Detection Performance Analysis of Multistatic Sonar System Based on Cumulative Detection

Zhou, Ziling; Erzheng, Fang; Xuanzi, Yin; Gui, Chenyang

doi:10.1109/coa50123.2021.9520022

Cited by 1 publication

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“…Step 3 Getting Possible Target Set Points: In this step, the source and receiver are placed at a known fixed point. According to the practical MSSN channel model, as specified in (13), the set of possible target points for boundary values where the SE function is equal to zero as a reference cookie-cutter sensor model is obtained by the brute force method [66]. Pings are considered to be detected if the signal level listened by a receiver is above the detection threshold level.…”

Section: Figure 7 Frequency Dependent Ocean Ambient Noisementioning

confidence: 99%

Three Dimensional Volume Coverage in Multistatic Sonar Sensor Networks

2022

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Recent changes in the design of enemy threats such as submarines and the technological achievements in sensor development have paved the way for multistatic sonar applications, which increase security and situational awareness in underwater tactical operations. Previously, coverage in multistatic sonar sensor networks (MSSN) was studied using Cassini ovals and the traditional sonar detection model in two dimensions without any discussion of the practicability and feasibility in terms of conditions related to the underwater acoustic propagation environment. In this study, a practical three-dimensional MSSN channel model is proposed. The proposed model covers a spectral variation of absorption loss, ambient noise, sound speed profile, and shadow zones. The realistic effects of sound propagation and environmental conditions are modeled and evaluated using Lybin, which is a well-known sonar performance prediction tool. Using the practical MSSN channel model, the number of source-receiver pairs required to cover a three-dimensional MSSN volume is calculated. The impacts of frequency, source-to-receiver distance, and source level are investigated. The results are compared to the Cassini oval and traditional sonar detection models via an error expression derived according to our verified practical model. The results reveal that the inclusion of ambient conditions and sound propagation characteristics in the channel model leads to huge error levels of 4700000% in the Cassini oval model and 170000% in the traditional sonar detection model, depending on frequency. Thus, the applicability of these models in realistic MSSN deployment scenarios is limited.

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Section: Figure 7 Frequency Dependent Ocean Ambient Noisementioning

confidence: 99%