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BLMS and FBLMS-Based adaptive time varying RCS estimation and 2D-imaging for monostatic MIMO radar systems
Ist Teil von
2017 Twenty-third National Conference on Communications (NCC), 2017, p.1-6
Ort / Verlag
IEEE
Erscheinungsjahr
2017
Link zum Volltext
Quelle
IEEE/IET Electronic Library
Beschreibungen/Notizen
This paper proposes an adaptive framework for radar cross section (RCS) estimation and 2D-imaging in monostatic MIMO radar systems for an unknown number of targets present in the scanning region of the radar with unknown angles and ranges. Further, the reflection coefficients/RCS of the targets are considered to be time varying. The block least mean square (BLMS) based adaptive framework is initially proposed for joint RCS estimation and 2D-imaging in monostatic MIMO radar systems. Subsequently, the fast block LMS (FBLMS) adaptive framework is developed to yield improved estimation as well as imaging accuracies and faster convergence in comparison to BLMS. Analytical expressions are derived for the mean square observation as well as estimation errors for the BLMS, FBLMS schemes along with their global convergence analyses. Simulation results are presented to demonstrate and compare the estimation and imaging performances of the proposed schemes and validate the analysis.