1,413 research outputs found
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
Sparse Estimation using Bayesian Hierarchical Prior Modeling for Real and Complex Linear Models
In sparse Bayesian learning (SBL), Gaussian scale mixtures (GSMs) have been
used to model sparsity-inducing priors that realize a class of concave penalty
functions for the regression task in real-valued signal models. Motivated by
the relative scarcity of formal tools for SBL in complex-valued models, this
paper proposes a GSM model - the Bessel K model - that induces concave penalty
functions for the estimation of complex sparse signals. The properties of the
Bessel K model are analyzed when it is applied to Type I and Type II
estimation. This analysis reveals that, by tuning the parameters of the mixing
pdf different penalty functions are invoked depending on the estimation type
used, the value of the noise variance, and whether real or complex signals are
estimated. Using the Bessel K model, we derive a sparse estimator based on a
modification of the expectation-maximization algorithm formulated for Type II
estimation. The estimator includes as a special instance the algorithms
proposed by Tipping and Faul [1] and by Babacan et al. [2]. Numerical results
show the superiority of the proposed estimator over these state-of-the-art
estimators in terms of convergence speed, sparseness, reconstruction error, and
robustness in low and medium signal-to-noise ratio regimes.Comment: The paper provides a new comprehensive analysis of the theoretical
foundations of the proposed estimators. Minor modification of the titl
Matrix Completion-Based Channel Estimation for MmWave Communication Systems With Array-Inherent Impairments
Hybrid massive MIMO structures with reduced hardware complexity and power
consumption have been widely studied as a potential candidate for millimeter
wave (mmWave) communications. Channel estimators that require knowledge of the
array response, such as those using compressive sensing (CS) methods, may
suffer from performance degradation when array-inherent impairments bring
unknown phase errors and gain errors to the antenna elements. In this paper, we
design matrix completion (MC)-based channel estimation schemes which are robust
against the array-inherent impairments. We first design an open-loop training
scheme that can sample entries from the effective channel matrix randomly and
is compatible with the phase shifter-based hybrid system. Leveraging the
low-rank property of the effective channel matrix, we then design a channel
estimator based on the generalized conditional gradient (GCG) framework and the
alternating minimization (AltMin) approach. The resulting estimator is immune
to array-inherent impairments and can be implemented to systems with any array
shapes for its independence of the array response. In addition, we extend our
design to sample a transformed channel matrix following the concept of
inductive matrix completion (IMC), which can be solved efficiently using our
proposed estimator and achieve similar performance with a lower requirement of
the dynamic range of the transmission power per antenna. Numerical results
demonstrate the advantages of our proposed MC-based channel estimators in terms
of estimation performance, computational complexity and robustness against
array-inherent impairments over the orthogonal matching pursuit (OMP)-based CS
channel estimator.Comment: This work has been submitted to the IEEE for possible publication.
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