Abstract
In this paper, indoor passive localization and tracking using a multiple-input multiple-output (MIMO) radar are studied. Indoor localization suffers from severe multipath propagation. We propose two novel clustering-based algorithms for multi-target tracking in such environments. These techniques aim to exploit either the delay-Doppler diversity offered by spread spectrum signals or the delay-angle diversity of received signals using successive beamforming. The targets are tracked using a bank of extended Kalman filters and the filters' predicted future locations of all targets are intelligently utilized to solve the data association problem. Finally, the feasibility of both techniques is analyzed and discussed in light of two common indoor tracking layouts.
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