Reference : Recurrent Design of Probing Waveform for Sparse Bayesian Learning Based DOA Estimation
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/53391
Recurrent Design of Probing Waveform for Sparse Bayesian Learning Based DOA Estimation
English
Wu, Linlong []
Dai, Jisheng []
Mysore Rama Rao, Bhavani Shankar mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SPARC >]
Hu, Ruizhi []
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
2022
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
IEEE
Yes
International
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
23-05-2022 to 27-05-2022
Singapore
Singapore
[en] Direction-of-arrival (DOA) estimation can be represented as a sparse signal recovery problem and effectively solved by sparse Bayesian learning (SBL). For the DOA estimation in active sensing, the SBL-based estimation error is related to the transmitted probing waveform. Therefore, it is expected to improve the estimation by waveform optimization. In this paper, we propose a recurrent scheme of waveform design by sequentially leveraging on the previous-round SBL estimates. Within this scheme, we formulate the waveform design problem as a minimization of the SBL estimation variance, which is non-convex and then solved by a majorization-minimization based algorithm. The simulations demonstrate the efficacy of the proposed design scheme in terms of avoiding incorrect detection and accelerating the DOA estimation convergence. Further, the results indicate that the waveform design is essentially a beampattern shaping methodology.
http://hdl.handle.net/10993/53391
10.1109/ICASSP43922.2022.9746708

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