Multiple emitter location and signal parameter estimation
Abstract
Processing the signals received on an array of sensors for the location of the emitter is of great enough interest to have been treated under many special case assumptions. The general problem considers sensors with arbitrary locations and arbitrary directional characteristics (gain/phase/polarization) in a noise/interference environment of arbitrary covariance matrix. This report is concerned first with the multiple emitter aspect of this problem and second with the generality of solution. A description is given of the multiple signal classification (MUSIC) algorithm, which provides asymptotically unbiased estimates of 1) number of incident wavefronts present; 2) directions of arrival (DOA) (or emitter locations); 3) strengths and cross correlations among the incident waveforms; 4) noise/interference strength. Examples and comparisons with methods based on maximum likelihood (ML) and maximum entropy (ME), as well as conventional beamforming are included. An example of its use as a multiple frequency estimator operating on time series is included.
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MUSIC avoids the nonlinear optimization process of finding the dipole location.
“An implementation of this approach is MUltiple SIgnal Classification (MUSIC) (37, 38), where the nonlinear optimization process of finding the dipole location is avoided.”
From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [38]
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“Because of this, many well-known sinusoidal parameter estimation algorithms, such as MUSIC (Schmidt, 1986), ESPRIT (Roy et al.”
MethodInfluentialIncremental Averaging Method to Improve Graph-Based Time-Difference-of-Arrival Estimation
Brümann, K., Yamaoka, K., Ono, N., & Doclo, S. (2025) · 2025 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) · doi:10.1109/waspaa66052.2025.11230939
“Several model-based and learning-based methods have been proposed, e.g., methods using time-differences-of-arrival (TDOAs) [1], [6]–[11], the steered response power with phase transform (SRP-PHAT) method [12]– [15], subspace-based methods [16], [17], and deep neural network-based methods [18], [19].”
- Authors
- Schmidt, R.
- Affiliations
- Cytovale (United States) (US)
- Corresponding author
- Schmidt, R.
- ORCID
- Schmidt, R.
- Venue
- IEEE Transactions on Antennas and Propagation
- Published
- 1986-03
- Volume
- 34
- Issue
- 3
- Pages
- 276-280
- ISSN
- 0018-926X (ISSN-L)
- 1558-2221
- OpenAlex
- W2113638573
- Cited by
- 14,718
- References
- 7
- Citations by year
- 2026: 662 · 2025: 869 · 2024: 850 · 2023: 841 · 2022: 746 · 2021: 812 · 2020: 701 · 2019: 816 · 2018: 720 · 2017: 691 · 2016: 613 · 2015: 555 · 2014: 534 · 2013: 489 · 2012: 425
- FWCI
- 81.69
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Antennas and Propagation (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Direction-of-Arrival Estimation Techniques
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Keywords
- direction of arrival estimation, array signal processing, signal parameter estimation, maximum likelihood estimation, maximum entropy estimation, beamforming, time series
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