Environmentally tolerant waveguide‐invariant target depth classification for active sonar.

2010 ◽  
Vol 128 (4) ◽  
pp. 2433-2433
Author(s):  
Ryan Goldhahn ◽  
Peter Nielson ◽  
Jeffrey Krolik
Keyword(s):  
Active Sonar ◽  
Waveguide Invariant ◽  
Depth Classification ◽  
Target Depth

Waveguide invariant minimum variance scatterer depth classification for active sonar.

2010 ◽  
Vol 127 (3) ◽  
pp. 2043-2043
Author(s):  
Ryan Goldhahn ◽  
Granger Hickman ◽  
Jeffrey Krolik
Keyword(s):  
Minimum Variance ◽  
Active Sonar ◽  
Waveguide Invariant ◽  
Depth Classification

Exploiting waveguide invariant reverberation striations for improved active sonar detection and classification

2007 ◽  
Vol 121 (5) ◽  
pp. 3172-3172
Author(s):  
Ryan Goldhahn ◽  
Jeff Rogers ◽  
Granger Hickman ◽  
Jeffrey Krolik
Keyword(s):  
Active Sonar ◽  
Waveguide Invariant

scholarly journals Target-depth estimation in active sonar

2016 ◽  
Vol 139 (4) ◽  
pp. 2052-2052
Author(s):  
Alexis F. Mours ◽  
Nicolas Josso ◽  
Jérôme I. Mars ◽  
Cornel Ioana ◽  
Yves Doisy
Keyword(s):  
Depth Estimation ◽  
Active Sonar ◽  
Target Depth

scholarly journals Performance Analysis of Target Depth Classification Algorithm Based on Sea Experiment Data

2019 ◽  
Vol 2019 ◽  
pp. 1-18
Author(s):  
Anbang Zhao ◽  
Xuejie Bi ◽  
Nansong Li ◽  
Minghui Zhang ◽  
Shengchun Piao
Keyword(s):  
Velocity Profile ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Algorithm Performance ◽  
Sound Velocity Profile ◽  
Depth Classification ◽  
Noise Ratio ◽  
Target Depth ◽  
Negative Gradient ◽  
The Ideal

Cross-spectrum signals can be calculated by the pressure signals. The sign distribution of cross-spectrum active component can be effectively used for target depth classification algorithm. The algorithm is applicable for depth classification of targets where frequencies can only excite the first two normal modes. The corresponding research results are mainly based on the theoretical study. There are few researches on the algorithm performance based on experiment results. To overcome this research lack, based on the effective depth model, the effects on various receiving depth, source frequency, and received signal-to-noise ratio on the algorithm performance have been studied in this paper. The influence of sound velocity profile parameters (negative gradient, thermocline intensity, thermocline thickness, and up-boundary depth) on the algorithm performance has also been researched. According to the simulation results, proper adjustment of the receiving depths can effectively improve the algorithm performance. The source frequency primarily affects the position of the ideal receiving depth which can be appropriately adjusted according to the depth classification requirements of the real sea environment. The algorithm performance improves gradually with the increase of signal-to-noise ratio. Moreover, the algorithm can also be applied under the conditions of negative gradient and thermocline. The comprehensive sound velocity profile parameters have a large impact on the depth classification performance of the algorithm. Even in the case of strong negative gradient or strong thermocline, the robustness of the algorithm is still high. The feasibility of our presented method has been verified by sea experiment. The practical application value of the ideal receiving depth has been researched and validated. The factors affecting the algorithm performance including line spectrum continuity and received signal-to-noise ratio have also been analyzed in our simulation and real sea experiments.

scholarly journals Research on target depth classification in low frequency acoustic field of shallow water

2009 ◽  
Vol 58 (9) ◽  
pp. 6335
Author(s):  
Yu Yun ◽  
Hui Jun-Ying ◽  
Chen Yang ◽  
Sun Guo-Cang ◽  
Teng Chao
Keyword(s):  
Shallow Water ◽  
Acoustic Field ◽  
Low Frequency ◽  
Depth Classification ◽  
Target Depth

scholarly journals Target-depth estimation in active sonar: Cramer–Rao bounds for a bilinear sound-speed profile

2016 ◽  
Vol 140 (3) ◽  
pp. 1771-1782 ◽  
Author(s):  
Alexis Mours ◽  
Cornel Ioana ◽  
Jérôme I. Mars ◽  
Nicolas F. Josso ◽  
Yves Doisy
Keyword(s):  
Sound Speed ◽  
Depth Estimation ◽  
Speed Profile ◽  
Sound Speed Profile ◽  
Active Sonar ◽  
Target Depth
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