20 research outputs found
Root-MUSIC Based Angle Estimation for MIMO Radar with Unknown Mutual Coupling
Direction of arrival (DOA) estimation problem for multiple-input multiple-output (MIMO) radar with unknown mutual coupling is studied, and an algorithm for the DOA estimation based on root multiple signal classification (MUSIC) is proposed. Firstly, according to the Toeplitz structure of the mutual coupling matrix, output data of some specified sensors are selected to eliminate the influence of the mutual coupling. Then the reduced-dimension transformation is applied to make the computation burden lower as well as obtain a Vandermonde structure of the direction matrix. Finally, Root-MUSIC can be adopted for the angle estimation. The angle estimation performance of the proposed algorithm is better than that of estimation of signal parameters via rotational invariance techniques (ESPRIT)-like algorithm and MUSIC-like algorithm. Furthermore, the proposed algorithm has lower complexity than them. The simulation results verify the effectiveness of the algorithm, and the theoretical estimation error of the algorithm is also derived
Joint-2D-SL0 Algorithm for Joint Sparse Matrix Reconstruction
Sparse matrix reconstruction has a wide application such as DOA estimation and STAP. However, its performance is usually restricted by the grid mismatch problem. In this paper, we revise the sparse matrix reconstruction model and propose the joint sparse matrix reconstruction model based on one-order Taylor expansion. And it can overcome the grid mismatch problem. Then, we put forward the Joint-2D-SL0 algorithm which can solve the joint sparse matrix reconstruction problem efficiently. Compared with the Kronecker compressive sensing method, our proposed method has a higher computational efficiency and acceptable reconstruction accuracy. Finally, simulation results validate the superiority of the proposed method
MIMO Radar Target Localization and Performance Evaluation under SIRP Clutter
Multiple-input multiple-output (MIMO) radar has become a thriving subject of
research during the past decades. In the MIMO radar context, it is sometimes
more accurate to model the radar clutter as a non-Gaussian process, more
specifically, by using the spherically invariant random process (SIRP) model.
In this paper, we focus on the estimation and performance analysis of the
angular spacing between two targets for the MIMO radar under the SIRP clutter.
First, we propose an iterative maximum likelihood as well as an iterative
maximum a posteriori estimator, for the target's spacing parameter estimation
in the SIRP clutter context. Then we derive and compare various
Cram\'er-Rao-like bounds (CRLBs) for performance assessment. Finally, we
address the problem of target resolvability by using the concept of angular
resolution limit (ARL), and derive an analytical, closed-form expression of the
ARL based on Smith's criterion, between two closely spaced targets in a MIMO
radar context under SIRP clutter. For this aim we also obtain the non-matrix,
closed-form expressions for each of the CRLBs. Finally, we provide numerical
simulations to assess the performance of the proposed algorithms, the validity
of the derived ARL expression, and to reveal the ARL's insightful properties.Comment: 34 pages, 12 figure
ESPRIT-like two-dimensional direction finding for mixed circular and strictly noncircular sources based on joint diagonalization
In this paper, a two-dimensional (2-D) direction-of-arrival (DOA) estimation method for a mixture of circular and strictly noncircular signals is presented based on a uniform rectangular array (URA). We first formulate a new 2-D array model for such a mixture of signals, and then utilize the observed data coupled with its conjugate counterparts to construct a new data vector and its associated covariance matrix for DOA estimation. By exploiting the second-order non-circularity of incoming signals, a computationally effective ESPRIT-like method is adopted to estimate the 2-D DOAs of mixed sources which are automatically paired by joint diagonalization of two direction matrices. One particular advantage of the proposed method is that it can solve the angle ambiguity problem when multiple incoming signals have the same angle θ or β. Furthermore, the theoretical error performance of the proposed method is analyzed and a closed-form expression for the deterministic Cramer-Rao bound (CRB) for the considered signal scenario is derived. Simulation results are provided to verify the effectiveness of the proposed method
Joint Beamforming Design and 3D DoA Estimation for RIS-aided Communication System
In this paper, we consider a reconfigurable intelligent surface
(RIS)-assisted 3D direction-of-arrival (DoA) estimation system, in which a
uniform planar array (UPA) RIS is deployed to provide virtual line-of-sight
(LOS) links and reflect the uplink pilot signal to sensors. To overcome the
mutually coupled problem between the beamforming design at the RIS and DoA
estimation, we explore the separable sparse representation structure and
propose an alternating optimization algorithm. The grid-based DoA estimation is
modeled as a joint-sparse recovery problem considering the grid bias, and the
Joint-2D-OMP method is used to estimate both on-grid and off-grid parts. The
corresponding Cram\'er-Rao lower bound (CRLB) is derived to evaluate the
estimation. Then, the beampattern at the RIS is optimized to maximize the
signal-to-noise (SNR) at sensors according to the estimated angles. Numerical
results show that the proposed alternating optimization algorithm can achieve
lower estimation error compared to benchmarks of random beamforming design.Comment: 6 pages, 6 figure
Performance Analysis of Integrated Sensing and Communications Under Gain-Phase Imperfections
This paper evaluates the performance of uplink integrated sensing and
communication systems in the presence of gain and phase imperfections.
Specifically, we consider multiple unmanned aerial vehicles (UAVs) transmitting
data to a multiple-input-multiple-output base-station (BS) that is responsible
for estimating the transmitted information in addition to localising the
transmitting UAVs. The signal processing at the BS is divided into two
consecutive stages: localisation and communication. A maximum likelihood (ML)
algorithm is introduced for the localisation stage to jointly estimate the
azimuth-elevation angles and Doppler frequency of the UAVs under gain-phase
defects, which are then compared to the estimation of signal parameters via
rotational invariance techniques (ESPRIT) and multiple signal classification
(MUSIC). Furthermore, the Cramer-Rao lower bound (CRLB) is derived to evaluate
the asymptotic performance and quantify the influence of the gain-phase
imperfections which are modelled using Rician and von Mises distributions,
respectively. Thereafter, in the communication stage, the location parameters
estimated in the first stage are employed to estimate the communication
channels which are fed into a maximum ratio combiner to preprocess the received
communication signal. An accurate closed-form approximation of the achievable
average sum data rate (SDR) for all UAVs is derived. The obtained results show
that gain-phase imperfections have a significant influence on both localisation
and communication, however, the proposed ML is less sensitive when compared to
other algorithms. The derived analysis is concurred with simulations.Comment: 38 pages, 7 figure
Two-dimensional angular parameter estimation for noncircular incoherently distributed sources based on an L-shaped array
In this paper, a two-stage reduced-rank estimator is proposed for two-dimensional (2D) direction estimation of incoherently distributed (ID) noncircular sources, including their center directions of arrival (DOAs) and angular spreads, based on an L-shaped array. Firstly, based on the first-order Taylor series approximation, a noncircularity-based extended generalized array manifold (GAM) model is established. Then, the 2D center DOAs of incident ID signals are obtained separately with the noncircularity-based generalized shift-invariance property of the array manifold and the reduced-rank principle. The pairing of the two center DOAs is completed by searching for the minimum value of a cost function. Secondly, the 2D angular spreads can be obtained in closed-form solution from the central moments of the angular distribution. The proposed estimator achieves higher accuracy in angle estimation that manages more sources and shows promising results in the general scenario, where different sources possess different angular distributions. Furthermore, the approximate noncircular stochastic Cramer-Rao bound (CRB) of the concerned problem is derived as a benchmark. Numerical analysis proves that the proposed algorithm achieves better estimation performance in both 2D center DOAs and 2D angular spreads than an existing estimator
Can Far-field Beam Training Be Deployed for Cross-field Beam Alignment in Terahertz UM-MIMO Communications?
Ultra-massive multiple-input multiple-output (UM-MIMO) is the enabler of
Terahertz (THz) communications in next-generation wireless networks. In THz
UM-MIMO systems, a new paradigm of cross-field communications spanning from
near-field to far-field is emerging, since the near-field range expands with
higher frequencies and larger array apertures. Precise beam alignment in
cross-field is critical but challenging. Specifically, unlike far-field beams
that rely only on the angle domain, the incorporation of dual-domain (angle and
distance) training significantly increases overhead. A natural question arises
of whether far-field beam training can be deployed for cross-field beam
alignment. In this paper, this question is answered, by demonstrating that the
far-field training enables sufficient signal-to-noise ratio (SNR) in both far-
and near-field scenarios, while exciting all channel dimensions. Based on that,
we propose a subarray-coordinated hierarchical (SCH) training with greatly
reduced overhead. To further obtain high-precision beam designs, we propose a
two-phase angle and distance beam estimator (TPBE). Extensive simulations
demonstrate the effectiveness of the proposed methods. Compared to near-field
exhaustive search, the SCH possesses 0.2\% training overhead. The TPBE achieves
0.01~degrees and 0.02~m estimation root-mean-squared errors for angle and
distance. Furthermore, with the estimated beam directions, a near-optimal SNR
with 0.11~dB deviation is attained after beam alignment