An Information-Based Approach to Sensor Management in Large Dynamic Networks

Abstract

Abstract-This paper addresses the problem of sensor management for a large network of agile sensors. Sensor management, as defined here, refers to the process of dynamically retasking agile sensors in response to an evolving environment. Sensors may be agile in a variety of ways, e.g., the ability to reposition, point an antenna, choose sensing mode, or waveform. The goal of sensor management in a large network is to choose actions for individual sensors dynamically so as to maximize overall network utility. An effective sensor management algorithm must combine prior knowledge, sensor models, environment models, and measurements to predict the best actions to take.Sensor management in the multiplatform setting is a challenging problem for several reasons. First, the state space required to characterize an environment is typically of very high dimension and poorly represented by a parametric form. Second, the network must simultaneously address a number of competing goals. Third, the number of potential taskings grows exponentially with the number of sensors. Finally, in low communication environments, decentralized methods are required.The approach we present in this paper addresses these challenges through a novel combination of particle filtering for nonparametric density estimation, information theory for comparing actions, and physicomimetics for computational tractability. The efficacy of the method is illustrated in a realistic surveillance application by simulation, where an unknown number of ground targets are to be detected and tracked by a network of mobile sensors.Index Terms-multiplatform sensor management, information theory, particle filtering, joint multitarget probability density, multitarget tracking.

Efficient distributed sensing using adaptive censoring based inference

“…Many authors have explored the notion of censoring agents/measurements based on some VoI metric to reduce communication cost [26][27][28][29][30][31][32][33]. Censoring has been mainly explored for centralized estimation frameworks [27,28].…”

VIGILANT+: mission objective interest groups for wireless sensor network surveillance applications

“…Selection for transmission, at each decision epoch, can be formulated in terms of state information gain using information discrimination techniques such as Rényi divergence, also known as α-divergence [24]. Utilising a state information gain approach forms a direct measure on the quality for sensor transmission selection, this being either to select transmission or not, with an expected utility calculated for each.…”

An information theoretic approach to processing management

“…In our approach, the calculation of information gain between two densities p 1 and p 0 is done using the Rényi information divergence [1], also known as the α-divergence:…”

“…Information flow is a nice metric for a number of reasons. First, it ably balances the desire to sharpen ones estimate about the number of targets with the desire to sharpen estimates about the kinematic states (i.e., position and velocity) and the classification of each target [1]. Second, information theoretic methods have been shown to bound any risk based criteria, and hence they provide a universal metric [2].…”