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Simultaneous source localization and polarization estimation via non-orthogonal joint diagonalization with vector-sensors.

Gong XF, Wang K, Lin QH, Liu ZW, Xu YG - Sensors (Basel) (2012)

Bottom Line: Two new CNJD algorithms are presented, which propose to tackle the high dimensional optimization problem in CNJD via a sequence of simple sub-optimization problems, by using LU or LQ decompositions of the target matrices as well as the Jacobi-type scheme.Furthermore, based on the above CNJD algorithms we present a novel strategy to exploit the multi-dimensional structure present in the second-order statistics of EMVS outputs for simultaneous DOA and polarization estimation.Simulations are provided to compare the proposed strategy with existing tensorial or joint diagonalization based methods.

View Article: PubMed Central - PubMed

Affiliation: School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China. xfgong@dlut.edu.cn

ABSTRACT
Joint estimation of direction-of-arrival (DOA) and polarization with electromagnetic vector-sensors (EMVS) is considered in the framework of complex-valued non-orthogonal joint diagonalization (CNJD). Two new CNJD algorithms are presented, which propose to tackle the high dimensional optimization problem in CNJD via a sequence of simple sub-optimization problems, by using LU or LQ decompositions of the target matrices as well as the Jacobi-type scheme. Furthermore, based on the above CNJD algorithms we present a novel strategy to exploit the multi-dimensional structure present in the second-order statistics of EMVS outputs for simultaneous DOA and polarization estimation. Simulations are provided to compare the proposed strategy with existing tensorial or joint diagonalization based methods.

No MeSH data available.


Performance of LUCJD, LQCJD, PARAFAC, UWEDGE, FAJD, JTJD versus SNR. The number of snapshots is 1,000, and the noise is with covariance levels (ρ1, ρ2) = (0.8, 0.8). (a) The overall RMSAE curves of DOA estimates. (b) The overall RMSE curves of polarization amplitude angle estimates. (c) The overall RMSE curves of polarization phase difference angle estimates.
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f4-sensors-12-03394: Performance of LUCJD, LQCJD, PARAFAC, UWEDGE, FAJD, JTJD versus SNR. The number of snapshots is 1,000, and the noise is with covariance levels (ρ1, ρ2) = (0.8, 0.8). (a) The overall RMSAE curves of DOA estimates. (b) The overall RMSE curves of polarization amplitude angle estimates. (c) The overall RMSE curves of polarization phase difference angle estimates.

Mentions: Firstly, we set the noise covariance levels (ρ1, ρ2) = (0.8, 0.8), set the number of snapshots to 1,000, and let SNR vary between 0 dB∼15 dB. The results obtained from 100 independent runs are plotted in Figure 4. We observe from the results that the proposed LUCJD and LQCJD algorithms provide almost equal precision as well as FAJD, and UWEDGE slightly underperforms these three algorithms. Furthermore, we note that the curves of LUCJD, LQCJD, FAJD, and UWEDGE drop more smoothly than PARAFAC, which indicates an improved robustness to colored noise of CNJD based methods over the PARAFAC based one. In particular, the proposed LUCJD and LQCJD outperform PARAFAC for low SNR levels (0∼6 dB), while PARAFAC performs better for high SNR levels (7∼15 dB). In addition, we note that JTJD underperforms the other algorithms with regards to the overall accuracy of DOA and polarization estimates. The main reason is that JTJD behaves unsteadily in our simulations, and sometimes converges to false solutions (we could draw the distribution of DOA and polarization estimates similarly to Figure 3 for JTJD to clearly show that it converges to false solutions for quite a few independent runs). We also note that UWEDGE slightly underperforms FAJD, LUCJD, and LQCJD with regards to the estimation accuracy. This is mainly because the WLS criterion based algorithms usually perform better with a set of properly designed weights for target matrices while UWEDGE adopts identical ones only.


Simultaneous source localization and polarization estimation via non-orthogonal joint diagonalization with vector-sensors.

Gong XF, Wang K, Lin QH, Liu ZW, Xu YG - Sensors (Basel) (2012)

Performance of LUCJD, LQCJD, PARAFAC, UWEDGE, FAJD, JTJD versus SNR. The number of snapshots is 1,000, and the noise is with covariance levels (ρ1, ρ2) = (0.8, 0.8). (a) The overall RMSAE curves of DOA estimates. (b) The overall RMSE curves of polarization amplitude angle estimates. (c) The overall RMSE curves of polarization phase difference angle estimates.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3376564&req=5

f4-sensors-12-03394: Performance of LUCJD, LQCJD, PARAFAC, UWEDGE, FAJD, JTJD versus SNR. The number of snapshots is 1,000, and the noise is with covariance levels (ρ1, ρ2) = (0.8, 0.8). (a) The overall RMSAE curves of DOA estimates. (b) The overall RMSE curves of polarization amplitude angle estimates. (c) The overall RMSE curves of polarization phase difference angle estimates.
Mentions: Firstly, we set the noise covariance levels (ρ1, ρ2) = (0.8, 0.8), set the number of snapshots to 1,000, and let SNR vary between 0 dB∼15 dB. The results obtained from 100 independent runs are plotted in Figure 4. We observe from the results that the proposed LUCJD and LQCJD algorithms provide almost equal precision as well as FAJD, and UWEDGE slightly underperforms these three algorithms. Furthermore, we note that the curves of LUCJD, LQCJD, FAJD, and UWEDGE drop more smoothly than PARAFAC, which indicates an improved robustness to colored noise of CNJD based methods over the PARAFAC based one. In particular, the proposed LUCJD and LQCJD outperform PARAFAC for low SNR levels (0∼6 dB), while PARAFAC performs better for high SNR levels (7∼15 dB). In addition, we note that JTJD underperforms the other algorithms with regards to the overall accuracy of DOA and polarization estimates. The main reason is that JTJD behaves unsteadily in our simulations, and sometimes converges to false solutions (we could draw the distribution of DOA and polarization estimates similarly to Figure 3 for JTJD to clearly show that it converges to false solutions for quite a few independent runs). We also note that UWEDGE slightly underperforms FAJD, LUCJD, and LQCJD with regards to the estimation accuracy. This is mainly because the WLS criterion based algorithms usually perform better with a set of properly designed weights for target matrices while UWEDGE adopts identical ones only.

Bottom Line: Two new CNJD algorithms are presented, which propose to tackle the high dimensional optimization problem in CNJD via a sequence of simple sub-optimization problems, by using LU or LQ decompositions of the target matrices as well as the Jacobi-type scheme.Furthermore, based on the above CNJD algorithms we present a novel strategy to exploit the multi-dimensional structure present in the second-order statistics of EMVS outputs for simultaneous DOA and polarization estimation.Simulations are provided to compare the proposed strategy with existing tensorial or joint diagonalization based methods.

View Article: PubMed Central - PubMed

Affiliation: School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China. xfgong@dlut.edu.cn

ABSTRACT
Joint estimation of direction-of-arrival (DOA) and polarization with electromagnetic vector-sensors (EMVS) is considered in the framework of complex-valued non-orthogonal joint diagonalization (CNJD). Two new CNJD algorithms are presented, which propose to tackle the high dimensional optimization problem in CNJD via a sequence of simple sub-optimization problems, by using LU or LQ decompositions of the target matrices as well as the Jacobi-type scheme. Furthermore, based on the above CNJD algorithms we present a novel strategy to exploit the multi-dimensional structure present in the second-order statistics of EMVS outputs for simultaneous DOA and polarization estimation. Simulations are provided to compare the proposed strategy with existing tensorial or joint diagonalization based methods.

No MeSH data available.