Detection and parameter estimation of intra-pulse modulated radar signals in complex interference environments+

被引:0
|
作者
Duong, Van Minh [1 ]
Vesely, Jiri [2 ]
Hubacek, Petr [2 ]
Janu, Premysl [2 ]
Tran, Xuan Luong [3 ]
机构
[1] Le Quy Don Tech Univ, Fac Radio Elect Engn, Dept Elect Warfare, 236s Hoang Quoc Viet, Hanoi, Vietnam
[2] Univ Def, Fac Mil Technol, Dept Commun Technol Elect Warfare & Radiolocat, Kounicova 65, Brno, Czech Republic
[3] Air Force Acad, Dept Radar Basics, Air Def, Hanoi, Vietnam
关键词
Probability of detection; Probability of pulse width estimation; Frequency modulation; Phase modulation; Autocorrelation;
D O I
10.1007/s42452-023-05403-x
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The main aim of this review is to describe in detail an advanced technique to detect and estimate the prior unknown parameters of intra-pulse modulated signals and the verification with typical radar signals. The method is approved for detecting parameters successfully, including chirp rate, carrier frequency, and pulse width. The review presents already done research on detecting and estimating single and multi-component linear frequency modulation (LFM) and binary phase shift keying (BPSK) signals in a strong noise environment and studies the technique on one more case, a mixture of LFM and BPSK signals. Firstly, the accuracy of the revised technique is shown in detecting and estimating parameters of a single and multi-component LFM signal in white noise and a mixture of continuous wave signals and noise, or a single BPSK signal in strong white noise. All of them have been done in the existing studies. This method is continuously tested in the second part by detecting a mixture of LFM and BPSK signals and estimating their parameters in intense noise. The tested experimental results demonstrate that the technique can detect single and multi-component realtime LFM signals, single BPSK as well as verification with a mixture of real-time LFM and BPSK signals with SNR = -12 dB is performed. As a result, the technique outperforms the existing detection methods based on machine learning and artificial intelligence.
引用
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页数:18
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