1,236 research outputs found
A Unified Approach to Sparse Signal Processing
A unified view of sparse signal processing is presented in tutorial form by
bringing together various fields. For each of these fields, various algorithms
and techniques, which have been developed to leverage sparsity, are described
succinctly. The common benefits of significant reduction in sampling rate and
processing manipulations are revealed.
The key applications of sparse signal processing are sampling, coding,
spectral estimation, array processing, component analysis, and multipath
channel estimation. In terms of reconstruction algorithms, linkages are made
with random sampling, compressed sensing and rate of innovation. The redundancy
introduced by channel coding in finite/real Galois fields is then related to
sampling with similar reconstruction algorithms. The methods of Prony,
Pisarenko, and MUSIC are next discussed for sparse frequency domain
representations. Specifically, the relations of the approach of Prony to an
annihilating filter and Error Locator Polynomials in coding are emphasized; the
Pisarenko and MUSIC methods are further improvements of the Prony method. Such
spectral estimation methods is then related to multi-source location and DOA
estimation in array processing. The notions of sparse array beamforming and
sparse sensor networks are also introduced. Sparsity in unobservable source
signals is also shown to facilitate source separation in SCA; the algorithms
developed in this area are also widely used in compressed sensing. Finally, the
multipath channel estimation problem is shown to have a sparse formulation;
algorithms similar to sampling and coding are used to estimate OFDM channels.Comment: 43 pages, 40 figures, 15 table
Joint DOA Estimation and Array Calibration Using Multiple Parametric Dictionary Learning
This letter proposes a multiple parametric dictionary learning algorithm for
direction of arrival (DOA) estimation in presence of array gain-phase error and
mutual coupling. It jointly solves both the DOA estimation and array
imperfection problems to yield a robust DOA estimation in presence of array
imperfection errors and off-grid. In the proposed method, a multiple parametric
dictionary learning-based algorithm with an steepest-descent iteration is used
for learning the parametric perturbation matrices and the steering matrix
simultaneously. It also exploits the multiple snapshots information to enhance
the performance of DOA estimation. Simulation results show the efficiency of
the proposed algorithm when both off-grid problem and array imperfection exist
A Unified Performance Analysis of Subspace-Based DOA Estimation Algorithms in Array Signal Processing
In the last decade, the subspace approach has found prominence in the problem of estimating directions of arrival using an array of sensors. Many subspace methods have been proposed and improved; the most attractive ones among these are MUSIC, Min-Norm, State-Space Realization (TAM) and ESPRIT. However, performance analyses are required for justifying and comparing these methods before applying them. Early performance justifications and comparisons were based on simulations. In recent years, many excellent analytical studies have been reported, but these studies have one or more of the following restrictions: (i) assume asymptotic measurements, (ii) analyze some specific parameter perturbation directly instead of through the perturbation of the appropriate subspace, (iii) evaluate individual algorithms using different approximations (so it is hard to compare the analyses of different methods), (iv) involve complicated mathematics and statistics which result in difficult expressions. In our attempt to obtain a unified, nonasymptotic analysis to subspace processing algorithms in a greatly simplified and self-contained fashion, we classify these algorithms into category by the subspace they use - orthogonalsubspace processing and signal-subspace processing. We then derive expressions for the first-order perturbation of the signal and orthogonal subspaces using a matrix approximation technique. These formulas provides a common foundation for our analysis of all the DOA estimation algorithms mentioned above. define three approaches by the numerical procedure these algorithms exploit - extrema-searching, polynomial-rooting approach, matrix-shifting approach. We establish a common model for each approach and analyze these common models (instead of individual algorithms), and specialize the results for each algorithm. provide a first-order relationship between subspace perturbations and direction-of-arrival perturbations. use the perturbation formulas to derive variance expressions for DOA estimates for all the algorithms. We make the comparisons and discussions among these algorithms and approaches with our theoretical prediction and numerical simulations.
The tractable formulas derived in this analysis provide insight into the performance of the algorithms. Simulations verify the analysis
A Simplified Sub-Nyquist Receiver Architecture for Joint DOA and Frequency Estimation
Joint estimation of carrier frequency and direction of arrival (DOA) for
multiple signals has been found in many practical applications such as
Cognitive Radio (CR). However, Nyquist sampling mechanism is costly or
implemented due to wide spectrum range. Taking advantage of sub-Nyquist
sampling technology, some array receiver architectures are proposed to realize
joint estimation of carrier frequency and DOA. To further decrease equivalent
sampling rate and hardware complexity, we propose a simplifying receiver
architecture based on our previous work. We come up with joint DOA and
frequency estimation algorithms for the novel architecture. The simulations
demonstrate that the receiver architecture and the proposed approaches are
feasible.Comment: arXiv admin note: text overlap with arXiv:1604.0503
Sparse Bayesian learning with uncertainty models and multiple dictionaries
Sparse Bayesian learning (SBL) has emerged as a fast and competitive method
to perform sparse processing. The SBL algorithm, which is developed using a
Bayesian framework, approximately solves a non-convex optimization problem
using fixed point updates. It provides comparable performance and is
significantly faster than convex optimization techniques used in sparse
processing. We propose a signal model which accounts for dictionary mismatch
and the presence of errors in the weight vector at low signal-to-noise ratios.
A fixed point update equation is derived which incorporates the statistics of
mismatch and weight errors. We also process observations from multiple
dictionaries. Noise variances are estimated using stochastic maximum
likelihood. The derived update equations are studied quantitatively using
beamforming simulations applied to direction-of-arrival (DoA). Performance of
SBL using single- and multi-frequency observations, and in the presence of
aliasing, is evaluated. SwellEx-96 experimental data demonstrates qualitatively
the advantages of SBL.Comment: 11 pages, 8 figure
Coarrays, MUSIC, and the Cram\'er Rao Bound
Sparse linear arrays, such as co-prime arrays and nested arrays, have the
attractive capability of providing enhanced degrees of freedom. By exploiting
the coarray structure, an augmented sample covariance matrix can be constructed
and MUSIC (MUtiple SIgnal Classification) can be applied to identify more
sources than the number of sensors. While such a MUSIC algorithm works quite
well, its performance has not been theoretically analyzed. In this paper, we
derive a simplified asymptotic mean square error (MSE) expression for the MUSIC
algorithm applied to the coarray model, which is applicable even if the source
number exceeds the sensor number. We show that the directly augmented sample
covariance matrix and the spatial smoothed sample covariance matrix yield the
same asymptotic MSE for MUSIC. We also show that when there are more sources
than the number of sensors, the MSE converges to a positive value instead of
zero when the signal-to-noise ratio (SNR) goes to infinity. This finding
explains the "saturation" behavior of the coarray-based MUSIC algorithms in the
high SNR region observed in previous studies. Finally, we derive the
Cram\'er-Rao bound (CRB) for sparse linear arrays, and conduct a numerical
study of the statistical efficiency of the coarray-based estimator.
Experimental results verify theoretical derivations and reveal the complex
efficiency pattern of coarray-based MUSIC algorithms.Comment: Revised Corollary 2. Added Fig.
Joint DOA and Frequency Estimation with Sub-Nyquist Sampling
In this paper, to jointly estimate the frequency and the
direction-of-arrival(DOA) of the narrowband far-field signals, a novel array
receiver architecture is presented by the concept of the sub-Nyquist sampling
techniques. In particular, our contribution is threefold. i) First, we propose
a time-space union signal reception model for receiving array signals, where
the sub-Nyquist sampling techniques and arbitrary array geometries are employed
to decrease the time-domain sampling rate and improve the DOA estimation
accuracy. A better joint estimation is obtained in the higher time-space union
space. ii) Second, two joint estimation algorithms are proposed for the
receiving model. One is based on a trilinear decomposition from the third-order
tensor theory and the other is based on subspace decomposition. iii) Third, we
derive the corresponding Cram\'er\text{-}Rao Bound (CRB) for frequency and DOA
estimates. In the case of the branch number of our architecture is equal to the
reduction factor of the sampling rate, it is observed that the CRB is robust in
terms of the number of signals, while the CRB based on the Nyquist sampling
scheme will increase with respect to the number of signals. In addition, the
new steer vectors of the union time-space model are completely uncorrelated
under the limited number of sensors, which improves the estimation performance.
Furthermore, the simulation results demonstrate that our estimates via the
receiver architecture associated with the proposed algorithms closely match the
CRB according to the noise levels, the branch number and the source number as
well
A DoA Estimation Based Robust Beam Forming Method for UAV-BS Communication
High data rate communication with Unmanned Aerial Vehicles (UAV) is of
growing demand among industrial and commercial applications since the last
decade. In this paper, we investigate enhancing beam forming performance based
on signal Direction of Arrival (DoA) estimation to support UAV-cellular network
communication. We first study UAV fast moving scenario where we found that
drone's mobility cause degradation of beam forming algorithm performance. Then,
we propose a DoA estimation algorithm and a steering vector adaptive receiving
beam forming method. The DoA estimation algorithm is of high precision with low
computational complexity. Also it enables a beam former to timely adjust
steering vector value in calculating beam forming weight. Simulation results
show higher SINR performance and more stability of proposed method than
traditional method based on Multiple Signal Classification (MUSIC) DoA
estimation algorithm.Comment: We would like to make some variations to the simulation result
Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas
Utilization of directional antennas is a promising solution for efficient
spectrum sensing and accurate source localization and tracking. Spectrum
sensors equipped with directional antennas should constantly scan the space in
order to track emitting sources and discover new activities in the area of
interest. In this paper, we propose a new formulation that unifies
received-signal-strength (RSS) and direction of arrival (DoA) in a compressive
sensing (CS) framework. The underlying CS measurement matrix is a function of
beamforming vectors of sensors and is referred to as the propagation matrix.
Comparing to the omni-directional antenna case, our employed propagation matrix
provides more incoherent projections, an essential factor in the compressive
sensing theory. Based on the new formulation, we optimize the antenna beams,
enhance spectrum sensing efficiency, track active primary users accurately and
monitor spectrum activities in an area of interest. In many practical scenarios
there is no fusion center to integrate received data from spectrum sensors. We
propose the distributed version of our algorithm for such cases. Experimental
results show a significant improvement in source localization accuracy,
compared with the scenario when sensors are equipped with omni-directional
antennas. Applicability of the proposed framework for dynamic radio cartography
is shown. Moreover, comparing the estimated dynamic RF map over time with the
ground truth demonstrates the effectiveness of our proposed method for accurate
signal estimation and recovery.Comment: SECON 2019 workshop on Edge Computing for Cyber Physical System
Iterative Sparse Asymptotic Minimum Variance Based Approaches for Array Processing
This paper presents a series of user parameter-free iterative Sparse
Asymptotic Minimum Variance (SAMV) approaches for array processing applications
based on the asymptotically minimum variance (AMV) criterion. With the
assumption of abundant snapshots in the direction-of-arrival (DOA) estimation
problem, the signal powers and noise variance are jointly estimated by the
proposed iterative AMV approach, which is later proved to coincide with the
Maximum Likelihood (ML) estimator. We then propose a series of power-based
iterative SAMV approaches, which are robust against insufficient snapshots,
coherent sources and arbitrary array geometries. Moreover, to overcome the
direction grid limitation on the estimation accuracy, the SAMV-Stochastic ML
(SAMV-SML) approaches are derived by explicitly minimizing a closed form
stochastic ML cost function with respect to one scalar parameter, eliminating
the need of any additional grid refinement techniques. To assist the
performance evaluation, approximate solutions to the SAMV approaches are also
provided at high signal-to-noise ratio (SNR) and low SNR, respectively.
Finally, numerical examples are generated to compare the performance of the
proposed approaches with existing approaches.Comment: Abeida Habti, Qilin Zhang, Jian Li, and Nadjim Merabtine. "Iterative
sparse asymptotic minimum variance based approaches for array processing."
IEEE Transactions on Signal Processing 61, no. 4 (2013): 933-94
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