Adaptive subspace detectors
By Shawn Kraut, Louis L. Scharf, L. McWhorter. IEEE Transactions on Signal Processing, 2001
749 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Artificial Intelligence, Signal Processing
View Paper: https://www.semanticscholar.org/paper/41f984ca015b27d939f6bd383c49df1149753fa4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Luís Pádua
- Raul Morais
- Nirmal Keshava
- Jonás Hruska
- Braham Himed
- José Elievam Bessa
- Hongbin Li
- William L. Melvin
In Artificial Intelligence
- Luís Pádua
- Raul Morais
- Nirmal Keshava
- Yi Chen
- Jonás Hruska
- Trac Duy Tran
- Braham Himed
- José Elievam Bessa
In Signal Processing
- Nirmal Keshava
- Yi Chen
- Trac Duy Tran
- Braham Himed
- Hongbin Li
- William L. Melvin
- Kwang June Sohn
- Nasser M. Nasrabadi
Citations in the corpus
- Distance metrics and band selection in hyperspectral processing with applications to material identification and spectral libraries (2004, methodology): “Three common detectors for unstructured backgrounds [29] are the Generalized Likelihood Ratio Test (GLRT) [21], the Adaptive Coherence Estimator (ACE) [26,27], and the Adaptive Matched Filter (AMF) [34].”
- Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry (2017, methodology): “Thereby, estimators relying on variance—directly related with the fill factor of the target—can be used, such as adaptive coherence cosine (ACE) estimator [80,81].”
- A STAP overview (2004, methodology): “In addition to the AMF, other extensions of the SMI beamformer yielding CFAR behavior include the generalized likelihood ratio test (GLRT) of [21] and the adaptive coherence estimator (ACE) given in [22].”
- Sparse Representation for Target Detection in Hyperspectral Imagery (2011, background): “The GLRT for the above problem is given by [15] (7) where is the estimated background covariance.”
- Parametric Rao Test for Multichannel Adaptive Signal Detection (2007, methodology): “The asymptotic distribution of the Rao test statistic is obtained in closed form, which follows an exponential distribution under the null hypothesis H0 and, respectively, a noncentral Chi-squared distribution with two degrees of freedom under the alternative hypothesis H1.”
- Collaborative Representation for Hyperspectral Anomaly Detection (2015, neutral): “(13)”