4 research outputs found
A Potential Game Approach for Information-Maximizing Cooperative Planning of Sensor Networks
This paper presents a potential game approach for distributed cooperative
selection of informative sensors, when the goal is to maximize the mutual
information between the measurement variables and the quantities of interest.
It is proved that a local utility function defined by the conditional mutual
information of an agent conditioned on the other agents' sensing decisions
leads to a potential game with the global potential being the original mutual
information of the cooperative planning problem. The joint strategy fictitious
play method is then applied to obtain a distributed solution that provably
converges to a pure strategy Nash equilibrium. Two numerical examples on
simplified weather forecasting and range-only target tracking verify
convergence and performance characteristics of the proposed game-theoretic
approach.Comment: 9 pages, 4 figures, submitted to IEEE Transactions on Control Systems
Technolog
Potential Game-Based Non-Myopic Sensor Network Planning for Multi-Target Tracking
This paper presents a potential game-based method for non-myopic planning of
mobile sensor networks in the context of target tracking. The planning
objective is to select the sequence of sensing points over more than one future
time steps to maximize information about the target states. This multi-step
lookahead scheme is studied to overcome getting trapped at local information
maximum when there are gaps in sensing coverage due to constraints on the
sensor platform mobility or limitations in sensing capabilities. However, the
long-term planning becomes computationally intractable as the length of planing
horizon increases. This work develops a gametheoretic approach to address the
computational challenges. The main contributions of this paper are twofold: (a)
to formulate a non-myopic planning problem for tracking multiple targets into a
potential game, the size of which linearly increases as the number of planning
steps (b) to design a learning algorithm exploiting the joint strategy
fictitious play and dynamic programming, which overcomes the gaps in sensing
coverage. Numerical examples of multi-target tracking demonstrate that the
proposed method gives better estimation performance than myopic planning and is
computationally tractable.Comment: Copyright 2018 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or
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this work in other work
Efficient sensor network planning method using approximate potential game
This paper addresses information-based sensing point selection from a set of
possible sensing locations, which determines a set of measurement points
maximizing the mutual information between the sensor measurements and the
variables of interest. A potential game approach has been applied to addressing
distributed implementation of decision making for cooperative sensor planning.
When a sensor network involves a large number of sensing agents, the local
utility function for a sensing agent is hard to compute, because the local
utility function depends on the other agents' decisions while each sensing
agent is inherently faced with limitations in both its communication and
computational capabilities. Accordingly, a local utility function for each
agent should be approximated to accommodate limitations in information
gathering and processing. We propose an approximation method for a local
utility function using only a portion of the decisions of other agents. The
part of the decisions that each agent considers is called the neighboring set
for the agent. The error induced by the approximation is also analyzed, and to
keep the error small we propose a neighbor selection algorithm that chooses the
neighbor set for each agent in a greedy way. The selection algorithm is based
on the correlation information between one agent's measurement selection and
the other agents' selections. Futhermore, we show that a game with an
approximate local utility function has an -equilibrium and the set of
the equilibria include the Nash equilibrium of the original potential game. We
demonstrate the validity of our approximation method through two numerical
examples on simplified weather forecasting and multi-target tracking.Comment: 24 pages, 4 figures, submitted to IJDSN(International Journal of
Distributed Sensor Networks
SUBMITTED TO IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 1 Efficient Targeting of Sensor Networks for Large-Scale Systems
This paper proposes an efficient approach to an observation targeting problem that is complicated by a combinatorial number of targeting choices and the large dimension of the system state, when the goal is to minimize the uncertainty in some quantities of interest. The primary improvements in the efficiency are obtained by computing the impact of each possible measurement choice on the uncertainty reduction backwards. This backward method provides an equivalent solution to a traditional forward approach under some standard assumptions, while removing the requirement of calculating a combinatorial number of covariance updates. A key contribution of this paper is to prove that the backward approach operates never slower than the forward approach, and that it works significantly faster than the forward one for ensemble-based representations. The primary benefits are shown on a simplified weather problem using the Lorenz-95 model. I