A Low-Complexity, High-Precision Time-of-Arrival Estimation Method for Indoor Multipath Channels Using 5G NR Signals

To reduce the impact of multipath indoors in time-of-arrival (TOA) estimation using 5G NR signals, a low-complexity optimization algorithm for first path identification is proposed. This algorithm can achieve precision comparable to that of the Multiple Signal Classification (MUSIC) algorithm in some scenarios while maintaining much lower complexity, offering significant advantages for real-time positioning and low-capacity devices. With the establishment of the multipath aliasing model, a peak reconstruction algorithm based on threshold is proposed. The algorithm achieves better performance in simulation when the normalized threshold is about 0.3 and the time delay interval between the first path and multipath components is relatively large. Real-world experimental results are consistent with simulations, with the root mean square error (RMSE) of positioning below 0.73 m. In the corridor experiment, the accuracy of the novel algorithm is equivalent to that of MUSIC algorithm.