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

TL;DR

This paper develops a theoretical Fisher information-based framework to analyze the bounds on position and velocity estimation accuracy in MIMO-OFDM ISAC networks, considering various configurations and system parameters.

Contribution

It introduces a novel Fisher information analysis framework for deriving CRLBs in cooperative MIMO-OFDM ISAC networks, extending to joint estimation in multistatic setups.

Findings

Cooperative sensing significantly improves estimation accuracy.

Estimation bounds depend on number of BSs, bandwidth, and antenna configuration.

Numerical results provide design insights for future ISAC systems.

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.