On Optimal Sensor Placement and Motion Coordination for Target Tracking

By Sulema Aranda, Sonia Martínez, Francesco Bullo. Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005

395 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5acd03c0816a6a334c8235c6d3c54875d89433ef

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Citations in the corpus

Mobile Sensor Network Control Using Mutual Information Methods and Particle Filters (2010, methodology): “Search methods with an EKF use metrics of the expected estimation covariance following a control action, often in an information theoretic context [8], [9], [10], [11].”
Coordination and Consensus of Networked Agents with Noisy Measurements: Stochastic Algorithms and Asymptotic Behavior (2009, methodology): “The initial condition is xt|t=0 = [5, 1, 3, 2] , and the i.”
Closing ranks in rigid multi‐agent formations using edge contraction (2010, background): “…angle of arrival data or time difference of arrival information is best achieved (in the sense of minimizing localization error) when the UAVs are located at the vertices of a regular polygon [ 12–14 ] . keeping This objective can be achieved by explicitly some inter-agent distances constant.”
Cooperative Filters and Control for Cooperative Exploration (2010, background): “Recent theoretical and experimental developments suggest that a balance between data collection and feasible motion is key to mission success [15], [18], [19].”
Continuous trajectory planning of mobile sensors for informative forecasting (2010, background): “…research, the plans are often generated to reduce uncertainty in some quantity of interest (e.g., position and velocity of targets [10– 12, 14, 15, 23, 29, 36], the pose of the sensor platform itself, the forecast of weather over some region of interest [1, 3, 22, 28], physical quantities under…”
Human Interaction With Robot Swarms: A Survey (2016, background): “The authors of [32] unify prior work on connectivity maintenance [33], rendezvous [34], deployment [35], [36], boundary estimation [37], and tracking [38], and present a rigorous”
Please Scroll down for Article International Journal of Control Distributed Linear Estimation over Sensor Networks Distributed Linear Estimation over Sensor Networks (background): “…applications include environmental monitoring, surveillance, object tracking collaborative information processing, traffic monitoring and mobile agents control; see, for instance Akyildiz, Su, Sankarasubramniam, and Cayirci (2002), Chu, Haussecker, and Zhao (2002) and Martinez and Bullo (2006).”
Distributed Kalman Filter with Embedded Consensus Filters (2005, background): “Sensor networks and intelligent arrays of micro-sensors have broad range of applications including information gathering and data fusion for modeling an environment, surveillance, active monitoring of forests & agricultural lands, health-care applications, collaborative information processing, and c”