Intrinsic Cram\'er-Rao Bound based 6D Localization and Tracking for 5G/6G Systems

TL;DR

This paper derives an intrinsic Cramér-Rao Bound for 6D localization and tracking in 5G/6G systems, addressing the challenges of rotation matrix representation and proposing two filters for accurate state estimation.

Contribution

It introduces an intrinsic performance benchmark (ICRB) for 6D localization, and develops two filters leveraging this benchmark for improved tracking accuracy.

Findings

ICRB effectively benchmarks 6D localization performance.

Proposed filters demonstrate improved accuracy in simulations.

Filters balance computational complexity and tracking precision.

Abstract

Localization and tracking are critical components of integrated sensing and communication (ISAC) systems, enhancing resource management, beamforming accuracy, and overall system reliability through precise sensing. Due to the high path loss of the high-frequency systems, antenna arrays are required at the transmitter and receiver sides for beamforming gain. However, beam misalignment may occur, which requires accurate tracking of the six-dimensional (6D) state, namely, 3D position and 3D orientation. In this work, we first address the challenge that the rotation matrix, being part of the Lie group rather than Euclidean space, necessitates the derivation of the ICRB for an intrinsic performance benchmark. Then, leveraging the derived ICRB, we develop two filters-one utilizing pose fusion and the other employing error-state Kalman filter to estimate the UE's 6D state for different computational resource consumption and accuracy requirements. Simulation results validate the ICRB and assess the performance of the proposed filters, demonstrating their effectiveness and improved accuracy in 6D state tracking.