Sensor scheduling for target tracking: A Monte Carlo sampling approach

He, Ying; Chong, Edwin K. P.

doi:10.1016/j.dsp.2005.02.005

Cited by 81 publications

(86 citation statements)

References 16 publications

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“…In each simulation run, the object was initially Next, we consider a network of 10 sensors where object locations are located on integers from 1 to 21. The observation for each sensor is continuous as in (6). For every object state and every scheduling action in the reduced control space, we sample 50 observations to construct estimates of the weight probabilities and compute the aggregate observation boundaries.…”

Section: Results and Simulationsmentioning

confidence: 99%

Sensor scheduling for energy-efficient target tracking in sensor networks

Atia¹,

Fuemmeler²,

Veeravalli³

2010

2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers

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In this paper we study the problem of tracking an object moving randomly through a network of wireless sensors. Our objective is to devise strategies for scheduling the sensors to optimize the tradeoff between tracking performance and energy consumption. We cast the scheduling problem as a Partially Observable Markov Decision Process (POMDP), where the control actions correspond to the set of sensors to activate at each time step. Using a bottom-up approach, we consider different sensing, motion and cost models with increasing levels of difficulty. At the first level, the sensing regions of the different sensors do not overlap and the target is only observed within the sensing range of an active sensor. Then, we consider sensors with overlapping sensing range such that the tracking error, and hence the actions of the different sensors, are tightly coupled. Finally, we consider scenarios wherein the target locations and sensors' observations assume values on continuous spaces. Exact solutions are generally intractable even for the simplest models due to the dimensionality of the information and action spaces. Hence, we devise approximate solution techniques, and in some cases derive lower bounds on the optimal tradeoff curves. The generated scheduling policies, albeit suboptimal, often provide close-to-optimal energy-tracking tradeoffs.

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Section: Results and Simulationsmentioning

confidence: 99%

Sensor scheduling for energy-efficient target tracking in sensor networks

Atia¹,

Fuemmeler²,

Veeravalli³

2010

2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers

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show abstract

“…There, the combinatorial complexity of the decision space is avoided by first selecting one leader node, followed by greedy sensor subset selection. Other related work on sensor scheduling includes leader-based distributed tracking schemes [116,117], where at any time instant one sensor, the leader sensor, is active, and the leader changes dynamically as a function of the object state, while the rest of the network is idle. Comparisons between scheduling and random sensor activation are made by Pattem et al [118], who show that orders of magnitude savings in energy are possible with scheduling.…”

Section: (C) Sensor Management Scheduling and Sleep Controlmentioning

confidence: 99%

Distributed inference in wireless sensor networks

Veeravalli

Varshney

2012

Phil. Trans. R. Soc. A.

107

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Statistical inference is a mature research area, but distributed inference problems that arise in the context of modern wireless sensor networks (WSNs) have new and unique features that have revitalized research in this area in recent years. The goal of this paper is to introduce the readers to these novel features and to summarize recent research developments in this area. In particular, results on distributed detection, parameter estimation and tracking in WSNs will be discussed, with a special emphasis on solutions to these inference problems that take into account the communication network connecting the sensors and the resource constraints at the sensors.

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“…The base policy is suboptimal, but should be easy to compute. He and Chong [13] solve a sensor management problem by applying a roll-out approach based on a particle filter. Miller et al [14] propose a POMDP approximation based on a Gaussian target representation and the use of nominal state trajectories in the planning.…”

Section: Background and Literature Surveymentioning

confidence: 99%

“…For a large number of t the cumulative probability of detection (13) can be approximated (using the binomial theorem) by the exponential distribution [46] Π 0:…”

Section: Cumulative Probability Of No Detectionmentioning

confidence: 99%

Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle

et al. 2012

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This article considers a sensor management problem where a number of road bounded vehicles are monitored by an unmanned aerial vehicle (UAV) with a gimballed vision sensor. The problem is to keep track of all discovered targets and simultaneously search for new targets by controlling the pointing direction of the vision sensor and the motion of the UAV. A planner based on a state-machine is proposed with three different modes; target tracking, known target search, and new target search. A high-level decision maker chooses among these sub-tasks to obtain an overall situational awareness. A utility measure for evaluating the combined search and target tracking performance is also proposed. By using this measure it is possible to evaluate and compare the rewards of updating known targets versus searching for new targets in the same framework. The targets are assumed to be road bounded and the road network information is used both to improve the tracking and sensor management performance. The tracking and search are based on flexible target density representations provided by particle mixtures and deterministic grids.

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Sensor scheduling for target tracking: A Monte Carlo sampling approach

Cited by 81 publications

References 16 publications

Sensor scheduling for energy-efficient target tracking in sensor networks

Sensor scheduling for energy-efficient target tracking in sensor networks

Distributed inference in wireless sensor networks

Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle

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