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Adaptive two-step Bayesian MIMO detectors in compound-Gaussian clutter
- Source :
- Signal Processing. 161:1-13
- Publication Year :
- 2019
- Publisher :
- Elsevier BV, 2019.
-
Abstract
- The problem of adaptive target detection in compound-Gaussian clutter with unknown covariance matrix for multiple-input multiple-output (MIMO) radar is addressed in this paper. A set of secondary data for each receiver is assumed to be available, and the primary data and the secondary data own the same covariance matrix structure but different power levels (textures). Firstly, a Bayesian approach is proposed, where the structure is modeled as a random matrix with an appropriate distribution. Then, two ways are adopted to model the texture: an unknown deterministic quantity or a random variable ruled by certain distribution. In this framework, three adaptive generalized likelihood ratio tests (GLRTs) are developed using the two-step design procedure. Finally, the capabilities of the proposed detectors and their superiority with respect to some existing techniques are evaluated via numerical simulations.
- Subjects :
- Computer science
Covariance matrix
Gaussian
Detector
Bayesian probability
MIMO
020206 networking & telecommunications
02 engineering and technology
law.invention
symbols.namesake
Control and Systems Engineering
law
Signal Processing
0202 electrical engineering, electronic engineering, information engineering
symbols
Clutter
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering
Radar
Algorithm
Random matrix
Random variable
Software
Subjects
Details
- ISSN :
- 01651684
- Volume :
- 161
- Database :
- OpenAIRE
- Journal :
- Signal Processing
- Accession number :
- edsair.doi...........5388104ee4de4cfc01de002790f4673f
- Full Text :
- https://doi.org/10.1016/j.sigpro.2019.03.008