Demo Data from EDBT 2026 / reviewers in the wild / expert
Jacques Munier
dblp:46/10899
· DBLP profile ↗
2ranked-venue papers
1as first author
0since 2021 · last 1989
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer networks
1 paper |
Wireless sensing and localization · 77% Physical-layer communications · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wireless sensing and localization
source localization |
0.0 | 1 | 1987 | Spatial analysis in passive listening using adaptive techniques · Proc. IEEE 1987 |
Physical-layer communications › antenna arrays
adaptive antenna array |
0.0 | 1 | 1987 | Spatial analysis in passive listening using adaptive techniques · Proc. IEEE 1987 |
Methods — techniques the papers use, named apart from their topics
matched filter · 0.0cross-spectral matrix eigenvalue reduction · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1989 | Source localization using a distorted antennaabstractA method is proposed for estimating both the outline of a distorted array antenna and the bearings of some incident plane waves. This is possible if locations are known for a number of sensors equal to the number of remote sources present. Using an operator named the propagator, which can be extracted adaptively from the data, the shape of the whole antenna can be restored, and thus the usual superresolutive direction-finding algorithms can be applied efficiently. Owing to the adaptive character of the method, the case of time-varying antenna shape and/or moving sources can also be handled. This method is particularly useful when the number of sensors is much greater than the number of sources, and it imposes only a low computational burden.> Sylvie Marcos, Jacques Munier |
ICASSP | 2 |
| 1987 | Spatial analysis in passive listening using adaptive techniquesabstractThe various known methods intended for continuous analysis of uncorrelated source fields (MEM-AR, Capon, Borgiotti-Lagunas, Bienvenu-Schmidt) are shown to be based upon a unique conventional diagram, namely one of a spatial linear multiport filter followed by a variance estimator. This filter has K inputs corresponding to K sensors and a single output, and acts as a scalar product system. A fundamental property is then the possibility of having zero responses for the signals coming from K - 1 directions. Using the classical matched filter principle for minimization of undesirable signals (when looking in a given direction), a general class of data-adaptive spatial filters, from which all the above-mentioned methods can be derived with specific choices of parameters, is defined. These filters can be further submitted to normalizing constraints; two examples of this are discussed. This new interpretation leads to the consideration of the above-mentioned methods as special applications of the well-known adaptive array techniques to the estimation problem. Moreover, the asymptotic behaviors are examined when a noise subtraction technique based upon the reduction of the smallest cross-spectral matrix eigenvalue is applied. The convergence toward the Pisarenko solution is ascertained for the estimated directions of the sources, whereas it is not for their estimated intensities (except in the case of the Capon method). In the case of partially correlated sources, the above-mentioned methods are still valid for the localization of sources, but not for their intensities. Other appropriate techniques, such as those of Schmidt, Wax, Shan, and Kailath, must then be considered. Jacques Munier, Gilles Y. Delisle |
Proc. IEEE | 1 |