29,517 research outputs found
Space Time MUSIC: Consistent Signal Subspace Estimation for Wide-band Sensor Arrays
Wide-band Direction of Arrival (DOA) estimation with sensor arrays is an
essential task in sonar, radar, acoustics, biomedical and multimedia
applications. Many state of the art wide-band DOA estimators coherently process
frequency binned array outputs by approximate Maximum Likelihood, Weighted
Subspace Fitting or focusing techniques. This paper shows that bin signals
obtained by filter-bank approaches do not obey the finite rank narrow-band
array model, because spectral leakage and the change of the array response with
frequency within the bin create \emph{ghost sources} dependent on the
particular realization of the source process. Therefore, existing DOA
estimators based on binning cannot claim consistency even with the perfect
knowledge of the array response. In this work, a more realistic array model
with a finite length of the sensor impulse responses is assumed, which still
has finite rank under a space-time formulation. It is shown that signal
subspaces at arbitrary frequencies can be consistently recovered under mild
conditions by applying MUSIC-type (ST-MUSIC) estimators to the dominant
eigenvectors of the wide-band space-time sensor cross-correlation matrix. A
novel Maximum Likelihood based ST-MUSIC subspace estimate is developed in order
to recover consistency. The number of sources active at each frequency are
estimated by Information Theoretic Criteria. The sample ST-MUSIC subspaces can
be fed to any subspace fitting DOA estimator at single or multiple frequencies.
Simulations confirm that the new technique clearly outperforms binning
approaches at sufficiently high signal to noise ratio, when model mismatches
exceed the noise floor.Comment: 15 pages, 10 figures. Accepted in a revised form by the IEEE Trans.
on Signal Processing on 12 February 1918. @IEEE201
Uplink CoMP under a Constrained Backhaul and Imperfect Channel Knowledge
Coordinated Multi-Point (CoMP) is known to be a key technology for next
generation mobile communications systems, as it allows to overcome the burden
of inter-cell interference. Especially in the uplink, it is likely that
interference exploitation schemes will be used in the near future, as they can
be used with legacy terminals and require no or little changes in
standardization. Major drawbacks, however, are the extent of additional
backhaul infrastructure needed, and the sensitivity to imperfect channel
knowledge. This paper jointly addresses both issues in a new framework
incorporating a multitude of proposed theoretical uplink CoMP concepts, which
are then put into perspective with practical CoMP algorithms. This
comprehensive analysis provides new insight into the potential usage of uplink
CoMP in next generation wireless communications systems.Comment: Submitted to IEEE Transactions on Wireless Communications in February
201
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
The significance of the integrated Sachs-Wolfe effect revisited
We revisit the state of the integrated Sachs-Wolfe (ISW) effect measurements
in light of newly available data and address criticisms about the measurements
which have recently been raised. We update the data set previously assembled by
Giannantonio et al. to include new data releases for both the cosmic microwave
background (CMB) and the large-scale structure (LSS) of the Universe. We find
that our updated results are consistent with previous measurements. By fitting
a single template amplitude, we now obtain a combined significance of the ISW
detection at the 4.4 sigma level, which fluctuates by 0.4 sigma when
alternative data cuts and analysis assumptions are considered. We also make new
tests for systematic contaminations of the data, focusing in particular on the
issues raised by Sawangwit et al. Amongst them, we address the rotation test,
which aims at checking for possible systematics by correlating pairs of
randomly rotated maps. We find results consistent with the expected data
covariance, no evidence for enhanced correlation on any preferred axis of
rotation, and therefore no indication of any additional systematic
contamination. We publicly release the results, the covariance matrix, and the
sky maps used to obtain them.Comment: 19 pages, 10 figures. MNRAS in pres
Dynamic Decomposition of Spatiotemporal Neural Signals
Neural signals are characterized by rich temporal and spatiotemporal dynamics
that reflect the organization of cortical networks. Theoretical research has
shown how neural networks can operate at different dynamic ranges that
correspond to specific types of information processing. Here we present a data
analysis framework that uses a linearized model of these dynamic states in
order to decompose the measured neural signal into a series of components that
capture both rhythmic and non-rhythmic neural activity. The method is based on
stochastic differential equations and Gaussian process regression. Through
computer simulations and analysis of magnetoencephalographic data, we
demonstrate the efficacy of the method in identifying meaningful modulations of
oscillatory signals corrupted by structured temporal and spatiotemporal noise.
These results suggest that the method is particularly suitable for the analysis
and interpretation of complex temporal and spatiotemporal neural signals
Block-Online Multi-Channel Speech Enhancement Using DNN-Supported Relative Transfer Function Estimates
This work addresses the problem of block-online processing for multi-channel
speech enhancement. Such processing is vital in scenarios with moving speakers
and/or when very short utterances are processed, e.g., in voice assistant
scenarios. We consider several variants of a system that performs beamforming
supported by DNN-based voice activity detection (VAD) followed by
post-filtering. The speaker is targeted through estimating relative transfer
functions between microphones. Each block of the input signals is processed
independently in order to make the method applicable in highly dynamic
environments. Owing to the short length of the processed block, the statistics
required by the beamformer are estimated less precisely. The influence of this
inaccuracy is studied and compared to the processing regime when recordings are
treated as one block (batch processing). The experimental evaluation of the
proposed method is performed on large datasets of CHiME-4 and on another
dataset featuring moving target speaker. The experiments are evaluated in terms
of objective and perceptual criteria (such as signal-to-interference ratio
(SIR) or perceptual evaluation of speech quality (PESQ), respectively).
Moreover, word error rate (WER) achieved by a baseline automatic speech
recognition system is evaluated, for which the enhancement method serves as a
front-end solution. The results indicate that the proposed method is robust
with respect to short length of the processed block. Significant improvements
in terms of the criteria and WER are observed even for the block length of 250
ms.Comment: 10 pages, 8 figures, 4 tables. Modified version of the article
accepted for publication in IET Signal Processing journal. Original results
unchanged, additional experiments presented, refined discussion and
conclusion
Quantum information with Gaussian states
Quantum optical Gaussian states are a type of important robust quantum states
which are manipulatable by the existing technologies. So far, most of the
important quantum information experiments are done with such states, including
bright Gaussian light and weak Gaussian light. Extending the existing results
of quantum information with discrete quantum states to the case of continuous
variable quantum states is an interesting theoretical job. The quantum Gaussian
states play a central role in such a case. We review the properties and
applications of Gaussian states in quantum information with emphasis on the
fundamental concepts, the calculation techniques and the effects of
imperfections of the real-life experimental setups.
Topics here include the elementary properties of Gaussian states and relevant
quantum information device, entanglement-based quantum tasks such as quantum
teleportation, quantum cryptography with weak and strong Gaussian states and
the quantum channel capacity, mathematical theory of quantum entanglement and
state estimation for Gaussian states.Comment: 170 pages. Minors of the published version are corrected and listed
in the Acknowledgement part of this versio
- …