The DOI matches its Crossref record.
Improving the resolution performance of eigenstructure-based direction-finding algorithms
Abstract
Recently there has been much interest in algorithms which form a direction-finding spectrum based on the eigenstructure of the sensor covariance matrix. These algorithms are attractive because of their ability to achieve Cramer-Rao direction-finding accuracy bounds for closely spaced emitters, provided the available signal-to-noise (SNR) is high enough to resolve two distinct peaks in the estimated spectrum. In this paper we present several methods for reducing the SNR required for resolution. The first method involves examining the roots of the spectrum polynomial. This technique is applicable when a uniformly spaced sensor array is in use. The second method uses the properties of the so-called signal-space eigenvectors to define a rational (pole-zero) spectrum function with improved resolution capabilities.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
A sparse signal reconstruction perspective for source localization with sensor arrays
Malioutov, D., Cetin, M., & Willsky, A. S. (2005) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2005.850882 · Cited by 2,638
An introduction to multisensor data fusion
Hall, D. L., & Llinas, J. (1997) · Proceedings of the IEEE · doi:10.1109/5.554205 · Cited by 2,629
Performance analysis of Root-Music
Rao, B. D., & Hari, K. V. S. (1989) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/29.45540 · Cited by 992
Subspace-based signal analysis using singular value decomposition
Van Der Veen, A.-J., Deprettere, E. F., & Swindlehurst, A. L. (1993) · Proceedings of the IEEE · doi:10.1109/5.237536 · Cited by 348
Metadata
- Authors
- Barabell, A.
- Venue
- ICASSP '83. IEEE International Conference on Acoustics, Speech, and Signal Processing
- Published
- 2005-03-24
- Volume
- 8
- Pages
- 336-339
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W1952629836
- Cited by
- 1,030
- References
- 0
- FWCI
- 41.12
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Improving the resolution performance of eigenstructure-based direction-finding algorithms. https://app.cochat.ai/papers/10.1109/icassp.1983.1172124
- The paper
- Barabell, A. (2005). Improving the resolution performance of eigenstructure-based direction-finding algorithms. ICASSP '83. IEEE International Conference on Acoustics, Speech, and Signal Processing. https://doi.org/10.1109/icassp.1983.1172124
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
