Learning of a tracker model from multi-radar data for performance prediction of air surveillance system

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{ruotsalainen:2017:CEC,
  author =       "Marja Ruotsalainen and Juha Jylha",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "Learning of a tracker model from multi-radar data for
                 performance prediction of air surveillance system",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "2128--2136",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "A valid model of the air surveillance system
                 performance is highly valued when making decisions
                 related to the optimal control of the system. We
                 formulate a model for a multi-radar tracker system by
                 combining a radar performance model with a tracker
                 performance model. A tracker as a complex software
                 system is hard to model mathematically and physically.
                 Our novel approach is to use machine learning to create
                 a tracker model based on measurement data from which
                 the input and target output for the model are
                 calculated. The measured data comprises the time series
                 of 3D coordinates of cooperative aircraft flights, the
                 corresponding target detection recordings from multiple
                 radars, and the related multi-radar track recordings.
                 The collected data is used to calculate performance
                 measures for the radars and the tracker at specific
                 locations in the air space. We apply genetic
                 programming to learning such rules from radar
                 performance measures that explain tracker performance.
                 The easily interpretable rules are intended to reveal
                 the real behavior of the system providing comprehension
                 for its control and further development. The learned
                 rules allow predicting tracker performance level for
                 the system control in all radar geometries, modes, and
                 conditions at any location. In the experiments, we show
                 the feasibility of our approach to learning a tracker
                 model and compare our rule learner with two tree
                 classifiers, another rule learner, a neural network,
                 and an instance-based classifier using the real air
                 surveillance data. The tracker model created by our
                 rule learner outperforms the models by the other
                 methods except for the neural network whose prediction
                 performance is equal.",
  keywords =     "genetic algorithms, genetic programming, aircraft
                 control, learning (artificial intelligence),
                 neurocontrollers, object detection, optimal control,
                 radar tracking, surveillance, time series, air space,
                 air surveillance system performance, aircraft flights,
                 instance-based classifier, machine learning, multiradar
                 data, multiradar track recordings, multiradar tracker
                 system, neural network, radar geometries, radar
                 performance model, rule learner, target detection,
                 tracker performance model, Atmospheric modeling, Radar
                 detection, Radar measurements, Spaceborne radar, Target
                 tracking",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969562",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as
                 \cite{7969562}",
}

Genetic Programming entries for Marja Ruotsalainen Juha Jylha

Citations