Principles and practice of information theory
Principles and practice of information theory
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mathematics of Data Fusion
Bayesian Multiple Target Tracking
Bayesian Multiple Target Tracking
A dual-space approach to tracking and sensor management in wireless sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Rollout Algorithms for Stochastic Scheduling Problems
Journal of Heuristics
Feature Space Trajectory Methods for Active Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden Markov model multiarm bandits: a methodology for beamscheduling in multitarget tracking
IEEE Transactions on Signal Processing
Conditional-mean estimation via jump-diffusion processes inmultiple target tracking/recognition
IEEE Transactions on Signal Processing
A Bayesian approach to tracking multiple targets using sensorarrays and particle filters
IEEE Transactions on Signal Processing
Algorithms for optimal scheduling and management of hidden Markovmodel sensors
IEEE Transactions on Signal Processing
Discrimination gain to optimize detection and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
Partially Observable Markov Decision Process Approximations for Adaptive Sensing
Discrete Event Dynamic Systems
An Information Roadmap Method for Robotic Sensor Path Planning
Journal of Intelligent and Robotic Systems
Approximate nonmyopic sensor selection via submodularity and partitioning
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Information-driven search strategies in the board game of CLUE®
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information-driven sensor path planning by approximate cell decomposition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information driven search for point sources of gamma radiation
Signal Processing
Information theoretic adaptive tracking of epidemics in complex networks
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Planning to see: A hierarchical approach to planning visual actions on a robot using POMDPs
Artificial Intelligence
Generalized frequency modulated waveform libraries for radar tracking applications
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Energy aware iterative source localization for wireless sensor networks
IEEE Transactions on Signal Processing
Sensor management: a new paradigm for automatic video surveillance
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
Active visual sensing and collaboration on mobile robots using hierarchical POMDPs
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
ACTIDS: an active strategy for detecting and localizing network attacks
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
Hi-index | 0.09 |
An approach that is common in the machine learning literature, known as active sensing, is applied to provide a method for managing agile sensors in a dynamic environment. We adopt an active sensing approach to scheduling sensors for multiple target tracking applications that combines particle filtering, predictive density estimation, and relative entropy maximization. Specifically, the goal of the system is to learn the number and states of a group of moving targets occupying a surveillance region. At each time step, the system computes a sensing action to take, based on an entropy measure called the Rényi divergence. After the measurement is made, the system updates its probability density on the number and states of the targets. This procedure repeats at each time where a sensor is available for use. The algorithms developed here extend standard active sensing methodology to dynamically evolving objects and continuous state spaces of high dimension. It is shown using simulated measurements on real recorded target trajectories that this method of sensor management yields more than a ten fold gain in sensor efficiency when compared to periodic scanning.