Simulation of Depth Resolution for MFP Passive Detection

Luo, Xin; Han, Qing; Zhou, De Fu; Li, Jian; Yin, Cheng; Fan, Hong Hui; Zhu, Chang Ping

doi:10.4028/www.scientific.net/amm.742.136

Cited by 1 publication

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“…Target depth classification was achieved using a waveguide invariant adaptive matched filter by Goldhahn [6]. There are various researches about target depth estimation on the basis of matched field processing [7][8][9][10][11][12]. Premus and Backman [13] proposed a matched subspace [14] approach for depth discrimination.…”

Section: Introductionmentioning

confidence: 99%

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

Zhao

et al. 2019

Journal of Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

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

Zhao

et al. 2019

Journal of Sensors

View full text Add to dashboard Cite

show abstract

Simulation of Depth Resolution for MFP Passive Detection

Cited by 1 publication

References 2 publications

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

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

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