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arXiv · 2411.05187

Cooperative Maximum Likelihood Target Position Estimation for MIMO-ISAC Networks

Lorenzo Pucci·Tommaso Bacchielli·Andrea Giorgetti
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Abstract

This letter investigates target position estimation in integrated sensing and communication networks composed of multiple cooperating monostatic base stations (BSs). Each BS employs a MIMO-orthogonal time-frequency space (OTFS) scheme, enabling the coexistence of communication and sensing. A general cooperative maximum likelihood (ML) framework is derived, directly estimating the target position in a common reference system rather than relying on local range and angle estimates at each BS. Positioning accuracy is evaluated in single-target scenarios by varying the number of collaborating BSs, using root mean square error (RMSE), and comparing against the square root of the Cram\'er-Rao lower bound. Numerical results demonstrate that the ML framework significantly reduces the position RMSE as the number of cooperating BSs increases.

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Lorenzo Pucci, Tommaso Bacchielli, Andrea Giorgetti. 2024-11-07. Cooperative Maximum Likelihood Target Position Estimation for MIMO-ISAC Networks. https://doi.org/10.1109/lwc.2025.3548446

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