&lt;title&gt;Evaluation methodology for hyperspectral automatic target cueing systems&lt;/title&gt;

Thornburg, Ross; Maciejewski, Phil; Claypool, Rifka; Jarratt, Mary; Westerkamp, Lori A.

doi:10.1117/12.478771

Cited by 2 publications

(5 citation statements)

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“…In these techniques, the fusion algorithm maintains an internal probabilistic model relating the data that the algorithm is given ( A and V ) to the target type portions of the OCs ( T R (2) or, using the chain rule for CPDs and Bayes rule, Equation 2 is:…”

Section: Fusion Equationmentioning

confidence: 99%

“…We will use the term Operating Conditions (OCs) for all factors that influence ATR performance, which are often separated into sensor OCs, target OCs, and environment OCs. 1,2 ATRs occasionally make errors (e.g., labeling a tree as a target or labeling a T72 as an M1), especially in difficult OCs. Data fusion 3 combines data from multiple sensors (or sensor collections) to (1) improve the reliability and conciseness of the information provided to end-users, (2) increase spatial coverage, and (3) reduce the time to decision making.…”

Section: Introductionmentioning

confidence: 99%

“…1,2 ATRs occasionally make errors (e.g., labeling a tree as a target or labeling a T72 as an M1), especially in difficult OCs. Data fusion 3 combines data from multiple sensors (or sensor collections) to (1) improve the reliability and conciseness of the information provided to end-users, (2) increase spatial coverage, and (3) reduce the time to decision making. There are a variety of approaches to data fusion, but we are concerned here only with decision-level identity fusion (DLF).…”

Section: Introductionmentioning

confidence: 99%

“…We refer to these factors as "operating conditions" or OCs. 1,2 The scope of OCs includes target, environment, and sensor variability and how they affect the sensor data and the ATR. The target OCs include the target categories, from class to type to versions (including modifications/additions); and configuration, articulation, damage, and moving-part variants.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian framework for ATR decision-level fusion experiments

Morgan

Ross

2007

Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007

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The US Air Force Research Laboratory (AFRL) Fusion for Identifying Targets Experiment (FITE) program aims to determine the benefits of decision-level fusion (DLF) of Automatic Target Recognition (ATR) products. This paper describes the Bayesian framework used to characterize the trade-space for DLF approaches and applications. The overall fusion context is represented as a Bayesian network and the fusion algorithms use Bayesian probability computations. Bayesian networks conveniently organize the large sets of random variables and distributions appearing in fusion system models, including models of operating conditions, prior knowledge, ATR performance, and fusion algorithms. The relationship between fuser performance and these models may be analytically stated (the FITE equation), but must be solved via stochastic system modeling and Monte Carlo simulation. A key element of the DLF trade-space is the degree to which the various models depend on ATR operating conditions, since these will determine the fuser's complexity and performance and will suggest new requirements on source ATRs.

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Section: Fusion Equationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Bayesian framework for ATR decision-level fusion experiments

Morgan

Ross

2007

Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007

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“…A number of recent papers 1,2,[5][6][7][8][9] have identified and discussed that OCs are split into three categories: target, environment, and sensor, as well as the impact of OCs on ATR performance. Many of these papers assessed the performance of ATR systems using measured datasets where some of the OCs were identified and varied.…”

mentioning

confidence: 99%

Operating condition modeling for ATR fusion assessment

Kahler

Blasch

Goodwon

2007

Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007

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Real world Operating Conditions (OCs) influence sensor data that in turn affects the performance of target detection and identification systems utilizing the collected information. The impact of operating conditions on collected data is widely accepted, but not fully characterized. OCs that affect data depend on sensor wavelength and associated scenario phenomenology, and can vary significantly between electro-optical (EO), infrared (IR), and radar sensors. This paper will discuss what operating conditions might be modeled for each sensor type and how they could affect automatic target recognition (ATR) systems designed to exploit their respective sensory data. The OCs are broken out into four categories; sensor, environment, target, and ATR algorithm training. These main categories will further contain subcategories with varying levels of influence. The purpose of this work is to develop an OC distribution model for the "real world" that can be used to realistically represent the performance of multiple ATR systems, and ultimately the decision made from the fused ATR results. An accurate OC model will greatly enhance the performance assessment of ATR and fusion systems by affording Bayesian conditioning in fusion performance analysis and aiding in the sensitivity analysis of fusion performance over different operational conditions. Accurate OC models will also be useful in the fusion algorithm operation.

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<title>Evaluation methodology for hyperspectral automatic target cueing systems</title>

Cited by 2 publications

References 0 publications

A Bayesian framework for ATR decision-level fusion experiments

A Bayesian framework for ATR decision-level fusion experiments

Operating condition modeling for ATR fusion assessment

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