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In a multiple-input multiple-output (MIMO) radar, adaptive multitarget tracking can be achieved using subspace tracking algorithms in conjunction with super-resolution subspace localization algorithms. However, the presence of unknown radar clutter deteriorates or breaks algorithms that rely on the white noise assumption. In this article, an   $H^{\infty }$   approach is proposed for robust tracking of each target&#x0027;s range, direction-of-arrival and velocity with unknown clutter. Specifically, two &#x201C;manifold extenders&#x201D; are first proposed by combining the slow-time and fast-time dimensions of a pulse MIMO radar&#x0027;s received signal. Then, in the &#x201C;extended&#x201D; space, an   $H^{\infty }$   adaptive algorithm is proposed to track an equivalent noise subspace, which exists regardless of the noise assumption. Finally, the target parameters are extracted from the adaptively tracked noise subspace. Based on computer simulation studies, the performance of the proposed   $H^{\infty }$   tracking approach is evaluated using challenging tracking scenarios and compared against several existing subspace tracking algorithms that have been modified to operate on the &#x201C;extended&#x201D; space.

MIMO Radar: An H-Infinity Approach for Robust Multitarget Tracking in Unknown Cluttered Environment