Signal Parameter Estimation Based on ESPRIT With State Space Model for 5G NR Signals in Indoor and Urban Environments

Positioning with fifth-generation (5G) new radio (NR) signals is promising, particularly in areas where the global navigation satellite system (GNSS) is blocked. However, the design of the receiver for 5G NR signals is challenging by the presence of multipath in indoor and urban environments, especially for time-of-arrival (TOA) tracking. In this paper, we propose a signal parameter estimation method for 5G NR that utilizes the estimation of signal parameters via rotational invariance techniques (ESPRIT) and the state space model to achieve signal parameter estimation. First, the ESPRIT is employed as the delay and phase discriminator, and a path selection criterion based on amplitude is proposed to avoid outliers in ESPRIT estimation caused by overestimating the channel order based on the minimum description length (MDL) criterion. Then, the state space model is used for carrier phase and Doppler frequency estimation, and the measurement matrix based on the phase error of the ESPRIT discriminator is derived. Finally, the performance of the proposed method is tested in an indoor environment containing a commercial small cell base station (BS). The results demonstrate that the proposed method outperforms the DLL and ESPRIT, with a pseudorange estimation root mean square error (RMSE) of 0.63m. Therefore, the proposed method in this article has essential reference significance for promoting and commercializing 5G indoor positioning.