Range-Doppler Imaging via Forward-Backward Sparse Bayesian Learning

Range-Doppler Imaging via Forward-Backward Sparse Bayesian Learning

We consider range-Doppler imaging via transmitting a train of probing pulses as in radar and active sonar. We show that range-Doppler imaging can be formulated as a sparse signal recovery problem and that we can use an expectation maximization based sparse Bayesian learning (EM-SBL) algorithm to ach...

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Veröffentlicht in:IEEE transactions on signal processing 2010-04, Vol.58 (4), p.2421-2425
Hauptverfasser: Tan, Xing, Li, Jian
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Applied sciences
Bayesian analysis
Bayesian methods
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Filters
Forward-backward algorithm
Government
High-resolution imaging
Image processing
Image resolution
Imaging
Information, signal and communications theory
Layout
Learning
Maximization
Miscellaneous
Pulse compression methods
Pulse modulation
Radar imaging
Range-Doppler imaging
Signal and communications theory
Signal processing
Signal, noise
Sonar
sparse Bayesian learning
super resolution
Telecommunications and information theory
Trains
Transmission
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Beschreibung
Zusammenfassung:We consider range-Doppler imaging via transmitting a train of probing pulses as in radar and active sonar. We show that range-Doppler imaging can be formulated as a sparse signal recovery problem and that we can use an expectation maximization based sparse Bayesian learning (EM-SBL) algorithm to achieve high resolution imaging. We also reduce the complexity of EM-SBL significantly by using an efficient forward-backward algorithm in the E step of the EM algorithm.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2009.2037667