4 research outputs found
Minimax rank estimation for subspace tracking
Rank estimation is a classical model order selection problem that arises in a
variety of important statistical signal and array processing systems, yet is
addressed relatively infrequently in the extant literature. Here we present
sample covariance asymptotics stemming from random matrix theory, and bring
them to bear on the problem of optimal rank estimation in the context of the
standard array observation model with additive white Gaussian noise. The most
significant of these results demonstrates the existence of a phase transition
threshold, below which eigenvalues and associated eigenvectors of the sample
covariance fail to provide any information on population eigenvalues. We then
develop a decision-theoretic rank estimation framework that leads to a simple
ordered selection rule based on thresholding; in contrast to competing
approaches, however, it admits asymptotic minimax optimality and is free of
tuning parameters. We analyze the asymptotic performance of our rank selection
procedure and conclude with a brief simulation study demonstrating its
practical efficacy in the context of subspace tracking.Comment: 10 pages, 4 figures; final versio
A Modified Fast Approximated Power Iteration Subspace Tracking Method for Space-Time Adaptive Processing
We propose a subspace-tracking-based space-time adaptive processing technique for airborne radar applications. By applying a modified approximated power iteration subspace tracing algorithm, the principal subspace in which the clutter-plus-interference reside is estimated. Therefore, the moving targets are detected by projecting the data on the minor subspace which is orthogonal to the principal subspace. The proposed approach overcomes the shortcomings of the existing methods and has satisfactory performance. Simulation results confirm that the performance improvement is achieved at very small secondary sample support, a feature that is particularly attractive for applications in heterogeneous environments
Tensor-based tracking schemes for time-delay estimation in GNSS multi-antenna receivers
Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2017.Embora os receptores GNSS (Global Navigation Satellite Systems) alcancem atualmente
alta precisão ao processar sua localização geográfica sob condições de Linha de Visão
(Line of Sight), erros devido a interferência por componentes multipercurso e ruído são
as fontes mais degradantes desse sistema. A fim de resolver a interferência multipercurso, receptores baseados em múltiplas antenas tornaram-se o foco de pesquisa e desenvolvimento tecnológico devido ao fato de que podem mitigar a ocorrência de multipercurso fornecendo as melhores estimativas para o atraso do sinal transmitido, que é um parâmetro relevante para determinar a geolocalização do usuário. Neste contexto, abordagens tensoriais baseadas em modelos PARAFAC (PArallel FActor Analysis) têm sido propostas na literatura, proporcionando um ótimo desempenho. Como essas técnicas são baseadas em subespaços, considerando um cenário de rastreamento em tempo real, o cálculo de uma EVD (Eigenvalue Decomposition)/SVD (Singular Value Decomposition) completa para estimativa de subespaço de sinal em cada instante de amostragem não é adequado, devido a razões de complexidade. Portanto, uma alternativa para reduzir o tempo de computação (Time of Computing) de estimativas de subespacos tem sido o desenvolvimento de algoritmos de rastreamento de subespaço. Este trabalho propõe o emprego de dois esquemas de rastreamento de subespaços para fornecer uma redução no desempenho computacional geral das técnicas de estimativa de atraso de tempo baseadas em tensores.Although Global Navigation Satellite Systems (GNSS) receivers nowadays achieve high
accuracy when processing their geographic location under conditions of Line of Sight
(LOS), errors due to interference by multipath and noise are the most degrading sources
of accuracy. In order to solve the multipath interference, receivers based on multiple antennas have become the focus of technological research and development due to the fact they can mitigate multipath occurrence providing best estimates to the transmitted signal time-delay, which is a relevant parameter for determining the user’s geolocation. In
this context, tensor-based approaches based on PArallel FActor Analysis (PARAFAC)
models have been proposed in the literature, providing optimal performance. As these
techniques are subspace-based, considering a real-time tracking scenario, the computation of a full Eigenvalue Decomposition (EVD)/Singular Value Decomposition (SVD) for signal subspace estimation at every sampling instant is not suitable, due to complexity reasons. Therefore, an alternative to reduce the Time of Computing (ToC) of subspace estimations has been the development of subspace tracking algorithms. This work proposes the employment of two subspace tracking schemes to provide a reduction in the overall computational performance of tensor-based time-delay estimation techniques