High-Resolution Parameter Estimation for Time-Varying Double Directional V2V Channel

This paper describes a high-resolution parameter estimation algorithm applied to the vehicle-to-vehicle (V2V) multi-dimensional channel measurements. The algorithm, which is based on a maximum likelihood estimator, jointly estimates parameters of multipath components, such as time-of-arrival, direction of departure, direction of arrival, and Doppler shift. It serves to provide a comprehensive understanding of the V2V propagation channel. We propose computationally attractive methods to initialize parameters in the global search and evaluate key components in the local optimization. We also apply the algorithm to actual V2V channel measurement data. The results suggest an overall good performance of the estimator, where 80% of the snapshots have a diffuse power ratio less than 20% and the dominant specular paths match well with the environment and dynamics of the measurement.