Author

T. Routtenberg

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2026

Oracle Cramér-Rao Bounds for Sparse Vector Estimation

In this paper, we revisit the Cramér-Rao bound (CRB) for deterministic sparse vector estimation under a general observation model. For a given support set <inline-formula><tex-math notation="LaTeX">$\mathcal {S}$</tex-math></inline-formula>, we identify and analyze two alternative oracle CRB formulations: (i) a <italic...

Morad Halihal, T. Routtenberg · IEEE Signal Processing Lette... · 0 citations

Preprint Aug 2026

EM-KalmanNet: Learned Expectation-Maximization for Adaptive Tracking in Partially Known, Block-Wise Time-Varying State-Space Models

State estimation in partially known state space (SS) models is challenging when the dynamics or observation model varies across short data blocks. Classical model-based approaches, such as the expectation-maximization (EM) Kalman filter, jointly recover the latent states and the unknown model parameters, but rely on li...

O. Cohen, Nir Shlezinger, T. Routtenberg · 0 citations