The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Matched subspace detectors
Open accessAbstract
We formulate a general class of problems for detecting subspace signals in subspace interference and broadband noise. We derive the generalized likelihood ratio (GLR) for each problem in the class. We then establish the invariances for the GLR and argue that these are the natural invariances for the problem. In each case, the GLR is a maximal invariant statistic, and the distribution of the maximal invariant statistic is monotone. This means that the GLR test (GLRT) is the uniformly most powerful invariant detector. We illustrate the utility of this finding by solving a number of problems for detecting subspace signals in subspace interference and broadband noise. In each case we give the distribution for the detector and compute performance curves.>
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Cites
(2012) · SpringerReference · doi:10.1007/springerreference_205634 · Cited by 526
Signal processing applications of oblique projection operators
Behrens, R. T., & Scharf, L. L. (1994) · IEEE Transactions on Signal Processing · doi:10.1109/78.286957 · Cited by 476
Self-citationPerformance analysis of a class of transient detection algorithms-a unified framework
Porat, B., & Friedlander, B. (1992) · IEEE Transactions on Signal Processing · doi:10.1109/78.157294 · Cited by 39
Self-citationPerformance analysis of transient detectors based on linear data transforms
Friedlander, B., & Porat, B. (2002) · Asilomar Conference on Signals, Systems & Computers (ACSSC) · doi:10.1109/acssc.1991.186550 · Cited by 7
Self-citation
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Cited by
Hyperspectral remote sensing data analysis and future challenges
Bioucas-Dias, J. M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., & Chanussot, J. (2013) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2013.2244672 · Cited by 2,223
Detection algorithms for hyperspectral imaging applications
Manolakis, D., & Shaw, G. (2002) · IEEE Signal Processing Magazine · doi:10.1109/79.974724 · Cited by 1,116
The detection of low magnitude seismic events using array-based waveform correlation
Gibbons, S. J., & Ringdal, F. (2006) · Geophysical Journal International · doi:10.1111/j.1365-246x.2006.02865.x · Cited by 772
Collaborative representation for hyperspectral anomaly detection
Li, W., & Du, Q. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2343955 · Cited by 758
Anomaly detection from hyperspectral imagery
Stein, D. W. J., Beaven, S. G., Hoff, L. E., Winter, E. M., Schaum, A. P., & Stocker, A. D. (2002) · IEEE Signal Processing Magazine · doi:10.1109/79.974730 · Cited by 687
Metadata
- Authors
- Scharf, L. L., & Friedlander, B.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 1994
- Volume
- 42
- Issue
- 8
- Pages
- 2146-2157
- ISSN
- 1053-587X (ISSN-L)
- OpenAlex
- W2107820823
- Cited by
- 958
- References
- 4
- FWCI
- 10.14
- Type
- journal article
- Language
- en
- Open access
- green
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