4 research outputs found
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
Neural Network-Based DOA Estimation in the Presence of Non-Gaussian Interference
This work addresses the problem of direction-of-arrival (DOA) estimation in
the presence of non-Gaussian, heavy-tailed, and spatially-colored interference.
Conventionally, the interference is considered to be Gaussian-distributed and
spatially white. However, in practice, this assumption is not guaranteed, which
results in degraded DOA estimation performance. Maximum likelihood DOA
estimation in the presence of non-Gaussian and spatially colored interference
is computationally complex and not practical. Therefore, this work proposes a
neural network (NN) based DOA estimation approach for spatial spectrum
estimation in multi-source scenarios with a-priori unknown number of sources in
the presence of non-Gaussian spatially-colored interference. The proposed
approach utilizes a single NN instance for simultaneous source enumeration and
DOA estimation. It is shown via simulations that the proposed approach
significantly outperforms conventional and NN-based approaches in terms of
probability of resolution, estimation accuracy, and source enumeration accuracy
in conditions of low SIR, small sample support, and when the angular separation
between the source DOAs and the spatially-colored interference is small.Comment: Submitted to IEEE Transactions on Aerospace and Electronic System
Iterative Marginal Maximum Likelihood DOD and DOA Estimation for MIMO Radar in the Presence of SIRP Clutter
The spherically invariant random process (SIRP) clutter model is commonly
used in scenarios where the radar clutter cannot be correctly modeled as a
Gaussian process. In this short communication, we devise a novel
Maximum-Likelihood (ML)-based iterative estimator for direction-of-departure
and direction-of-arrival estimation in the Multiple-input multiple-output
(MIMO) radar context in the presence of SIRP clutter. The proposed estimator
employs a stepwise numerical concentration approach w.r.t. the objective
function related to the marginal likelihood of the observation data. Our
estimator leads to superior performance, as our simulations show, w.r.t. to the
existing likelihood based methods, namely, the conventional, the conditional
and the joint likelihood based estimators, and w.r.t. the robust subspace
decomposition based methods. Finally, interconnections and comparison between
the Iterative Marginal ML Estimator (IMMLE), Iterative Joint ML Estimator
(IJMLE) and Iterative Conditional ML Estimator (ICdMLE) are provided