Abstract
In this paper, Bayesian Compressive Sensing algorithm is studied. To deal with signals with multiple snapshots, we extend traditional Bayesian algorithm under the condition of single snapshot to multi-snapshot Bayesian Compressed Sensing (MBCS) algorithm and apply MBCS algorithm to direction of arrival (DOA) estimation of narrowband signals and wideband signals. Simulation shows that the application of BCS to DOA has certain advantages in algorithm performance.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Candès, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Signal Process. Mag. 25(2), 21–30 (2008)
Candès, E.J., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)
Vallet, P., Mestre, X., Loubaton, P.: Performance analysis of an improved MUSIC DoA estimator. IEEE Trans. Signal Process. 63(23), 6407–6422 (2015)
Amiri, P.M., Ghofrani, S.: MUSIC algorithm for DOA estimation of coherent sources. IET Signal Process. 11(4), 429–436 (2017)
Ghofrani, S., Amin, M.G., Zhang, Y.D.: High-resolution direction finding of non-stationary signals using matching pursuit. Signal Process. 93(12), 3466–3478 (2013)
Parian, M.A., Ghofrani, S.: Using ℓ1, 2 mixed-norm MUSIC based on compressive sampling for direction of arrival estimation. In: IEEE International Symposium on Signal Processing and Information Technology, pp. 258–263 (2015)
Steinwandt, J., Roemer, F., Haardt, M., et al.: R-dimensional esprit-type algorithms for strictly second-order non-circular sources and their performance analysis. IEEE Trans. Signal Process. 62(18), 4824–4838 (2014)
Donoho, D.L., Javanmard, A., Montanari, A.: Information-theoretically optimal compressed sensing via spatial coupling and approximate message passing. IEEE Trans. Inf. Theory 59(11), 7434–7464 (2013)
Stanković, L., Orović, I., Stanković, S., et al.: Compressive sensing based separation of nonstationary and stationary signals overlapping in time-frequency. IEEE Trans. Signal Process. 61(18), 4562–4572 (2013)
Li, G., Zhu, Z., Yang, D., et al.: On projection matrix optimization for compressive sensing systems. IEEE Trans. Signal Process. 61(11), 2887–2898 (2013)
Ibrahim, M., Roemer, F., Del, G.G.: On the design of the measurement matrix for compressed sensing based DOA estimation. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3631–3635 (2015)
Acknowledgement
This work was supported by the Nation Science Foundation of China (Under Grant: 61671176).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Li, S., Ma, Y., Gao, Y., Li, J. (2019). DOA Estimation Based on Bayesian Compressive Sensing. In: Jia, M., Guo, Q., Meng, W. (eds) Wireless and Satellite Systems. WiSATS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 280. Springer, Cham. https://doi.org/10.1007/978-3-030-19153-5_62
Download citation
DOI: https://doi.org/10.1007/978-3-030-19153-5_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19152-8
Online ISBN: 978-3-030-19153-5
eBook Packages: Computer ScienceComputer Science (R0)