Determination of Parameter Value in Constraint of Sparse Spectrum Fitting DOA Estimation Algorithm 


Vol. 41,  No. 8, pp. 917-920, Aug.  2016


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  Abstract

SpSF algorithm is direction-of-arrival estimation algorithm based on sparse representation of incident signlas. Cost function to be optimized for DOA estimation is multi-dimensional nonlinear function, which is hard to handle for optimization. After some manipulation, the problem can be cast into convex optimiztion problem. Convex optimization problem tuns out to be constrained optimization problem, where the parameter in the constraint has to be determined. The solution of the convex optimization problem is dependent on the specific parameter value in the constraint. In this paper, we propose a rule-of-thumb for determining the parameter value in the constraint. Based on the fact that the noise in the array elements is complex Gaussian distributed with zero mean, the average of the Frobenius norm of the matrix in the constraint can be rigorously derived. The parameter in the constrint is set to be two times the average of the Frobenius norm of the matrix in the constraint. It is shown that the SpSF algorithm actually works with the parameter value set by the method proposed in this paper.

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  Cite this article

[IEEE Style]

Y. Cho, J. Paik, J. Lee, "Determination of Parameter Value in Constraint of Sparse Spectrum Fitting DOA Estimation Algorithm," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 8, pp. 917-920, 2016. DOI: .

[ACM Style]

Yunseung Cho, Ji-Woong Paik, and Joon-Ho Lee. 2016. Determination of Parameter Value in Constraint of Sparse Spectrum Fitting DOA Estimation Algorithm. The Journal of Korean Institute of Communications and Information Sciences, 41, 8, (2016), 917-920. DOI: .

[KICS Style]

Yunseung Cho, Ji-Woong Paik, Joon-Ho Lee, "Determination of Parameter Value in Constraint of Sparse Spectrum Fitting DOA Estimation Algorithm," The Journal of Korean Institute of Communications and Information Sciences, vol. 41, no. 8, pp. 917-920, 8. 2016.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880