Iterative adaptive Kronecker MIMO radar beamformer: description and convergence analysis

Authors:
Yuri I. Abramovich;Gordon J. Frazer;Ben A. Johnson
Affiliations:
Intelligence, Surveillance and Reconnaissance Division, Defence Science and Technology Organisation, Edinburgh, SA, Australia;Intelligence, Surveillance and Reconnaissance Division, Defence Science and Technology Organisation, Edinburgh, SA, Australia;Lockheed Martin Australia and the University of South Australia, Mawson Lakes, SA, Australia
Venue:
IEEE Transactions on Signal Processing
Year:
2010

Citing 5
Cited 0

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Abstract

We introduce an iterative procedure for design of adaptive KL-variate linear beamformers that are structured as the Kronecker product of K-variate (transmit) and L-variate (receive) beamformers. We focus on MIMO radar applications for scenarios where only joint transmit and receive adaptive beamforming can efficiently mitigate multi-mode propagated backscatter interference. This is because the direction-of-departure (DoD) on one interference mode, and the direction-of-arrival (DoA) on the other, coincide with those of a target, respectively. We introduce a Markov model for the adaptive iterative routine, specify its convergence condition, and derive final (stable) signal-to-interference-plus-noise ratio (SINR) performance characteristics. Simulation results demonstrate high accuracy of the analytical derivations. In addition, we demonstrate, that for the considered class of multiple-input multiple-output (MIMO) radar interference scenarios, the diagonally loaded sample matrix inversion (SMI) algorithm provides additional performance improvement and convergence rate for this iterative adaptive Kronecker beamformer.