Target parameter estimation for spatial and temporal formulations in MIMO radars using compressive sensing
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
KAUST Grant NumberKAUST-002
Online Publication Date2017-01-09
Print Publication Date2017-12
Permanent link to this recordhttp://hdl.handle.net/10754/622689
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AbstractConventional algorithms used for parameter estimation in colocated multiple-input-multiple-output (MIMO) radars require the inversion of the covariance matrix of the received spatial samples. In these algorithms, the number of received snapshots should be at least equal to the size of the covariance matrix. For large size MIMO antenna arrays, the inversion of the covariance matrix becomes computationally very expensive. Compressive sensing (CS) algorithms which do not require the inversion of the complete covariance matrix can be used for parameter estimation with fewer number of received snapshots. In this work, it is shown that the spatial formulation is best suitable for large MIMO arrays when CS algorithms are used. A temporal formulation is proposed which fits the CS algorithms framework, especially for small size MIMO arrays. A recently proposed low-complexity CS algorithm named support agnostic Bayesian matching pursuit (SABMP) is used to estimate target parameters for both spatial and temporal formulations for the unknown number of targets. The simulation results show the advantage of SABMP algorithm utilizing low number of snapshots and better parameter estimation for both small and large number of antenna elements. Moreover, it is shown by simulations that SABMP is more effective than other existing algorithms at high signal-to-noise ratio.
CitationAli H, Ahmed S, Al-Naffouri TY, Sharawi MS, Alouini M-S (2017) Target parameter estimation for spatial and temporal formulations in MIMO radars using compressive sensing. EURASIP Journal on Advances in Signal Processing 2017. Available: http://dx.doi.org/10.1186/s13634-016-0436-x.
SponsorsThis research was funded by a grant from the office of competitive research funding (OCRF) at the King Abdullah University of Science and Technology (KAUST). The work was also supported by the Deanship of Scientific Research (DSR) at King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia, through project number KAUST-002. The authors acknowledge the Information Technology Center at King Fahd University of Petroleum and Minerals (KFUPM) for providing high performance computing resources that have contributed to the research results reported within this paper.
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