NDLI: Linear multitarget integrated probabilistic data association for multiple detection target tracking

Content Provider	IET Digital Library
Author	Huang, Yuan Song, Taek Lyul Kim, Da Sol
Abstract	The point target assumption, which suggests that a target can generate at most one measurement at a time, is used in typical target tracking algorithms. However, in many practical applications, multiple scattering points of a target can be resolved using a high-resolution sensor, which gives rise to the multiple detection problem. The typical algorithms with the point target assumption are not eligible for multiple detection tracking environments. The multiple detection joint integrated probabilistic data association algorithm is designed to solve the multiple detection multitarget tracking problem. However, the computational complexity of this algorithm grows exponentially with the number of tracks and measurement cells. Here, multiple detection linear multitarget integrated probabilistic data association is proposed to enhance computational efficiency by introducing the modulated clutter measurement density, which takes into account the contributions of clutter as well as other targets of each measurement cell. The computational complexity of the proposed algorithm is linear in the number of tracks and the number of measurement cells. Simulation results verify the applicability and efficiency of the proposed algorithm in multiple detection multitarget tracking scenarios.
Starting Page	945
Ending Page	953
Page Count	9
ISSN	17518784
Volume Number	12
e-ISSN	17518792
Issue Number	Issue 9, Sep (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-rsn/12/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2017.0481
Journal	IET Radar, Sonar & Navigation
Publisher Date	2018-04-26
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computational Complexity Computational Efficiency High-resolution Sensor Measurement Cell Modulated Clutter Measurement Density Multiple Detection Joint Integrated Probabilistic Data Association Algorithm Multiple Detection Linear Multitarget Integrated Probabilistic Data Association Multiple Detection Multitarget Tracking Problem Multiple Detection Target Tracking Multiple Scattering Point Point Target Assumption Probability Sensor Fusion Signal Processing And Detection Signal Processing Theory Statistics Target Tracking
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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