A High-Resolution TOA Estimation Algorithm Based on Coherent Subspace for Dual-Band Signals

Abstract

The high-resolution TOA estimation of multipath channel is essential for many areas while the resolution of conventional algorithms is constrained by the system bandwidth. With the advancement of communication technology, more applications demand higher positioning accuracy, yet single-band systems are restricted by their limited bandwidth. To address this, we consider utilizing decentralized multi-band signals to obtain a larger available bandwidth for high-resolution TOA estimation. Taking dual-band signals as an example, we exploit the dual-band coherent subspace and propose a novel subspace-based high-resolution TOA estimation algorithm. The proposed algorithm does not require parameter matching with high complexity and is suitable for the existing dual-band communication systems. We also derive the Cramér-Rao Bound (CRB) for the dual-band signal model. Simulation results show that the performance of the proposed algorithm is better than existing subspace-based algorithms available for dual-band signals and converges to the derived CRB.