Teaching notes of MVDR in digital signal processing (DSP)

Author

Guo, Xiansheng ; Wan, Qun ; Zhang, Ying ; Liang, Jin

Author_Institution

Dept. of Electron. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China

fYear

2012

fDate

20-23 Aug. 2012

Abstract

The minimum variance distortionless response (MVDR) beamformer is a classical filter to reduce the interference plus noise energy without distorting the desired signal. Semidefinite programming (SDP) is a subfield of convex optimization concerned with the optimization of a linear objective function over the intersection of the cone of positive semidefinite matrices. In this paper, we will show MVDR objective function can be derived from SDP objective function and vice versa. This conclusion can help students better understand MVDR from convex optimization and bring them a new insight of MVDR theory.

Keywords

array signal processing; filters; DSP; MVDR; convex optimization; digital signal processing; filter; interference plus noise energy reduction; linear objective function optimization; minimum variance distortionless response beamformer; positive semidefinite matrices; semidefinite programming; Arrays; Convex functions; Covariance matrix; Education; Estimation; Linear programming; Vectors; array signal processing; beamforming; linearly constrained minimum variance (LCMV); minimum variance distortionless response (MVDR); semidefinite programming (SDP);

fLanguage

English

Publisher

ieee

Conference_Titel

Teaching, Assessment and Learning for Engineering (TALE), 2012 IEEE International Conference on

Conference_Location

Hong Kong

Print_ISBN

978-1-4673-2417-5

Electronic_ISBN

978-1-4673-2416-8

Type

conf

DOI

10.1109/TALE.2012.6360341

Filename

6360341