Paper Info
Title
Enhanced target tracking by incorporating target visibility into the IMM algorithms
Abstract
In this paper we introduce the concept of target visibility into the Interacting-Multiple-Model estimator with Probabilistic Data Association Filter (IMMPDAF) and the Interacting-Multiple-Model estimator with Nearest Neighborhood Filter (IMMNNF) in order to take into account those instances when the target becomes invisible and cannot be detected by the sensor. Tracks can be automatically terminated when the target becomes invisible or when it enters an area occluded by the physical limitations of the sensor. We employ the Natural Logarithm of the Dynamic Error Spectrum (NL-DES) to evaluate the performance of these filters. Results show that the visibility concept significantly improves the performance of the IMMNNF and IMMPDAF. This enhances the capability of platforms that employ the IMMNNF and/or IMMPDAF in tracking maneuvering targets. It also improves the ability of such platforms to detect and identify possible threats that may endanger a protected zone and consequently their ability to provide early warning of the presence of such threats.
Year
DOI
Venue
2014
10.1109/ICDSP.2014.6900751
Digital Signal Processing
Keywords
Field
DocType
filtering theory,performance evaluation,probability,sensor fusion,target tracking,IMM algorithms,IMMPDAF,NL-DES,early warning,enhanced target tracking,interacting-multiple-model estimator with nearest neighborhood filter,interacting-multiple-model estimator with probabilistic data association filter,natural logarithm of the dynamic error spectrum,performance evaluation,target visibility,IMMNNF,IMMPDAF,dynamic error spectrum,target tracking
Computer vision,Visibility,Pattern recognition,Computer science,Artificial intelligence
Conference
ISSN
Citations 
PageRank 
1546-1874
0
0.34
References 
Authors
3
2
Name
Order
Citations
PageRank
Madeleine G. Sabordo100.34
Elias Aboutanios220326.36