Position and Velocity Estimation Accuracy in MIMO-OFDM ISAC Networks: A Fisher Information Analysis

Abstract

This paper presents a theoretical framework to derive information-theoretic bounds on the estimation accuracy of target position and velocity in orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) networks composed of multiple cooperative and distributed multiple-input multiple-output (MIMO) base stations (BSs). Leveraging Fisher information analysis, we derive closed-form expressions for the Cram\'er-Rao lower bounds (CRLBs) in both monostatic and bistatic configurations. The framework is then extended to cooperative settings, including networks with multiple coordinated monostatic sensors and multistatic configurations, enabling joint estimation of target position and velocity. We systematically examine how estimation accuracy depends on key system parameters such as the number of BSs, bandwidth, antenna configuration, and network geometry. Numerical results highlight the performance gains enabled by cooperative sensing and provide insights to guide the design of future ISAC systems.