M
Mark R. Morelande
Researcher at RMIT University
Publications - 146
Citations - 3430
Mark R. Morelande is an academic researcher from RMIT University. The author has contributed to research in topics: Particle filter & Kalman filter. The author has an hindex of 28, co-authored 145 publications receiving 3105 citations. Previous affiliations of Mark R. Morelande include University of Melbourne & National Australia Bank.
Papers
More filters
Book
Fundamentals of Object Tracking
TL;DR: This book discusses object tracking with time-delayed, out-of-sequence measurements, as well asBayesian smoothing algorithms for object tracking, and Pseudo-functions in object tracking.
Proceedings Article
Statistical analysis of motion patterns in AIS Data: Anomaly detection and motion prediction
TL;DR: In this article, a statistical analysis of vessel motion patterns in the ports and waterways using AIS ship self-reporting data is devoted to statistical analysis, which is carried out in the framework of adaptive kernel density estimation.
Journal ArticleDOI
A Bayesian Approach to Multiple Target Detection and Tracking
TL;DR: Simulation results, with measurements generated from real target trajectories, demonstrate the ability of the proposed procedure to simultaneously detect and track ten targets with a reasonable sample size.
Journal ArticleDOI
An Information-Based Approach to Sensor Management in Large Dynamic Networks
TL;DR: This paper addresses the problem of sensor management for a large network of agile sensors through a novel combination of particle filtering for nonparametric density estimation, information theory for comparing actions, and physicomimetics for computational tractability.
Journal ArticleDOI
An Efficient Multi-Frame Track-Before-Detect Algorithm for Multi-Target Tracking
TL;DR: By factorizing the joint posterior density using the structure of MTT, an efficient DP-TBD algorithm is developed to approximately solve the joint maximization in a fast but accurate manner and can accurately estimate the number of targets and reliably track multiple targets even when targets are in proximity.