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Joint Estimation of Velocity, Angle-of-Arrival and Range (JEVAR) Using a Conjugate Pair of Zadoff-Chu Sequences

This paper studies the joint estimation of velocity, AOA, and range (JEVAR) of a target in a multipath environment, which has gain renewed interest with the advent of 5G Internet of Things (IoT) technologies, owing to the numerous emerging localization-related applications. To solve the JEVAR problem, we propose a novel scheme, wherein the target transmits a pair of conjugate Zadoff-Chu (ZC) sequences and the multi-antenna receiver conducts maximum likelihood (ML) estimation. The proposed scheme is computationally efficient: it uses alternating projection (AP) to reduce the high-dimensional problem due to the multipaths to multiple lower-dimensional ones per path; it uses a conjugate pair of ZC sequences to decouple the joint estimation of the frequency and time offsets of each path into two separate estimates. The proposed scheme is highly versatile: it can resolve the multipaths with super resolution in both spatial and temporal domain; it can measure the velocity of the target via estimating the frequency offsets of the line-of-sight (LOS) signal and its multipath reflections. The simulations verify the effectiveness of the proposed scheme by showing that its performance can approach the Cramer-Rao bound (CRB).