13,188 research outputs found
Sequential Detection with Mutual Information Stopping Cost
This paper formulates and solves a sequential detection problem that involves
the mutual information (stochastic observability) of a Gaussian process
observed in noise with missing measurements. The main result is that the
optimal decision is characterized by a monotone policy on the partially ordered
set of positive definite covariance matrices. This monotone structure implies
that numerically efficient algorithms can be designed to estimate and implement
monotone parametrized decision policies.The sequential detection problem is
motivated by applications in radar scheduling where the aim is to maintain the
mutual information of all targets within a specified bound. We illustrate the
problem formulation and performance of monotone parametrized policies via
numerical examples in fly-by and persistent-surveillance applications involving
a GMTI (Ground Moving Target Indicator) radar
Active sequential hypothesis testing
Consider a decision maker who is responsible to dynamically collect
observations so as to enhance his information about an underlying phenomena of
interest in a speedy manner while accounting for the penalty of wrong
declaration. Due to the sequential nature of the problem, the decision maker
relies on his current information state to adaptively select the most
``informative'' sensing action among the available ones. In this paper, using
results in dynamic programming, lower bounds for the optimal total cost are
established. The lower bounds characterize the fundamental limits on the
maximum achievable information acquisition rate and the optimal reliability.
Moreover, upper bounds are obtained via an analysis of two heuristic policies
for dynamic selection of actions. It is shown that the first proposed heuristic
achieves asymptotic optimality, where the notion of asymptotic optimality, due
to Chernoff, implies that the relative difference between the total cost
achieved by the proposed policy and the optimal total cost approaches zero as
the penalty of wrong declaration (hence the number of collected samples)
increases. The second heuristic is shown to achieve asymptotic optimality only
in a limited setting such as the problem of a noisy dynamic search. However, by
considering the dependency on the number of hypotheses, under a technical
condition, this second heuristic is shown to achieve a nonzero information
acquisition rate, establishing a lower bound for the maximum achievable rate
and error exponent. In the case of a noisy dynamic search with size-independent
noise, the obtained nonzero rate and error exponent are shown to be maximum.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1144 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Communication under Strong Asynchronism
We consider asynchronous communication over point-to-point discrete
memoryless channels. The transmitter starts sending one block codeword at an
instant that is uniformly distributed within a certain time period, which
represents the level of asynchronism. The receiver, by means of a sequential
decoder, must isolate the message without knowing when the codeword
transmission starts but being cognizant of the asynchronism level A. We are
interested in how quickly can the receiver isolate the sent message,
particularly in the regime where A is exponentially larger than the codeword
length N, which we refer to as `strong asynchronism.'
This model of sparse communication may represent the situation of a sensor
that remains idle most of the time and, only occasionally, transmits
information to a remote base station which needs to quickly take action.
The first result shows that vanishing error probability can be guaranteed as
N tends to infinity while A grows as Exp(N*k) if and only if k does not exceed
the `synchronization threshold,' a constant that admits a simple closed form
expression, and is at least as large as the capacity of the synchronized
channel. The second result is the characterization of a set of achievable
strictly positive rates in the regime where A is exponential in N, and where
the rate is defined with respect to the expected delay between the time
information starts being emitted until the time the receiver makes a decision.
As an application of the first result we consider antipodal signaling over a
Gaussian channel and derive a simple necessary condition between A, N, and SNR
for achieving reliable communication.Comment: 26 page
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