The DOI matches its Crossref record.
Source separation using higher order moments
Abstract
The author presents a simple algebraic method for the extraction of independent components in multidimensional data. Since statistical independence is a much stronger property than uncorrelation, it is possible, using higher-order moments, to identify source signatures in array data without any a priori model for propagation or reception, that is, without directional vector parameterization, provided that the emitting sources are independent with different probability distributions. The author proposes such a blind identification procedure. Source signatures are directly identified as covariance eigenvectors after data have been orthonormalized and nonlinearly weighted. Potential applications to array processing are illustrated by a simulation consisting of a simultaneous range-bearing estimation with a passive array.>
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
Blind beamforming for non-gaussian signals
Cardoso, J. F., & Souloumiac, A. (1993) · IEE Proceedings F Radar and Signal Processing · doi:10.1049/ip-f-2.1993.0054 · Cited by 2,738
Self-citationNatural Image Statistics and Neural Representation
Simoncelli, E. P., & Olshausen, B. A. (2001) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.24.1.1193 · Cited by 2,460
Ossadtchi, A., Baillet, S., Mosher, J. C., Thyerlei, D., Sutherling, W., & Leahy, R. M. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2003.10.036 · Cited by 104
Metadata
- Authors
- Cardoso, J.-F.
- Venue
- International Conference on Acoustics, Speech, and Signal Processing
- Publisher
- IEEE
- Published
- 2003-01-13
- Pages
- 2109-2112
- OpenAlex
- W1533423434
- Cited by
- 556
- References
- 0
- FWCI
- 33.36
- 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: Source separation using higher order moments. https://app.cochat.ai/papers/10.1109/icassp.1989.266878
- The paper
- Cardoso, J.-F. (2003). Source separation using higher order moments. International Conference on Acoustics, Speech, and Signal Processing. https://doi.org/10.1109/icassp.1989.266878
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.
