Extended object tracking using random matrix with converted measurements

Li, Zhifei; Zhang, Jianyun; Jie-gui, Wang; Zhou, Quan

doi:10.1049/iet-rsn.2019.0512

Cited by 1 publication

(3 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(11). For this reason, approaches to deal with nonlinear measurement equations have been developed recently, e.g., [26]- [28]. In [27], statistical linearization and Variational Bayes (VB) are used to deal with nonlinear measurement equations, and the methods [26], [28] are based on converted measurements.…”

Section: B: Nonlinear Measurementsmentioning

confidence: 99%

“…As we assume that the star-convex contour C(τ ) of the target is known, the objective is to estimate the orientation α, length l 1 , and width l 2 using the parameterization in Eq. (28). For this purpose, a measurement equation can be formulated according to…”

Section: Cartesian-defined Star-convex Modelsmentioning

confidence: 99%

“…In the Gaussian mixture extent model from [86], the target state consists of the length, width and orientation of the target as in Eq. (28). A Gaussian mixture representation of the spatial distribution ( 9) is learned from real data for a normalized target that is located at the origin, axis aligned, and has unit width and length, i.e., the spatial distribution…”

Section: Gaussian Mixture Extent Modelmentioning

confidence: 99%

See 2 more Smart Citations

A Tutorial on Multiple Extended Object Tracking

Granström¹,

Baum²

2022

Preprint

View full text Add to dashboard Cite

<div>This tutorial introduces state-of-the-art methods for tracking multiple spatially extended objects based on unlabeled noisy point clouds, e.g., from radar or lidar sensors. In the first part, the focus lies on tracking a single extended object, i.e., the objective is to simultaneously estimate the shape and position of a moving object based on spatially distributed noisy detections. Model-based approaches for tracking elliptical and star-convex shaped objects are treated. Furthermore, recent learning-based approaches are discussed, which learn the spatial distribution of detections from real data. The second part considers the track management and data association problem, i.e., the initialization and termination of tracks as well as the association of detections to objects. After an in-depth analysis of the data association problem for extended objects, various data association approaches are discussed. Finally, recent frameworks for multiple extended object tracking are introduced such as the random finite set-based Poisson multi-Bernoulli mixture framework.</div>

show abstract