PARALIND-based identifiability results for parameter estimation via uniform linear array

This article applies PARAllel profiles with LINear Dependencies (PARALIND) model to analyze identifiability of parameter estimation in the presence of incoherent multipath via uniform linear array (ULA). New identifiability results are derived based on the uniqueness property of PARALIND model and structure property of ULA. With the strong properties of trilinear model, the proposed identifiability conditions for propagation parameter identification are superior to early studies. We give a new tradeoff between the number of receiving antennae and sampling diversity to ensure parameter identification. Furthermore, a new lower bound of the number of receiving antennae for identifiability is derived. It also shows that the identifiability results is not only determined by traditional factors, such as the number of receiving antennae, oversampling factor or the total number of transmitting paths, but also related to the structure of multipath of sources.