Robust Adaptive Vector Sensor Processing

2008 ◽  
Vol 123 (5) ◽  
pp. 3438-3438
Author(s):  
Andrew J. Poulsen ◽  
Arthur B. Baggeroer ◽  
Jonathan Paul Kitchens ◽  
Jennifer A. Watson
Keyword(s):  
Vector Sensor ◽  
Sensor Processing ◽  
Robust Adaptive

scholarly journals The Maximal Eigengap Estimator for Acoustic Vector-Sensor Processing

2021 ◽  
Author(s):  
Robert Bassett ◽  
Jacob Foster ◽  
Kay L. Gemba ◽  
Paul Leary ◽  
Kevin B. Smith
Keyword(s):  
Acoustic Vector Sensor ◽  
Vector Sensor ◽  
Sensor Processing

Coherent vector sensor processing for autonomous underwater glider networks

2014 ◽  
Vol 135 (4) ◽  
pp. 2396-2396
Author(s):  
Brendan Nichols ◽  
James Martin ◽  
Karim Sabra ◽  
David Trivett ◽  
Kevin B. Smith
Keyword(s):  
Underwater Glider ◽  
Vector Sensor ◽  
Sensor Processing ◽  
Autonomous Underwater Glider

scholarly journals User–Parameter–Free Robust Adaptive Beamforming Algorithm for Vector–Sensor Arrayswithin the Hypercomplex Framework

2013 ◽  
Vol 64 (2) ◽  
pp. 100-105
Author(s):  
Xiaoming Gou ◽  
Zhiwen Liu ◽  
Jingyan Ma ◽  
Yougen Xu
Keyword(s):  
Numerical Simulations ◽  
Sensor Arrays ◽  
Optimal Solution ◽  
Adaptive Beamforming ◽  
Model Errors ◽  
Vector Sensor ◽  
Two Component ◽  
Maximal Correlation ◽  
Robust Adaptive ◽  
Beamforming Algorithm

The major flaw of the conventional diagonal loading (DL) method is that it is unclear to choose appropriate DL levels or user-parameters (UPs), though several remarkable contributions have been made to regularize model errors without UPs. An UP-free algorithm for two-component vector-sensor arrays, which is robust to steering vector errors, is considered. The algorithm is within the hypercomplex framework using quaternions, and the optimal solution is found at the maximal correlation between the quaternionic and complex outputs. The performance of the proposed beamformer is illustrated via numerical simulations and is compared with several other UP-free adaptive beamformers

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