Sensor position estimation and source ranging in a shallow water environment
Abstract
Underwater acoustic transient signals are generated mechanically at known positions along a wharf. These signals are received by a wide aperture planar array of four underwater acoustic sensors, whose positions relative to the wharf are unknown. A method is described that enables the positions of the sensors to be estimated from accurate differential time-of-arrival measurements (with 0.1 /spl mu/s precision) as the signal wavefronts traverse the array. A comparison of the estimated positions with the nominal positions of the first three sensors, which form a 20-m-wide aperture horizontal line array, reveals a 2-cm displacement of the middle sensor from the line array axis. This slight bowing of the line array results in overranging (bias error of 3%) when the wavefront curvature method is used with the nominal collinear sensor positions to locate a static source of active sonar transmissions at a range of 59.2 m. The use of the spherical intersection method coupled with the estimated sensor positions of the line array provides an order of magnitude improvement in the range estimate (within 0.3% of the actual value). However, systematic ranging errors are observed when the sound propagation medium becomes nonstationary. Next, the differences in the arrival times of the direct path and boundary-reflected path signals at the middle sensor of the wide aperture line array are estimated using the differential phase residue of the analytic signal at the sensor output. These multipa…
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These conventional methods are also inaccurate in the presence of noise.
“These methods are also inaccurate in the presence of noise [121].”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [121]
Conventional algorithmic approaches to vessel classification and ranging from hydrophone data perform well at close range (<180 m) but their performance degrades quickly at longer ranges.
“Conventional algorithmic approaches to vessel classification and ranging from hydrophone data perform well at close range (<180 m) but their performance degrades quickly at longer ranges.”
From A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance (2022) · cites this paper as [121]
Cites
Discrete time techniques for time delay estimation
Jacovitti, G., & Scarano, G. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.193195 · Cited by 433 · see citing works
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Domingos, L. C. F., Santos, P. E., Skelton, P. S. M., Brinkworth, R. S. A., & Sammut, K. (2022) · Sensors · doi:10.3390/s22062181 · Cited by 93 · see citing works
“These methods are also inaccurate in the presence of noise [121].”
Method
- Authors
- Ferguson, B. G., Lo, K. W., & Thuraisingham, R. A.
- Affiliations
- Defence Science and Technology Group (AU)
- ORCID
- Thuraisingham, R.A.
- Venue
- IEEE Journal of Oceanic Engineering
- Published
- 2005-04
- Volume
- 30
- Issue
- 2
- Pages
- 327-337
- ISSN
- 0364-9059 (ISSN-L)
- OpenAlex
- W2117842897
- Cited by
- 24
- References
- 1
- Citations by year
- 2025: 2 · 2023: 1 · 2022: 3 · 2021: 3 · 2019: 2 · 2018: 1 · 2017: 1 · 2014: 1 · 2013: 3
- FWCI
- 1.43
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal of Oceanic Engineering (publishedVersion)
- Funders
- Department of Science and Technology, Ministry of Science and Technology, India
- Topics
- Physical Sciences › Computer Science › Signal Processing › Direction-of-Arrival Estimation Techniques
- Physical Sciences › Earth and Planetary Sciences › Oceanography › Underwater Acoustics Research
- Physical Sciences › Engineering › Ocean Engineering › Underwater Vehicles and Communication Systems
- Keywords
- shallow water acoustics, line arrays, time of arrival, multipath delay
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