DOA Estimation for a Mixture of Uncorrelated and Coherent Sources Based on Hierarchical Sparse Bayesian Inference with a Gauss-Exp-Chi2 Prior
Direction of arrival (DOA) estimation algorithms based on sparse Bayesian inference (SBI) can effectively estimate coherent sources without recurring to extra decorrelation techniques, and their estimation performance is highly dependent on the selection of sparse prior. Specifically, the specified...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2018/3505918 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832552378561200128 |
|---|---|
| author | Pinjiao Zhao Weijian Si Guobing Hu Liwei Wang |
| author_facet | Pinjiao Zhao Weijian Si Guobing Hu Liwei Wang |
| author_sort | Pinjiao Zhao |
| collection | DOAJ |
| description | Direction of arrival (DOA) estimation algorithms based on sparse Bayesian inference (SBI) can effectively estimate coherent sources without recurring to extra decorrelation techniques, and their estimation performance is highly dependent on the selection of sparse prior. Specifically, the specified sparse prior is expected to concentrate its mass on the zero and distribute with heavy tails; otherwise, these algorithms may suffer from performance degradation. In this paper, we introduce a new sparse-encouraging prior, referred to as “Gauss-Exp-Chi2” prior, and develop an efficient DOA estimation algorithm for a mixture of uncorrelated and coherent sources under a hierarchical SBI framework. The Gauss-Exp-Chi2 prior distribution exhibits a sharp peak at the origin and heavy tails, and this property makes it an appropriate prior to encourage sparse solutions. A three-layer hierarchical sparse Bayesian model is established. Then, by exploiting variational Bayesian approximation, the model parameters are estimated by alternately updating until Kullback-Leibler (KL) divergence between the true posterior and the variational approximation becomes zero. By constructing the source power spectra with the estimated model parameters, the number and locations of the highest peaks are extracted to obtain source number and DOA estimates. In addition, some implementation details for algorithm optimization are discussed and the Cramér-Rao bound (CRB) of DOA estimation is derived. Simulation results demonstrate the effectiveness and favorable performance of the proposed algorithm as compared with the state-of-the-art sparse Bayesian algorithms. |
| format | Article |
| id | doaj-art-a56c0ae4b7d64628a910a18639f9a23e |
| institution | Kabale University |
| issn | 1687-5869 1687-5877 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-a56c0ae4b7d64628a910a18639f9a23e2025-02-03T05:58:52ZengWileyInternational Journal of Antennas and Propagation1687-58691687-58772018-01-01201810.1155/2018/35059183505918DOA Estimation for a Mixture of Uncorrelated and Coherent Sources Based on Hierarchical Sparse Bayesian Inference with a Gauss-Exp-Chi2 PriorPinjiao Zhao0Weijian Si1Guobing Hu2Liwei Wang3Department of Electronic and Information Engineering, Jinling Institute of Technology, Nanjing 211169, ChinaDepartment of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaDepartment of Electronic and Information Engineering, Jinling Institute of Technology, Nanjing 211169, ChinaNo. 8511 Research of Institute of CASIC, Nanjing 210007, ChinaDirection of arrival (DOA) estimation algorithms based on sparse Bayesian inference (SBI) can effectively estimate coherent sources without recurring to extra decorrelation techniques, and their estimation performance is highly dependent on the selection of sparse prior. Specifically, the specified sparse prior is expected to concentrate its mass on the zero and distribute with heavy tails; otherwise, these algorithms may suffer from performance degradation. In this paper, we introduce a new sparse-encouraging prior, referred to as “Gauss-Exp-Chi2” prior, and develop an efficient DOA estimation algorithm for a mixture of uncorrelated and coherent sources under a hierarchical SBI framework. The Gauss-Exp-Chi2 prior distribution exhibits a sharp peak at the origin and heavy tails, and this property makes it an appropriate prior to encourage sparse solutions. A three-layer hierarchical sparse Bayesian model is established. Then, by exploiting variational Bayesian approximation, the model parameters are estimated by alternately updating until Kullback-Leibler (KL) divergence between the true posterior and the variational approximation becomes zero. By constructing the source power spectra with the estimated model parameters, the number and locations of the highest peaks are extracted to obtain source number and DOA estimates. In addition, some implementation details for algorithm optimization are discussed and the Cramér-Rao bound (CRB) of DOA estimation is derived. Simulation results demonstrate the effectiveness and favorable performance of the proposed algorithm as compared with the state-of-the-art sparse Bayesian algorithms.http://dx.doi.org/10.1155/2018/3505918 |
| spellingShingle | Pinjiao Zhao Weijian Si Guobing Hu Liwei Wang DOA Estimation for a Mixture of Uncorrelated and Coherent Sources Based on Hierarchical Sparse Bayesian Inference with a Gauss-Exp-Chi2 Prior International Journal of Antennas and Propagation |
| title | DOA Estimation for a Mixture of Uncorrelated and Coherent Sources Based on Hierarchical Sparse Bayesian Inference with a Gauss-Exp-Chi2 Prior |
| title_full | DOA Estimation for a Mixture of Uncorrelated and Coherent Sources Based on Hierarchical Sparse Bayesian Inference with a Gauss-Exp-Chi2 Prior |
| title_fullStr | DOA Estimation for a Mixture of Uncorrelated and Coherent Sources Based on Hierarchical Sparse Bayesian Inference with a Gauss-Exp-Chi2 Prior |
| title_full_unstemmed | DOA Estimation for a Mixture of Uncorrelated and Coherent Sources Based on Hierarchical Sparse Bayesian Inference with a Gauss-Exp-Chi2 Prior |
| title_short | DOA Estimation for a Mixture of Uncorrelated and Coherent Sources Based on Hierarchical Sparse Bayesian Inference with a Gauss-Exp-Chi2 Prior |
| title_sort | doa estimation for a mixture of uncorrelated and coherent sources based on hierarchical sparse bayesian inference with a gauss exp chi2 prior |
| url | http://dx.doi.org/10.1155/2018/3505918 |
| work_keys_str_mv | AT pinjiaozhao doaestimationforamixtureofuncorrelatedandcoherentsourcesbasedonhierarchicalsparsebayesianinferencewithagaussexpchi2prior AT weijiansi doaestimationforamixtureofuncorrelatedandcoherentsourcesbasedonhierarchicalsparsebayesianinferencewithagaussexpchi2prior AT guobinghu doaestimationforamixtureofuncorrelatedandcoherentsourcesbasedonhierarchicalsparsebayesianinferencewithagaussexpchi2prior AT liweiwang doaestimationforamixtureofuncorrelatedandcoherentsourcesbasedonhierarchicalsparsebayesianinferencewithagaussexpchi2prior |