A Multidimensional TDOA Association Algorithm for Joint Multitarget Localization and Multisensor Synchronization

This article considers the problem of multitarget localization using time difference of arrival (TDOA) measurements at multiple sensors with misaligned clocks in the presence of data association uncertainty. Sensor synchronization and data association are two essential steps in target localization a...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems Vol. 56; no. 3; pp. 2083 - 2100
Main Authors: Ge, Tongyu, Tharmarasa, Ratnasingham, Lebel, Bernard, Florea, Mihai, Kirubarajan, Thiagalingam T.
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Clocks
Computer simulation
Cramer-Rao bounds
Cramér-Rao lower bound (CRLB)
data association
Localization
Lower bounds
Maximum likelihood estimators
Measurement uncertainty
Multiple target tracking
multisensor synchronization
multitarget localization
Performance evaluation
Position measurement
Position sensing
Sensors
Synchronism
Synchronization
Target tracking
time difference of arrival (TDOA) data
track initialization
Uncertainty
ISSN:0018-9251, 1557-9603
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This article considers the problem of multitarget localization using time difference of arrival (TDOA) measurements at multiple sensors with misaligned clocks in the presence of data association uncertainty. Sensor synchronization and data association are two essential steps in target localization and tracking to align sensor clocks and to associate measurements from different sensors. In practice, sensor synchronization errors can adversely affect data association performance and vice versa. Although these two processes affect each other, they are usually addressed separately. We propose a novel joint multidimensional association algorithm for multisensor synchronization (JMDA4MS), which performs data association and yields sensor clock offset and target position estimates simultaneously using TDOA measurements. Considering the observability of the unknown parameters, the joint multiframe multidimensional association algorithm is developed as a multiframe extension of the JMDA4MS algorithm. A gating method and a multidimensional plus sequential two-dimensional association approach are utilized to improve the efficiency of the proposed algorithms. The Cramér–Rao lower bound for the proposed maximum likelihood estimator is derived as a performance benchmark. Computer simulations are carried out to evaluate the performance of the proposed algorithms on different scenarios.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2019.2943786