392 research outputs found
Performance of two Low-Rank STAP Filters in a Heterogeneous Noise
International audienceThis paper considers the Space Time Adaptive Processing (STAP) problem where the disturbance is modeled as the sum of a Low-Rank (LR) Spherically Invariant Random Vector (SIRV) clutter and a zero-mean white Gaussian noise. To derive our adaptive LR-STAP filters, the estimation of the projector onto the clutter subspace is performed from the Sample Covariance Matrix (SCM) and the Normalized Sample Covari-ance Matrix (NSCM). We compute the theoretical performance of both corresponding LR-STAP filters through the analysis of the Signal to Interference plus Noise Ratio (SINR) Loss, based on a perturbation analysis. Numerical simulations validate the theoretical formula and allow to show that the LR-STAP filter built from the SCM performance does not depend on the heterogeneity of the SIRV clutter whereas the LR-STAP filter built from the NSCM performance does
Robust estimation of the clutter subspace for a Low Rank heterogeneous noise under high Clutter to Noise Ratio assumption
International audienceIn the context of an heterogeneous disturbance with a Low Rank (LR) structure (called clutter), one may use the LR approximation for filtering and detection process. These methods are based on the projector onto the clutter subspace instead of the noise covariance matrix. In such context, adaptive LR schemes have been shown to require less secondary data to reach equivalent performances as classical ones. The main problem is then the estimation of the clutter subspace instead of the noise covariance matrix itself. Maximum Likelihood estimator (MLE) of the clutter subspace has been recently studied for a noise composed of a LR Spherically Invariant Random Vector (SIRV) plus a white Gaussian Noise (WGN). This paper focuses on environments with a high Clutter to Noise Ratio (CNR). An original MLE of the clutter subspace is proposed in this context. A cross-interpretation of this new result and previous ones is provided. Validity and interest - in terms of performance and robustness - of the different approaches are illustrated through simulation results
Single data set detection for multistatic doppler radar
The aim of this thesis is to develop and analyse single data set (SDS) detection algorithms that
can utilise the advantages of widely-spaced (statistical) multiple-input multiple-output (MIMO)
radar to increase their accuracy and performance. The algorithms make use of the observations
obtained from multiple space-time adaptive processing (STAP) receivers and focus on covariance
estimation and inversion to perform target detection.
One of the main interferers for a Doppler radar has always been the radarâs own signal being
reflected off the surroundings. The reflections of the transmitted waveforms from the ground
and other stationary or slowly-moving objects in the background generate observations that can
potentially raise false alarms. This creates the problem of searching for a target in both additive
white Gaussian noise (AWGN) and highly-correlated (coloured) interference. Traditional STAP
deals with the problem by using target-free training data to study this environment and build
its characteristic covariance matrix. The data usually comes from range gates neighbouring
the cell under test (CUT). In non-homogeneous or non-stationary environments, however, this
training data may not reflect the statistics of the CUT accurately, which justifies the need to develop
SDS methods for radar detection. The maximum likelihood estimation detector (MLED)
and the generalised maximum likelihood estimation detector (GMLED) are two reduced-rank
STAP algorithms that eliminate the need for training data when mapping the statistics of the
background interference. The work in this thesis is largely based on these two algorithms.
The first work derives the optimal maximum likelihood (ML) solution to the target detection
problem when the MLED and GMLED are used in a multistatic radar scenario. This application
assumes that the spatio-temporal Doppler frequencies produces in the individual bistatic
STAP pairs of the MIMO system are ideally synchronised. Therefore the focus is on providing
the multistatic outcome to the target detection problem. It is shown that the derived MIMO
detectors possess the desirable constant false alarm rate (CFAR) property. Gaussian approximations
to the statistics of the multistatic MLED and GMLED are derived in order to provide
a more in-depth analysis of the algorithms. The viability of the theoretical models and their
approximations are tested against a numerical simulation of the systems.
The second work focuses on the synchronisation of the spatio-temporal Doppler frequency
data from the individual bistatic STAP pairs in the multistatic MLED scenario. It expands
the idea to a form that could be implemented in a practical radar scenario. To reduce the
information shared between the bistatic STAP channels, a data compression method is proposed
that extracts the significant contributions of the MLED likelihood function before transmission.
To perform the inter-channel synchronisation, the Doppler frequency data is projected into
the space of potential target velocities where the multistatic likelihood is formed. Based on
the expected structure of the velocity likelihood in the presence of a target, a modification to
the multistatic MLED is proposed. It is demonstrated through numerical simulations that the
proposed modified algorithm performs better than the basic multistatic MLED while having the
benefit of reducing the data exchange in the MIMO radar system
SynthÚse des traitements STAP pour la détection en environnement hétérogÚne
Cet article synthétise les différents algorithmes spatio-temporels adaptatifs (STAP) développés et/ou utilisés pour la détection en environnement non-homogÚne. Nous rappelons en
premier lieu les causes principales qui peuvent conduire à un environnement hétérogÚne. Puis nous présentons les stratégies STAP les plus communément utilisées dans de tels environnements
Two-dimensional multivariate parametric models for radar applications-Part I: Maximum-entropy extensions for Toeplitz-block matrices
Copyright © 2008 IEEEIn a series of two papers, a new class of parametric models for two-dimensional multivariate (matrix-valued, space-time) adaptive processing is introduced. This class is based on the maximum-entropy extension and/or completion of partially specified matrix-valued Hermitian covariance matrices in both the space and time dimensions. This first paper considers the more restricted class of Toeplitz Hermitian covariance matrices that model stationary clutter. If the clutter is stationary only in time then we deal with a Toeplitz-block matrix, whereas clutter that is stationary in time and space is described by a Toeplitz-block-Toeplitz matrix. We first derive exact expressions for this new class of 2-D models that act as approximations for the unknown true covariance matrix. Second, we propose suboptimal (but computationally simpler) relaxed 2-D time-varying autoregressive models (ldquorelaxationsrdquo) that directly use the non-Toeplitz Hermitian sample covariance matrix. The high efficiency of these parametric models is illustrated by simulation results using true ground-clutter covariance matrices provided by the DARPA KASSPER Dataset 1, which is a trusted phenomenological airborne radar model, and a complementary AFRL dataset.Yuri I. Abramovich, Ben A. Johnson, and Nicholas K. Spence
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