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

Integrated sensing and communication (ISAC) is a core technology for future wireless networks, enabling high-resolution sensing and reliable data transmission within a unified radio platform. This paper develops a theoretical framework to assess the estimation accuracy of target position and velocity in heterogeneous orthogonal frequency division multiplexing (OFDM)-based ISAC networks with multiple cooperative and distributed multiple-input multiple-output (MIMO) base stations (BSs). Using Fisher information analysis, we first derive closed-form Cram\'er-Rao lower bounds (CRLBs) for target localization in single monostatic and bistatic configurations. We then analyze the benefits of BS cooperation by deriving CRLBs for joint position and velocity estimation in a general setting that encompasses multiple cooperating monostatic systems and multistatic networks with multiple transmitters (Txs) and receivers (Rxs). The influence of key system parameters, including the number of BSs, bandwidth, antenna array configuration, and network geometry, is systematically examined. Numerical results highlight the performance gains enabled by cooperative sensing and provide insights to guide the design of future ISAC systems.