272 research outputs found
Mean acquisition time analysis of fixed-step serial search algorithms
Cataloged from PDF version of article.In this paper, mean acquisition time (MAT) analysis
of fixed-step serial search (FSSS) algorithms is presented. First,
it is shown that the MAT of an FSSS algorithm can be obtained
from that of a conventional serial search (CSS) algorithm after
a certain mapping of the uncertainty region. Then, a generic
formula for the MAT of FSSS algorithms is derived, which
is valid for both dense and sparse channel environments. In
addition, MAT formulas for high signal-to-noise ratio scenarios,
for large uncertainty regions, and for dense channels are obtained
as special cases of the generic solution. Finally, simulation results
are presented to verify the analysis and to investigate the factors
that affect the optimal step size for FSSS algorithms
Theoretical limits for estimation of periodic movements in pulse-based UWB systems
Cataloged from PDF version of article.In this paper, Cramer-Rao lower bounds (CRLBs) for
estimation of signal parameters related to periodically moving objects
in pulse-based ultra-wideband (UWB) systems are presented.
The results also apply to estimation of vital parameters, such as
respiration rate, using UWB signals. In addition to obtaining the
CRLBs, suboptimal estimation algorithms are also presented.
First, a single-path channel with additive white Gaussian noise
is considered, and closed-form CRLB expressions are obtained
for sinusoidal object movements. Also, a two-step suboptimal
algorithm is proposed, which is based on time delay estimation
via matched filtering followed by least-squares estimation, and its
asymptotic optimality property is shown in the limit of certain
system parameters. Then, a multipath environment is considered,
and exact and approximate CRLB expressions are derived. Moreover,
suboptimal schemes for parameter estimation are studied.
Simulation studies are performed for the estimation of respiration
rates in order to evaluate the lower bounds and performance of
the suboptimal algorithms for realistic system parameters
Noise Enhanced M-ary Composite Hypothesis-Testing in the Presence of Partial Prior Information
Cataloged from PDF version of article.In this correspondence, noise enhanced detection is studied for M-ary composite hypothesis-testing problems in the presence of partial prior information. Optimal additive noise is obtained according to two criteria, which assume a uniform distribution (Criterion 1) or the least-favorable distribution (Criterion 2) for the unknown priors. The statistical characterization of the optimal noise is obtained for each criterion. Specifically, it is shown that the optimal noise can be represented by a constant signal level or by a randomization of a finite number of signal levels according to Criterion 1 and Criterion 2, respectively. In addition, the cases of unknown parameter distributions under some composite hypotheses are considered, and upper bounds on the risks are obtained. Finally, a detection example is provided in order to investigate the theoretical results. © 2010 IEEE
On the Improvability and Nonimprovability of Detection via Additional Independent Noise
Cataloged from PDF version of article.Addition of independent noise to measurements
can improve performance of some suboptimal detectors under
certain conditions. In this letter, sufficient conditions under which
the performance of a suboptimal detector cannot be enhanced
by additional independent noise are derived according to the
Neyman–Pearson criterion. Also, sufficient conditions are obtained
to specify when the detector performance can be improved.
In addition to a generic condition, various explicit sufficient
conditions are proposed for easy evaluation of improvability.
Finally, a numerical example is presented and the practicality of
the proposed conditions is discussed
On the Performance of Single-Threshold Detectors for Binary Communications in the Presence of Gaussian Mixture Noise
Cataloged from PDF version of article.In this paper, probability of error performance of
single-threshold detectors is studied for binary communications
systems in the presence of Gaussian mixture noise. First, suffi-
cient conditions are proposed to specify when the sign detector
is (not) an optimal detector among all the single-threshold
detectors. Then, a monotonicity property of the error probability
is derived for the optimal single-threshold detector. In addition,
a theoretical limit is obtained on the maximum ratio between
the average probabilities of error for the sign detector and the
optimal single-threshold detector. Finally, numerical examples
are presented to investigate the theoretical results
Detector Randomization and Stochastic Signaling for Minimum Probability of Error Receivers
Cataloged from PDF version of article.Optimal receiver design is studied for a communications
system in which both detector randomization and stochastic
signaling can be performed. First, it is proven that stochastic signaling
without detector randomization cannot achieve a smaller
average probability of error than detector randomization with
deterministic signaling for the same average power constraint
and noise statistics. Then, it is shown that the optimal receiver
design results in a randomization between at most two maximum
a-posteriori probability (MAP) detectors corresponding to two
deterministic signal vectors. Numerical examples are provided
to explain the results
Noise enhanced hypothesis-testing according to restricted Neyman-Pearson criterion
Cataloged from PDF version of article.Noise enhanced hypothesis-testing is studied according to the restricted Neyman-Pearson (NP) criterion. First, a problem formulation is presented for obtaining the optimal probability distribution of additive noise in the restricted NP framework. Then, sufficient conditions for improvability and nonimprovability are derived in order to specify if additive noise can or cannot improve detection performance over scenarios in which no additive noise is employed. Also, for the special case of a finite number of possible parameter values under each hypothesis, it is shown that the optimal additive noise can be represented by a discrete random variable with a certain number of point masses. In addition, particular improvability conditions are derived for that special case. Finally, theoretical results are provided for a numerical example and improvements via additive noise are illustrated. © 2013 Elsevier Inc
Accurate Positioning in Ultra-Wideband Systems
Cataloged from PDF version of article.Accurate positioning systems can be realized via ultra-wideband signals due to their high time resolution. In this article, position estimation is studied for UWB systems. After a brief introduction to UWB signals and their positioning applications,
two-step positioning systems are investigated from a UWB perspective. It is observed that time-based positioning is well suited for UWB systems. Then time-based UWB ranging is studied in detail, and the main challenges, theoretical limits, and range estimation algorithms are presented. Performance of some practical time-based ranging algorithms is investigated and compared against the maximum likelihood estimator and the theoretical limits. The trade-off between complexity and accuracy is .observe
Cost minimization of measurement devices under estimation accuracy constraints in the prsence of Gaussian noise
Cataloged from PDF version of article.Novel convex measurement cost minimization problems are proposed based on various estimation
accuracy constraints for a linear system subject to additive Gaussian noise. Closed form solutions are
obtained in the case of an invertible system matrix. In addition, the effects of system matrix uncertainty
are studied both from a generic perspective and by employing a specific uncertainty model. The results
are extended to the Bayesian estimation framework by treating the unknown parameters as Gaussian
distributed random variables. Numerical examples are presented to discuss the theoretical results in
detail.
© 2012 Elsevier Inc. All rights reserved
Ranging in a single-input multiple-output (SIMO) system
Cataloged from PDF version of article.In this letter, optimal ranging in a single-input
multiple-output (SIMO) system is studied. The theoretical limits
on the accuracy of time-of-arrival (TOA) (equivalently, range)
estimation are calculated in terms of the Cramer-Rao lower
bound (CRLB). Unlike the conventional phased array antenna
structure, a more generic fading model is employed, which allows
for the analysis of spatial diversity gains from the viewpoint of
a ranging system. In addition to the optimal solution, a two-step
suboptimal range estimator is proposed, and its performance is
compared with the CRLBs
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