| 1. |
- Panahi, Ashkan, 1986, et al.
(författare)
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A novel method of DOA tracking by penalized least squares
- 2013
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Ingår i: 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013. - 9781467331463 ; , s. 61-64
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Konferensbidrag (refereegranskat)abstract
- This work develops a new DOA tracking technique by proposing a novel semi-parametric method of sequential sparse recovery for a dynamic sparsity model. The proposed method iteratively provides a sequence of spatial spectrum estimates. The final process of estimating direction paths from the spectrum sequence is not considered. However, the simulation results show concentration of the spectrum around the true directions, which simplifies DOA tracking, for example, using a pattern recognition approach. We have also proved analytical results indicating consistency in terms of spectral concentration, which we omit in the interest of space and postpone to a more extensive work. The semi-parametric nature of the proposed method avoids highly complex data association and makes the method robust against crossing. The computational complexity per time sample is proportional to grid size, which can be contrasted to a single-snapshot LASSO solution that has a polynomial complexity order.
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| 2. |
- Zachariah, Dave, et al.
(författare)
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Enhanced capon beamformer using regularized covariance matching
- 2013
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Ingår i: 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). - Piscataway, NJ : IEEE conference proceedings. - 9781467331463 - 9781467331449 ; , s. 97-100
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Konferensbidrag (refereegranskat)abstract
- The Capon method is a powerful nonparametric approach in array processing based on the sample covariance matrix. For small sample sets, however, its performance is degraded. In this paper we formulate a regularized covariance matching framework based on the nuclear norm for enhancing the Capon method. An approximate iterative solution is developed and tested using simulated data. Appropriate regularization parameter values are also inferred from the data, drawing upon the cross-validation approach. The results show significantly improved spatial spectral and signal waveform estimates.
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