Fundamental Limits of Target Parameter Estimation in OFDM-Based 3D NTN ISAC Systems

This work investigates the fundamental performance bound of three-dimensional (3D) target localization and velocity estimation in monostatic orthogonal frequency-division multiplexing (OFDM)-based ISAC systems equipped with uniform rectangular arrays (URAs). Using the equivalent Fisher information matrix, we derive the Cramér-Rao lower bound (CRLB) for position estimation in the 3D case and show how it can be naturally reduced to existing two-dimensional bounds when the URA becomes linear, thereby generalizing prior work. Additionally, under the practical assumption of known direction of motion, particularly relevant in applications such as road traffic monitoring with unmanned aerial vehicles (UAVs), we derive a closed-form CRLB expression for the estimation of target velocity magnitude. These CRLB expressions are then used to assess the impact of key system parameters, including subcarrier count, OFDM frame size, and array geometry, on estimation accuracy. The results provide actionable insights into UAV fleet deployment strategies, such as selecting the optimal sensing node based on spatial configuration and performance metrics. Numerical simulations validate the analytical bounds and highlight fundamental trade-offs in the design of future non-terrestrial network (NTN)-based ISAC architectures.