Title
A Pseudo-Measurement Approach to Simultaneous Registration and Track Fusion
Abstract
In multi-sensor tracking, registration is expected to be performed at the track level instead of the measurement level especially for the distributed sensor networks. However, registration at the track level becomes more difficult due to the implicit sensor biases hidden behind the local tracks. We propose a pseudo-measurement approach to solve the simultaneous registration and fusion problem at the track level. A pseudo-measurement equation is derived from the local trackers, which explicitly reveals the relationship between the pseudo-measurements and the sensor biases in a closed-form expression. The resulting registration model then allows us to formulate the track registration and fusion as a maximum likelihood (ML) estimation problem. We propose using the expectation maximization (EM) approach to perform track registration and fusion simultaneously. Both batch and recursive EM algorithms are developed, accompanied by the performance analysis. Simulation results demonstrate that both EM algorithms are capable of providing accurate estimates. Moreover, we apply the proposed method to an air surveillance radar network which suffers from relatively serious registration problems. The proposed method is verified to effectively fuse and register the tracks generated by local radars and to provide a consistent air picture.
Year
DOI
Venue
2012
10.1109/TAES.2012.6237594
IEEE Trans. Aerospace and Electronic Systems
Keywords
Field
DocType
Radar tracking,Vectors,Covariance matrix,Mathematical model,Target tracking,Noise,Equations
Secondary surveillance radar,Computer vision,BitTorrent tracker,Radar tracker,Expectation–maximization algorithm,Sensor fusion,Artificial intelligence,Covariance matrix,Fuse (electrical),Wireless sensor network,Mathematics
Journal
Volume
Issue
ISSN
48
3
0018-9251
Citations 
PageRank 
References 
12
1.13
7
Authors
3
Name
Order
Citations
PageRank
Dongliang Huang1121.81
Henry Leung21309151.88
Éloi Bossé338626.19