TY - GEN

T1 - MIMO-Radar Waveform Design for Beampattern Using Particle-Swarm-Optimisation

AU - Ahmed, Sajid

AU - Thompson, John S.

AU - Mulgrew, Bernard

N1 - KAUST Repository Item: Exported on 2020-10-01

PY - 2012/12/4

Y1 - 2012/12/4

N2 - Multiple input multiple output (MIMO) radars have many advantages over their phased-array counterparts: improved spatial resolution; better parametric identifiably and greater flexibility to acheive the desired transmit beampattern. The desired transmit beampatterns using MIMO-radar requires the waveforms to have arbitrary auto- and cross-correlations. To design such waveforms, generally a waveform covariance matrix, R, is synthesised first then the actual waveforms are designed. Synthesis of the covariance matrix, R, is a constrained optimisation problem, which requires R to be positive semidefinite and all of its diagonal elements to be equal. To simplify the first constraint the covariance matrix is synthesised indirectly from its square-root matrix U, while for the second constraint the elements of the m-th column of U are parameterised using the coordinates of the m-hypersphere. This implicitly fulfils both of the constraints and enables us to write the cost-function in closed form. Then the cost-function is optimised using a simple particle-swarm-optimisation (PSO) technique, which requires only the cost-function and can optimise any choice of norm cost-function. © 2012 IEEE.

AB - Multiple input multiple output (MIMO) radars have many advantages over their phased-array counterparts: improved spatial resolution; better parametric identifiably and greater flexibility to acheive the desired transmit beampattern. The desired transmit beampatterns using MIMO-radar requires the waveforms to have arbitrary auto- and cross-correlations. To design such waveforms, generally a waveform covariance matrix, R, is synthesised first then the actual waveforms are designed. Synthesis of the covariance matrix, R, is a constrained optimisation problem, which requires R to be positive semidefinite and all of its diagonal elements to be equal. To simplify the first constraint the covariance matrix is synthesised indirectly from its square-root matrix U, while for the second constraint the elements of the m-th column of U are parameterised using the coordinates of the m-hypersphere. This implicitly fulfils both of the constraints and enables us to write the cost-function in closed form. Then the cost-function is optimised using a simple particle-swarm-optimisation (PSO) technique, which requires only the cost-function and can optimise any choice of norm cost-function. © 2012 IEEE.

UR - http://hdl.handle.net/10754/236735

UR - http://ieeexplore.ieee.org/document/6364672/

UR - http://www.scopus.com/inward/record.url?scp=84871956977&partnerID=8YFLogxK

U2 - 10.1109/ICC.2012.6364672

DO - 10.1109/ICC.2012.6364672

M3 - Conference contribution

SN - 9781457720536

SP - 6381

EP - 6385

BT - 2012 IEEE International Conference on Communications (ICC)

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -