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Demonstration of spiral wave front sonar for active localization
Benjamin R Dzikowicz1, James F Tressler1, David A Brown2
1Naval Research Laboratory, Physical Acoustics Branch, Code 7136, 4555 Overlook Avenue Southwest, Washington, DC 20375, USA.
This study introduces spiral wave front sonar, a novel active sonar technique for precise remote target localization. The system achieves accurate azimuthal aspect determination and offers spectral data for target classification.
Area of Science:
- Acoustics
- Signal Processing
- Sonar Technology
Background:
- Active sonar systems are crucial for remote target localization.
- Non-imaging sonar techniques offer unique advantages in specific applications.
- Accurate azimuthal localization remains a challenge in sonar technology.
Purpose of the Study:
- To introduce and evaluate a novel spiral wave front sonar technique.
- To assess the system's capability for remote target localization and azimuthal aspect determination.
- To explore the potential of spiral sonar for target classification using spectral response.
Main Methods:
- Development and testing of two prototype spiral sonar systems.
- Utilizing a spiral signal with phase variation across the azimuthal plane.
- Measuring range via time-of-flight and azimuthal aspect via phase difference across frequencies.
- Laboratory experiments involving tracking fixed targets through 360° rotations.
Main Results:
- The first prototype demonstrated aspect errors less than 20° under optimal conditions.
- The second prototype, using higher frequency transducers, achieved aspect errors less than 10° after calibration.
- The technique provides both range and azimuthal information, along with target spectral response.
Conclusions:
- Spiral wave front sonar is a viable technique for remote target localization with accurate azimuthal aspect determination.
- The system's performance is promising, with potential for improvement through system design and calibration.
- The availability of spectral response data opens possibilities for advanced target classification algorithms.
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