Low-Complexity High-Order Propagator Method for Near-Field Source Localization

Abstract

In this paper, an efficient high-order propagator method is proposed to localize near-field sources. We construct a specific non-Hermitian matrix based on the high-order cumulant of the received signals. With its columns and rows, we can obtain two subspaces orthogonal to all the columns of two steering matrices, respectively, with which the estimation of the directions of arrival (DOA) and ranges of near-field sources can be achieved. Different from other methods, the proposed method needs only one matrix for estimating two parameters separately, therefore leading to a smaller computational burden. Simulation results show that the proposed method achieves the same performance as the other high order statistics-based methods with a lower complexity.

Investigation of Near-Field Source Localization Using Uniform Rectangular Array

“…This shows that the computational cost of one iteration step of the DML algorithm is identical to that of one iteration cycle of the proposed algorithm without ( 29)- (31). In addition, the SAGE-based parameter updating strategy can improve the convergence rate, resulting in less iteration.…”

“…As described in Table 1, we only focus on the computational complexity of the DML and the proposed method in (29)- (31). Here, ∆θ, ∆ϕ, and ∆r represent the search steps of elevation, azimuth, and range, respectively.…”

“…However, the computational complexity of the parameter separation and polynomial rooting operations is high for multiple sources' localization. Some near-field localization algorithms based on the fourth-order cumulant are proposed in [30,31], but the implementation steps are found to be highly complex for a 2D array-based near-field model.…”

Localization for Mixed Near-Field and Far-Field Sources by Interlaced Nested Array

“…In order to remedy the aperture loss, some FOC-based algorithms [9][10][11][12] have been proposed to increase the virtual sensors. In [10,11], Zheng et al exploit second order statistic (SOS) to estimate DOAs of far-field sources, and a FOC matrix is exploited to estimate the DOAs of near-field sources after removing the information of far-field sources.…”

“…In [10,11], Zheng et al exploit second order statistic (SOS) to estimate DOAs of far-field sources, and a FOC matrix is exploited to estimate the DOAs of near-field sources after removing the information of far-field sources. In [12], a FOC-based PM algorithm is proposed for the localization of near-field sources. In fact, besides FOC, using sparse array is also an effective way to reduce aperture loss.…”