Abstract
A wave-based matching-pursuits algorithm is used to parse multi-aspect time-domain backscattering data into its underlying wavefront-resonance constituents, or features. Consequently, the N multi-aspect waveforms under test are mapped into N feature vectors, y/sub n/. Target identification is effected by fusing these N vectors in a maximum-likelihood sense, which we show, under reasonable assumptions, can be implemented via a hidden Markov model (HMM). The algorithm performance is assessed by considering measured acoustic scattering data from five similar submerged elastic targets.
Original language | English (US) |
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Title of host publication | 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258) |
Publisher | IEEE |
Pages | 2115-2118 |
Number of pages | 4 |
Volume | 4 |
ISBN (Print) | 0-7803-5041-3 |
DOIs | |
State | Published - Mar 19 1999 |
Externally published | Yes |
Event | 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258) - Phoenix, AZ, USA Duration: Mar 15 1999 → Mar 19 1999 |
Conference
Conference | 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258) |
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Period | 03/15/99 → 03/19/99 |
Keywords
- Matching pursuit algorithms
- Hidden Markov models
- Acoustic scattering
- Electromagnetic scattering
- Scattering parameters
- Physics
- Dictionaries
- Pursuit algorithms
- Maximum likelihood detection
- Acoustic pulses