A Parameter Estimation Method for Channel State Information Based on Matrix Pencil

Aiming at the requirement of real-time positioning due to high computational complexity in Wi-Fi indoor positioning technology, a joint Doppler Frequency Shift (DFS), angle of arrival (AoA) and time of flight (ToF) estimation based on improved matrix pencil method is proposed. First, the data matrix containing doppler velocity, angle and delay parameter information is obtained. Then, the proposed algorithm yields the joint DFS, AoA and ToF estimation. The algorithm avoids peak searching and parameter pairing search procedure. Finally, experimental results show demonstrate that the proposed algorithm has better parameter estimation accuracy, and the AoA estimation accuracy can reach 6.17°.

I. Introduction

In recent years, as Wi-Fi is widely deployed in various occasions, the advancement of Wi-Fi-based indoor positioning technology has been promoted. The positioning technology based on channel state information (CSI) has achieved encouraging results. Because the CSI carries the amplitude and phase of the Wi-Fi signal, it can be used to estimate the angle of arrival (AoA) [1], time of flight (ToF)[2] and Doppler frequency shift (DFS)[3]. ToF-based positioning methods multiply ToF and the speed of light to get the distance between transmitter and receiver, and use trilateral measurement to locate targets [4]. The purchase cost is too high to be commercialized if software defined radios (SDRs) are employed to obtain the ToF. For ordinary Wi-Fi commercial network cards, it is difficult to obtain true ToFs due to the lack of synchronization between the transmitter and the receiver. To solve this problem, Chronos [5] used zero sub-carriers (different frequency bands) to measure ToF to avoid parameter estimation problems caused by CSI phase errors. However, this system relies on frequency hopping technology and needs to modify the communication protocol. SpotFi [6] proposed an indoor AoA positioning system, which uses CSI to build a two-dimensional (2D) MUSIC super-resolution algorithm model, and overcomes the limitations of the number of antennas and coherent signals to a certain extent. However, this algorithm is computationally complex, and it is difficult to determine the target location in real time.