Target Tracking and Data Fusion in Sensor Networks

Target Tracking and Data Fusion in Sensor Networks is a research topic within Artificial Intelligence. Science Explorer counts 36k research works in it since 1950. 9.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on particle filtering and nonlinear estimation methods, including Kalman filters, Monte Carlo methods, sensor fusion, sequential Monte Carlo, Gaussian filters, and Bayesian inference. The papers cover a wide range of applications such as state estimation, multitarget tracking, and sensor network management.

• Particle Filters
• Nonlinear Estimation
• Kalman Filters
• Monte Carlo Methods
• State Estimation
• Sensor Fusion
• Sequential Monte Carlo
• Gaussian Filters
• Multitarget Tracking
• Bayesian Inference