52,214 research outputs found
Performances of conformal and planar arrays
Static and dynamic deformations can have a severe impact on the performance of conformal antennas on aircrafts and other vehicles. Therefore it is essential to study the different deformation and vibration mechanisms and their influence on the antenna's radiation pattern. This presentation gives an overview of different approaches concerning electromagnetic modelling of array antennas and investigations on antenna deformations presented in the scope of TG20
Performance Analysis of the Decentralized Eigendecomposition and ESPRIT Algorithm
In this paper, we consider performance analysis of the decentralized power
method for the eigendecomposition of the sample covariance matrix based on the
averaging consensus protocol. An analytical expression of the second order
statistics of the eigenvectors obtained from the decentralized power method
which is required for computing the mean square error (MSE) of subspace-based
estimators is presented. We show that the decentralized power method is not an
asymptotically consistent estimator of the eigenvectors of the true measurement
covariance matrix unless the averaging consensus protocol is carried out over
an infinitely large number of iterations. Moreover, we introduce the
decentralized ESPRIT algorithm which yields fully decentralized
direction-of-arrival (DOA) estimates. Based on the performance analysis of the
decentralized power method, we derive an analytical expression of the MSE of
DOA estimators using the decentralized ESPRIT algorithm. The validity of our
asymptotic results is demonstrated by simulations.Comment: 18 pages, 5 figures, submitted for publication in IEEE Transactions
on Signal Processin
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
Efficient Beam Alignment in Millimeter Wave Systems Using Contextual Bandits
In this paper, we investigate the problem of beam alignment in millimeter
wave (mmWave) systems, and design an optimal algorithm to reduce the overhead.
Specifically, due to directional communications, the transmitter and receiver
beams need to be aligned, which incurs high delay overhead since without a
priori knowledge of the transmitter/receiver location, the search space spans
the entire angular domain. This is further exacerbated under dynamic conditions
(e.g., moving vehicles) where the access to the base station (access point) is
highly dynamic with intermittent on-off periods, requiring more frequent beam
alignment and signal training. To mitigate this issue, we consider an online
stochastic optimization formulation where the goal is to maximize the
directivity gain (i.e., received energy) of the beam alignment policy within a
time period. We exploit the inherent correlation and unimodality properties of
the model, and demonstrate that contextual information improves the
performance. To this end, we propose an equivalent structured Multi-Armed
Bandit model to optimally exploit the exploration-exploitation tradeoff. In
contrast to the classical MAB models, the contextual information makes the
lower bound on regret (i.e., performance loss compared with an oracle policy)
independent of the number of beams. This is a crucial property since the number
of all combinations of beam patterns can be large in transceiver antenna
arrays, especially in massive MIMO systems. We further provide an
asymptotically optimal beam alignment algorithm, and investigate its
performance via simulations.Comment: To Appear in IEEE INFOCOM 2018. arXiv admin note: text overlap with
arXiv:1611.05724 by other author
CABE : a cloud-based acoustic beamforming emulator for FPGA-based sound source localization
Microphone arrays are gaining in popularity thanks to the availability of low-cost microphones. Applications including sonar, binaural hearing aid devices, acoustic indoor localization techniques and speech recognition are proposed by several research groups and companies. In most of the available implementations, the microphones utilized are assumed to offer an ideal response in a given frequency domain. Several toolboxes and software can be used to obtain a theoretical response of a microphone array with a given beamforming algorithm. However, a tool facilitating the design of a microphone array taking into account the non-ideal characteristics could not be found. Moreover, generating packages facilitating the implementation on Field Programmable Gate Arrays has, to our knowledge, not been carried out yet. Visualizing the responses in 2D and 3D also poses an engineering challenge. To alleviate these shortcomings, a scalable Cloud-based Acoustic Beamforming Emulator (CABE) is proposed. The non-ideal characteristics of microphones are considered during the computations and results are validated with acoustic data captured from microphones. It is also possible to generate hardware description language packages containing delay tables facilitating the implementation of Delay-and-Sum beamformers in embedded hardware. Truncation error analysis can also be carried out for fixed-point signal processing. The effects of disabling a given group of microphones within the microphone array can also be calculated. Results and packages can be visualized with a dedicated client application. Users can create and configure several parameters of an emulation, including sound source placement, the shape of the microphone array and the required signal processing flow. Depending on the user configuration, 2D and 3D graphs showing the beamforming results, waterfall diagrams and performance metrics can be generated by the client application. The emulations are also validated with captured data from existing microphone arrays.</jats:p
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