Robust adaptive beamforming based on the Kalman filter

In this paper, we present a novel approach to implement the robust minimum variance distortionless response (MVDR) beamformer. This beamformer is based on worst-case performance optimization and has been shown to provide an excellent robustness against arbitrary but norm-bounded mismatches in the de...

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Bibliographic Details
Published in:IEEE transactions on signal processing Vol. 53; no. 8; pp. 3032 - 3041
Main Authors: El-Keyi, A., Kirubarajan, T., Gershman, A.B.
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.08.2005
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Applied sciences
Array signal processing
Computational efficiency
Constrained Kalman filter
Constraints
Detection, estimation, filtering, equalization, prediction
Distortion
Exact sciences and technology
Information, signal and communications theory
interacting multiple model estimation
Interference
Kalman filters
Merging
On-line systems
Online
Optimization
Programming profession
robust MVDR beamforming
Robustness
Signal and communications theory
Signal processing
Signal, noise
Studies
Telecommunications and information theory
Variance
worst-case performance optimization
Performance evaluation
Second order
robust MVDR beamforming
Kalman filter
Worst case method
Programming
On-line systems
interacting multiple model estimation
Algorithm
Computational complexity
Mismatching
Beam forming
Implementation
Optimization
Constrained Kalman filter
worst-case performance optimization
Minimal variance
Robustness
ISSN:1053-587X, 1941-0476
Online Access:Get full text
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Description
Summary:In this paper, we present a novel approach to implement the robust minimum variance distortionless response (MVDR) beamformer. This beamformer is based on worst-case performance optimization and has been shown to provide an excellent robustness against arbitrary but norm-bounded mismatches in the desired signal steering vector. However, the existing algorithms to solve this problem do not have direct computationally efficient online implementations. In this paper, we develop a new algorithm for the robust MVDR beamformer, which is based on the constrained Kalman filter and can be implemented online with a low computational cost. Our algorithm is shown to have a similar performance to that of the original second-order cone programming (SOCP)-based implementation of the robust MVDR beamformer. We also present two improved modifications of the proposed algorithm to additionally account for nonstationary environments. These modifications are based on model switching and hypothesis merging techniques that further improve the robustness of the beamformer against rapid (abrupt) environmental changes.
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ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2005.851108