2-D DOA Estimation of Incoherently Distributed Sources Considering Gain-Phase Perturbations in Massive MIMO Systems

Abstract

This is a repository copy of 2-D DOA estimation of incoherently distributed sources considering gain-phase perturbations in massive MIMO systems.

An efficient target detection algorithm via Karhunen‐Loève transform for frequency modulated continuous wave (FMCW) radar applications

“…Herein, r l denotes the lth correlation coefficient of data X k while r l the conjugate of r l . Considering the ergodicity of the stationary Gaussian process, the correlationship denoted by r l can be estimated with the means of data vector X k in place of the statistical expectation, as (11) shown.…”

“…On a parallel track, the utilization of radar sensing has been widened to various applications, including air traffic control (ATC), remote sensing, weather observation, collision avoidance and automotive safety, which directly relates to sensing capability [7]. Various advanced processing techniques, such as transmit sequence design [8], timing synchronization [9], joint transmit weight and receive filter [10], and angle estimation considering gain‐phase perturbations [11], would address a modern high‐performance in sensing the interested targets. The radar systems perceive the environment in virtue of multipath, where the signal backscattered from the target is received at the radar site via various propagation path.…”

Spatial Parameter Identification for MIMO Systems in the Presence of Non-Gaussian Interference

“…By estimating the number of transmit antennas (NTA), the exact number of desired users can be obtained, and the users can be effectively regulated to alleviate the interference between them. Direction-of-arrival (DOA) estimation is used to determine the angle and position of the signal from the array of received data [9]. The effective information of the user's location obtained by DOA estimation is crucial for distinguishing the target user from the interfering users and obtaining accurate and reliable transmission of information.…”

Localization of Mixed Far-field and Near-field Incoherently Distributed Sources Using Two-stage RARE Estimator

“…For a rapidly time-varying channel, the channel coherency time is supposed to be much smaller than the observation period, while for a slowly time-varying channel, the situation is just the opposite. Many methods have been proposed for the localization of distributed sources; examples include the dimension reduction MUSIC algorithm for maximal noncircularity rated signals (DRNC-MUSIC) [25], the estimating signal parameters via rotational invariance technique (ESPRIT) [26] or unitary ESPRIT [27], and the perturbed sparse reconstruction based method [28] for CD sources, or the dispersed signal parameter estimator (DISPARE) [29] employing the asymptotic orthogonality property between the quasi-signal and noise subspaces, the ESPRIT [30], [31], the Beamspace [32], the covariance matching estimation techniques (COMET) [33] and the low-rank matrix recovery [34] based methods exploiting the low-rank property of the joint angular-frequency distribution matrix for ID sources. These methods are developed for pure FF distributed sources.…”

“…2: Perform EVD on R to obtain the signal subspace Ûs, and then divide it into Ûs1 and Ûs2 . 3: Construct D(θ) according to(31), and further estimate nominal DOA and the number of FF ID sources from (39). 4: Reconstruct A F ( θ) and calculate OP matrix E A F A N .…”