2,356 research outputs found
Joint probabilistic data association filter with unknown detection probability and clutter rate
This paper proposes a novel joint probabilistic data association (JPDA) filter for joint target tracking and track maintenance under unknown detection probability and clutter rate. The proposed algorithm consists of two main parts: (1) the standard JPDA filter with a Poisson point process birth model for multi-object state estimation; and (2) a multi-Bernoulli filter for detection probability and clutter rate estimation. The performance of the proposed JPDA filter is evaluated through empirical tests. The results of the empirical tests show that the proposed JPDA filter has comparable performance with ideal JPDA that is assumed to have perfect knowledge of detection probability and clutter rate. Therefore, the algorithm developed is practical and could be implemented in a wide range of application
Extended Object Tracking: Introduction, Overview and Applications
This article provides an elaborate overview of current research in extended
object tracking. We provide a clear definition of the extended object tracking
problem and discuss its delimitation to other types of object tracking. Next,
different aspects of extended object modelling are extensively discussed.
Subsequently, we give a tutorial introduction to two basic and well used
extended object tracking approaches - the random matrix approach and the Kalman
filter-based approach for star-convex shapes. The next part treats the tracking
of multiple extended objects and elaborates how the large number of feasible
association hypotheses can be tackled using both Random Finite Set (RFS) and
Non-RFS multi-object trackers. The article concludes with a summary of current
applications, where four example applications involving camera, X-band radar,
light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are
highlighted.Comment: 30 pages, 19 figure
Technical Report: Cooperative Multi-Target Localization With Noisy Sensors
This technical report is an extended version of the paper 'Cooperative
Multi-Target Localization With Noisy Sensors' accepted to the 2013 IEEE
International Conference on Robotics and Automation (ICRA).
This paper addresses the task of searching for an unknown number of static
targets within a known obstacle map using a team of mobile robots equipped with
noisy, limited field-of-view sensors. Such sensors may fail to detect a subset
of the visible targets or return false positive detections. These measurement
sets are used to localize the targets using the Probability Hypothesis Density,
or PHD, filter. Robots communicate with each other on a local peer-to-peer
basis and with a server or the cloud via access points, exchanging measurements
and poses to update their belief about the targets and plan future actions. The
server provides a mechanism to collect and synthesize information from all
robots and to share the global, albeit time-delayed, belief state to robots
near access points. We design a decentralized control scheme that exploits this
communication architecture and the PHD representation of the belief state.
Specifically, robots move to maximize mutual information between the target set
and measurements, both self-collected and those available by accessing the
server, balancing local exploration with sharing knowledge across the team.
Furthermore, robots coordinate their actions with other robots exploring the
same local region of the environment.Comment: Extended version of paper accepted to 2013 IEEE International
Conference on Robotics and Automation (ICRA
Robust Multi-Object Tracking: A Labeled Random Finite Set Approach
The labeled random finite set based generalized multi-Bernoulli filter is a tractable analytic solution for the multi-object tracking problem. The robustness of this filter is dependent on certain knowledge regarding the multi-object system being available to the filter. This dissertation presents techniques for robust tracking, constructed upon the labeled random finite set framework, where complete information regarding the system is unavailable
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