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 -