3,946 research outputs found
Clutter rejection for MTI radar using a single antenna and a long integration time
Moving Target Indicators (MTI) are airborne radar systems
designed to detect and track moving vehicles or aircrafts. In this paper, we address the problem of detecting hazardous collision targets to avoid them. One of the best known solutions to solve this problem is given by the so-called Space-Time Adaptive Processing (STAP) algorithms which optimally filter the target signal from interference and noise exploiting the specific relationship between Direction Of Arrival (DOA) and Doppler for the ground clutter. However, these algorithms require an antenna array and multiple reception channels that increase cost and complexity.
The authors propose an alternative solution using a single antenna only.
In addition to the standard Doppler shift related to the radial speed, the orthoradial speed of any target can be estimated if using a long integration time. Dangerous targets and ground clutter have different signatures in the radial-orthoradial velocity plane. An optimal detector is then proposed based on the oblique projection onto the signal subspace orthogonal to the clutter subspace. The theoretical performances of this detector are derived and a realistic radar scene simulation shows the benefits of this new MTI detector
Sparse-Based Estimation Performance for Partially Known Overcomplete Large-Systems
We assume the direct sum o for the signal subspace. As a result of
post- measurement, a number of operational contexts presuppose the a priori
knowledge of the LB -dimensional "interfering" subspace and the goal is to
estimate the LA am- plitudes corresponding to subspace . Taking into account
the knowledge of the orthogonal "interfering" subspace \perp, the Bayesian
estimation lower bound is de-
rivedfortheLA-sparsevectorinthedoublyasymptoticscenario,i.e. N,LA,LB -> \infty
with a finite asymptotic ratio. By jointly exploiting the Compressed Sensing
(CS) and the Random Matrix Theory (RMT) frameworks, closed-form expressions for
the lower bound on the estimation of the non-zero entries of a sparse vector of
interest are derived and studied. The derived closed-form expressions enjoy
several interesting features: (i) a simple interpretable expression, (ii) a
very low computational cost especially in the doubly asymptotic scenario, (iii)
an accurate prediction of the mean-square-error (MSE) of popular sparse-based
estimators and (iv) the lower bound remains true for any amplitudes vector
priors. Finally, several idealized scenarios are compared to the derived bound
for a common output signal-to-noise-ratio (SNR) which shows the in- terest of
the joint estimation/rejection methodology derived herein.Comment: 10 pages, 5 figures, Journal of Signal Processin
Joint ML calibration and DOA estimation with separated arrays
This paper investigates parametric direction-of-arrival (DOA) estimation in a
particular context: i) each sensor is characterized by an unknown complex gain
and ii) the array consists of a collection of subarrays which are substantially
separated from each other leading to a structured noise covariance matrix. We
propose two iterative algorithms based on the maximum likelihood (ML)
estimation method adapted to the context of joint array calibration and DOA
estimation. Numerical simulations reveal that the two proposed schemes, the
iterative ML (IML) and the modified iterative ML (MIML) algorithms for joint
array calibration and DOA estimation, outperform the state of the art methods
and the MIML algorithm reaches the Cram\'er-Rao bound for a low number of
iterations
Prior Knowledge Optimum Understanding by Means of Oblique Projectors and Their First Order Derivatives
International audienceRecently, an optimal Prior-knowledge method for DOA estimation has been proposed. This method solely estimates a subset of DOA's accounting known ones. The global idea is to maximize the orthogonal-ity between an estimated signal subspace and noise subspace by constraining the orthogonal noise-made projector to only project onto the desired unknown signal subspace. As it could be surprising, no deflation process is used for. Understanding how it is made possible needs to derive the variance for the DOA estimates. During the derivation, oblique projection operators and their first order derivatives appear and are needed. Those operators show in consequence how the optimal Prior-knowledge criterion can focus only on DOA's of interest and how the optimality is reached
Matched direction detectors and estimators for array processing with subspace steering vector uncertainties
In this paper, we consider the problem of estimating and detecting a signal whose associated spatial signature is known to lie in a given linear subspace but whose coordinates in this subspace are otherwise unknown, in the presence of subspace interference and broad-band noise. This situation arises when, on one hand, there exist uncertainties about the steering vector but, on the other hand, some knowledge about the steering vector errors is available. First, we derive the maximum-likelihood estimator (MLE) for the problem and compute the corresponding Cramer-Rao bound. Next, the maximum-likelihood estimates are used to derive a generalized likelihood ratio test (GLRT). The GLRT is compared and contrasted with the standard matched subspace detectors. The performances of the estimators and detectors are illustrated by means of numerical simulations
Gridless Evolutionary Approach for Line Spectral Estimation with Unknown Model Order
Gridless methods show great superiority in line spectral estimation. These
methods need to solve an atomic norm (i.e., the continuous analog of
norm) minimization problem to estimate frequencies and model order. Since
this problem is NP-hard to compute, relaxations of atomic norm, such as
nuclear norm and reweighted atomic norm, have been employed for promoting
sparsity. However, the relaxations give rise to a resolution limit,
subsequently leading to biased model order and convergence error. To overcome
the above shortcomings of relaxation, we propose a novel idea of simultaneously
estimating the frequencies and model order by means of the atomic norm.
To accomplish this idea, we build a multiobjective optimization model. The
measurment error and the atomic norm are taken as the two optimization
objectives. The proposed model directly exploits the model order via the atomic
norm, thus breaking the resolution limit. We further design a
variable-length evolutionary algorithm to solve the proposed model, which
includes two innovations. One is a variable-length coding and search strategy.
It flexibly codes and interactively searches diverse solutions with different
model orders. These solutions act as steppingstones that help fully exploring
the variable and open-ended frequency search space and provide extensive
potentials towards the optima. Another innovation is a model order pruning
mechanism, which heuristically prunes less contributive frequencies within the
solutions, thus significantly enhancing convergence and diversity. Simulation
results confirm the superiority of our approach in both frequency estimation
and model order selection.Comment: This work has been submitted to the IEEE for possible publication.
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longer be accessibl
Parallel Factor-Based Model for Two-Dimensional Direction Estimation
Two-dimensional (2D) Direction-of-Arrivals (DOA) estimation for elevation and azimuth angles assuming noncoherent, mixture of coherent and noncoherent, and coherent sources using extended three parallel uniform linear arrays (ULAs) is proposed. Most of the existing schemes have drawbacks in estimating 2D DOA for multiple narrowband incident sources as follows: use of large number of snapshots, estimation failure problem for elevation and azimuth angles in the range of typical mobile communication, and estimation of coherent sources. Moreover, the DOA estimation for multiple sources requires complex pair-matching methods. The algorithm proposed in this paper is based on first-order data matrix to overcome these problems. The main contributions of the proposed method are as follows: (1) it avoids estimation failure problem using a new antenna configuration and estimates elevation and azimuth angles for coherent sources; (2) it reduces the estimation complexity by constructing Toeplitz data matrices, which are based on a single or few snapshots; (3) it derives parallel factor (PARAFAC) model to avoid pair-matching problems between multiple sources. Simulation results demonstrate the effectiveness of the proposed algorithm
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