112, DOI 10.1109/ICCSN.2016.7586630
[15]  
Hang H., 2006, P 2006 8 INT C SIGN, DOI [10.1109/ICOSP.2006.344553, DOI 10.1109/ICOSP.2006.344553]
[16]   Identity Mappings in Deep Residual Networks [J].
He, Kaiming ;
Zhang, Xiangyu ;
Ren, Shaoqing ;
Sun, Jian .
COMPUTER VISION - ECCV 2016, PT IV, 2016, 9908 :630-645
[17]   Deep Residual Learning for Image Recognition [J].
He, Kaiming ;
Zhang, Xiangyu ;
Ren, Shaoqing ;
Sun, Jian .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :770-778
[18]  
Jain V., 2009, Advances in Neural Information Processing Systems, V21
[19]   Deep Learning Denoising Based Line Spectral Estimation [J].
Jiang, Yuan ;
Li, Hongbin ;
Rangaswamy, Muralidhar .
IEEE SIGNAL PROCESSING LETTERS, 2019, 26 (11) :1573-1577
[20]  
Kaihui Ding, 2011, 2011 International Conference on Electronic & Mechanical Engineering and Information Technology (EMEIT 2011), P654, DOI 10.1109/EMEIT.2011.6022981
1234