2,289 research outputs found
Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics
This paper focuses on the problem of recursive nonlinear least squares
parameter estimation in multi-agent networks, in which the individual agents
observe sequentially over time an independent and identically distributed
(i.i.d.) time-series consisting of a nonlinear function of the true but unknown
parameter corrupted by noise. A distributed recursive estimator of the
\emph{consensus} + \emph{innovations} type, namely , is
proposed, in which the agents update their parameter estimates at each
observation sampling epoch in a collaborative way by simultaneously processing
the latest locally sensed information~(\emph{innovations}) and the parameter
estimates from other agents~(\emph{consensus}) in the local neighborhood
conforming to a pre-specified inter-agent communication topology. Under rather
weak conditions on the connectivity of the inter-agent communication and a
\emph{global observability} criterion, it is shown that at every network agent,
the proposed algorithm leads to consistent parameter estimates. Furthermore,
under standard smoothness assumptions on the local observation functions, the
distributed estimator is shown to yield order-optimal convergence rates, i.e.,
as far as the order of pathwise convergence is concerned, the local parameter
estimates at each agent are as good as the optimal centralized nonlinear least
squares estimator which would require access to all the observations across all
the agents at all times. In order to benchmark the performance of the proposed
distributed estimator with that of the centralized nonlinear
least squares estimator, the asymptotic normality of the estimate sequence is
established and the asymptotic covariance of the distributed estimator is
evaluated. Finally, simulation results are presented which illustrate and
verify the analytical findings.Comment: 28 pages. Initial Submission: Feb. 2016, Revised: July 2016,
Accepted: September 2016, To appear in IEEE Transactions on Signal and
Information Processing over Networks: Special Issue on Inference and Learning
over Network
Multiresolution optimal interpolation and statistical analysis of topex/podeidon satellite altimetry
Includes bibliographical references (p. 18-20).Supported by the Office of Naval Research. N00014-91-J-1004 Supported by the Draper Laboratory. DL-H-467133 Supported by the Air Force Office of Scientific Research. F49620-92-J-0002 Supported by the Natural Sciences and Engineering Research Council of Canada. NSERC-67 Supported by NASA. NAGW-1048Paul W. Fieguth ... [et al.]
Asymptotic Signal Detection Rates with 1-bit Array Measurements
This work considers detecting the presence of a band-limited random radio
source using an antenna array featuring a low-complexity digitization process
with single-bit output resolution. In contrast to high-resolution
analog-to-digital conversion, such a direct transformation of the analog radio
measurements to a binary representation can be implemented hardware and
energy-efficient. However, the probabilistic model of the binary receive data
becomes challenging. Therefore, we first consider the Neyman-Pearson test
within generic exponential families and derive the associated analytic
detection rate expressions. Then we use a specific replacement model for the
binary likelihood and study the achievable detection performance with 1- bit
radio array measurements. As an application, we explore the capability of a
low-complexity GPS spectrum monitoring system with different numbers of
antennas and different observation intervals. Results show that with a moderate
amount of binary sensors it is possible to reliably perform the monitoring
task
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