A Study on the Feasibility of Low Probability of Intercept Sonar

Open Access
- Author:
- Park, Joonho Daniel
- Graduate Program:
- Electrical Engineering
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- November 11, 2009
- Committee Members:
- David Jonathan Miller, Thesis Advisor/Co-Advisor
David Jonathan Miller, Thesis Advisor/Co-Advisor
John F Doherty, Thesis Advisor/Co-Advisor - Keywords:
- Low Probability of Intercept
Sonar
Detection
Range Estimation - Abstract:
- The feasibility of low probability of intercept for sonar signals is explored. Using a noise-like active sonar signal, the transmitter (platform) employs a matched filter for echo detection while the target is assumed to use an energy detector. Decision statistic distributions are developed at both the platform and target. These distributions allow efficient Monte Carlo simulation of detection performance and comparison with a previous work’s assumption of Gaussian decision statistics. We then explore the detection advantage the platform can achieve by evasive on-off keying and by optimization of its transmitted power. A favorable (evasive) operating region of the platform in the (low power, small range) region of the (range, power) plane is identified. This suggests that the platform should start detection (and range-finding) using a low-power probing signal, increasing power until a reliable detection rate is first achieved while ensuring the target’s detection rate does not exceed a specified level. Using a detailed waveform-based simulation framework, the second part of the feasibility study using LPI sonar signal is about covert range estimation. A frequency selective channel filter is used for a more realistic simulation. As in the first part, the platform uses matched filtering and the target uses energy detection. However, the objective of the platform is to estimate the range to the target while ensuring that the target still fails to detect the platform’s pinging LPI waveform. A platform-target encounter scenario was designed for Monte Carlo simulation. Characterization of variables in the model was performed to show the feasible conditions of covert range estimation.