scholarly journals An experimental evaluation of the generalized sinusoidal frequency modulated waveform for active sonar systems

2019 ◽  
Vol 145 (6) ◽  
pp. 3741-3755 ◽  
Author(s):  
David A. Hague ◽  
John R. Buck
2015 ◽  
Vol 51 (2) ◽  
pp. 894-909 ◽  
Author(s):  
Luzhou Xu ◽  
Jian Li ◽  
Akshay Jain
Keyword(s):  

2008 ◽  
Vol 44 (4) ◽  
pp. 1371-1380 ◽  
Author(s):  
Suhwan Kim ◽  
Bonhwa Ku ◽  
Wooyoung Hong ◽  
Hanseok Ko

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2794 ◽  
Author(s):  
Shuxia Huang ◽  
Shiliang Fang ◽  
Ning Han

In active sonar systems, the target echoes are usually equivalent to a superposition of the Doppler-scaled reflections from multiple highlights. The reflections overlap with each other both in the time and frequency domain, which results in a decreased velocity estimation performance. Recently, the hyperbolic-frequency modulated signal has been widely employed in sonar systems for moving targets due to its Doppler tolerance, while the precise velocity estimation becomes a great challenge under such conditions. In this paper, the echo c is modeled onsidering a target with a constant velocity and multi-highlights. The velocity estimation performance is analyzed though the signal’s matched filter and the wideband ambiguity function. An improved method based on the sliding window matching algorithm is proposed to improve the performance. The method controls the energy of environmental noise and interference by focusing on the dominant target highlight, and applying a designed window which utilizes the Doppler characteristics of hyperbolic-frequency modulated signals. Simulations and lake experiment allow us to compare between the improved method and the conventional matched filter method. The results verify the influence of the multi-highlights in velocity estimation and indicate that the improved method has more effective performance.


Author(s):  
Woo-Sung Son ◽  
Young Kwang Seo ◽  
Wan-Jin Kim ◽  
Hyoung-Nam Kim

2006 ◽  
Vol 120 (5) ◽  
pp. 3221-3221
Author(s):  
Lisa Zurk ◽  
Jorge Quijano ◽  
Manish Velankar ◽  
Dan Rouseff
Keyword(s):  

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