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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 indices of LUCJD, LQCJD, UWEDGE, FAJD, and JTJD versus iterations. SNR is 10 dB, the number of snapshots is 1,000, and the noise is with covariance levels (ρ1, ρ2) = (0.5, 0.5).
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f6-sensors-12-03394: Performance indices of LUCJD, LQCJD, UWEDGE, FAJD, and JTJD versus iterations. SNR is 10 dB, the number of snapshots is 1,000, and the noise is with covariance levels (ρ1, ρ2) = (0.5, 0.5).

Mentions: The simulation settings are the same as the first simulation except that SNR is set to 5 dB. We plot the PI curves from 5 independent runs for all the compared CNJD algorithms in Figure 6. We could observe that the proposed LQCJD yields similar nice quadratic converging pattern as JTJD, of which the PI curves drop dramatically in the fist 2∼3 iterations. The LUCJD algorithm, on the other hand, is less efficient than LQCJD, JTJD, and FAJD with regards to the number of iterations.


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 indices of LUCJD, LQCJD, UWEDGE, FAJD, and JTJD versus iterations. SNR is 10 dB, the number of snapshots is 1,000, and the noise is with covariance levels (ρ1, ρ2) = (0.5, 0.5).
© Copyright Policy
Related In: Results  -  Collection

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

f6-sensors-12-03394: Performance indices of LUCJD, LQCJD, UWEDGE, FAJD, and JTJD versus iterations. SNR is 10 dB, the number of snapshots is 1,000, and the noise is with covariance levels (ρ1, ρ2) = (0.5, 0.5).
Mentions: The simulation settings are the same as the first simulation except that SNR is set to 5 dB. We plot the PI curves from 5 independent runs for all the compared CNJD algorithms in Figure 6. We could observe that the proposed LQCJD yields similar nice quadratic converging pattern as JTJD, of which the PI curves drop dramatically in the fist 2∼3 iterations. The LUCJD algorithm, on the other hand, is less efficient than LQCJD, JTJD, and FAJD with regards to the number of iterations.

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.