DRFM range false target cancellation method based on slope-varying LFM chirp signal

2016 ◽  
Author(s):  
Li Wei ◽  
Yang Xiaopeng ◽  
Zhou Chao ◽  
Li Shuai ◽  
Ning Liyue
Keyword(s):  
Chirp Signal ◽  
False Target

Preceded False Target Groups Jamming Against LFM Pulse Compression Radars

2011 ◽  
Vol 30 (6) ◽  
pp. 1350-1353 ◽  
Author(s):  
Jian-cheng Liu ◽  
Xue-song Wang ◽  
Zhong Liu ◽  
Jian-hua Yang ◽  
Guo-yu Wang
Keyword(s):  
Pulse Compression ◽  
Target Groups ◽  
False Target

scholarly journals A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

2021 ◽  
Vol 11 (2) ◽  
pp. 673
Author(s):  
Guangli Ben ◽  
Xifeng Zheng ◽  
Yongcheng Wang ◽  
Ning Zhang ◽  
Xin Zhang
Keyword(s):  
Maximum Likelihood ◽  
Local Search ◽  
Signal To Noise Ratio ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Estimation Of Parameters ◽  
Parameter Estimator ◽  
Denoising Method ◽  
Time Frequency ◽  
Approximate Estimation

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.

A Target Selection Algorithm of Seeker Based on the Prior Information and SVM

2013 ◽  
Vol 734-737 ◽  
pp. 3071-3074
Author(s):  
Guo Dong Zhang ◽  
Zhong Liu
Keyword(s):  
Prior Information ◽  
Simulation Experiment ◽  
Target Selection ◽  
Support Vector ◽  
Selection Algorithm ◽  
Distribution Law ◽  
Surface Ship ◽  
False Target ◽  
Target Characteristics ◽  
Selection Of

Aiming at the phenomenon that the chaff and corner reflector released by surface ship can influence the selection of missile seeker, this paper proposed a multi-target selection method based on the prior information of false targets distribution and Support Vector Machine (SVM). By analyzing the false targets distribution law we obtain two classification principles, which are used to train the SVM studies the true and false target characteristics. The trained SVM is applied to the seeker in the target selection. This method has advantages of simple programming and high classification accuracy, and the simulation experiment in this paper confirms the correctness and effectiveness of this method.

ISAR imaging via sparse frequency-stepped chirp signal

2011 ◽  
Vol 55 (4) ◽  
pp. 877-888 ◽  
Author(s):  
HongXian Wang ◽  
Yi Liang ◽  
MengDao Xing ◽  
ShouHong Zhang
Keyword(s):  
Chirp Signal ◽  
Isar Imaging
Sign in / Sign up

Export Citation Format

Share Document