EO/IR ATR performance Modeling to support fusion experimentation

被引:1
|
作者
Kahler, Bart [1 ]
Blasch, Erik [2 ]
Pikas, David J. [3 ]
Ross, Tim [2 ]
机构
[1] Gen Dynam Corp, Dayton, OH 45431 USA
[2] AFRL SNA, Air Force Res Lab, WPAFB, Wright Patterson AFB, OH 45433 USA
[3] Jacobs Adv Syst Grp, Beavecreek, OH 45431 USA
来源
关键词
EO; IR; performance model; ATR; fusion; operating conditions; OCs; identification; multiple looks;
D O I
10.1117/12.719591
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The identification of a target from an electro-optical or thermal imaging sensor requires accurate sensor registration, quality sensor data, and an exploitation algorithm. Combining the sensor data and exploitation, we are concerned with developing an electro-optical or infrared (EO/IR) performance model. To combat the registration issue, we need a detailed list of operating conditions (i.e. collection position) so that the sensor exploitation results can be evaluated with sensitivities to these operating conditions or collection parameters. The focus of this paper will build on the NVSED AQUIRE model(2). We are also concerned with developing an EO/IR model that affords comparable operating condition parameters to a synthetic aperture radar (SAR) performance model. The choice of EO/IR modeling additions are focused on areas were Fusion Gain might be realized through an experiment tradeoff between multiple EO/IR looks for ATR exploitation fusion. The two additions to known EO/IR models discussed in the paper are (1) adjacency and (2) obscuration. The methods to account for these new operating conditions and the corresponding results on the modeled performance are presented in this paper.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] V-NIIRS Fusion Modeling for EO/IR Systems
    Blasch, Erik
    Kahler, Bart
    PROCEEDINGS OF THE 2015 IEEE NATIONAL AEROSPACE AND ELECTRONICS CONFERENCE (NAECON), 2015, : 19 - 26
  • [2] Naval target classification by fusion of IR and EO sensors
    Giompapa, S.
    Croci, R.
    Di Stefano, R.
    Farina, A.
    Gini, F.
    Graziano, A.
    Lapierre, F.
    ELECTRO-OPTICAL AND INFRARED SYSTEMS: TECHNOLOGY AND APPLICATIONS IV, 2007, 6737
  • [3] Enhanced Modeling and Simulation of EO/IR Sensor Systems
    Hixson, Jonathan G.
    Miller, Brian
    May, Christopher
    MODELING AND SIMULATION FOR DEFENSE SYSTEMS AND APPLICATIONS X, 2015, 9478
  • [4] ATR performance modeling and estimation
    Dudgeon, DE
    DIGITAL SIGNAL PROCESSING, 2000, 10 (04) : 269 - 285
  • [5] ATR Performance Modeling Concepts
    Ross, Timothy D.
    Baker, Hyatt B.
    Nolan, Adam R.
    McGinnis, Ryan E.
    Paulson, Christopher R.
    ALGORITHMS FOR SYNTHETIC APERTURE RADAR IMAGERY XXIII, 2016, 9843
  • [6] EO/IR sensor performance for site perimeter security
    Forrai, DP
    Devitt, JW
    Back, TC
    Rogers, WJ
    Gaulding, SM
    Sensors, and Command, Control, Communications, and Intelligence (C31) Technologies for Homeland Security and Homeland Defense IV, Pts 1 and 2, 2005, 5778 : 782 - 791
  • [7] Operating condition modeling for ATR fusion assessment
    Kahler, Bart
    Blasch, Erik
    Goodwon, Lloyd
    MULTISENSOR, MULTISOURCE INFORMATION FUSION: ARCHITECTURES, ALGORITHMS, AND APPLICATIONS 2007, 2007, 6571
  • [8] Performance of an EO/IR sensor system in marine search and rescue
    Leonard, CL
    DeWeert, MJ
    Gradie, J
    Iokepa, J
    Stalder, CL
    Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications II, 2005, 5787 : 122 - 133
  • [9] Fidelity score for ATR performance modeling
    Blasch, E
    Lavely, E
    Ross, T
    Algorithms for Synthetic Aperture Radar Imagery XII, 2005, 5808 : 383 - 394
  • [10] Modelling and performance assessment in QinetiQ of EO and IR airborne reconnaissance systems
    Williams, JW
    Potter, GE
    AIRBORNE RECONNAISSANCE XXVI, 2002, 4824 : 102 - 111