Predicting Aircraft Detectability

Author(s):  
Akerman Alexander ◽  
Robert E. Kinzly

A visual search model, VIDEM, has been formulated for predicting the detectability of a single, unknown target in an unstructured surround. The intended application is aircraft detection. The model consists of four components: a liminal contrast threshold, a frequency-of-seeing curve, a soft shell search representation, and discrete cumulation of single glimpse detection probabilities. The formulation was developed by registering five existing models against three controlled search experiments. The five models used represent all appropriate laboratory threshold data, including those of Blackwell, Lamar, Sloan, and Taylor. The search experiments included a large set of aircraft field tests, with precise photometric target measurements correlated to the detection events. The model registrations were done using nonlinear parameter estimation techniques and by comparing model predictions to actual event cumulatives with the Kolmogorov-Smirnov statistic. The resultant VIDEM model is a derivative of Sloan's data, cast into the popular visual lobe equations of Lamar.

2019 ◽  
Vol 13 (4) ◽  
pp. 100982
Author(s):  
Yurij L. Katchanov ◽  
Yulia V. Markova ◽  
Natalia A. Shmatko

2000 ◽  
Vol 18 (4-5) ◽  
pp. 368-382 ◽  
Author(s):  
Dmitrii N Rassokhin ◽  
Dimitris K Agrafiotis

2019 ◽  
Vol 4 (343) ◽  
pp. 21-38
Author(s):  
Adam Piotr Idczak

Granting a credit product has always been at the heart of banking. Simultaneously, banks are obligated to assess the borrower’s credit risk. Apart from creditworthiness, to grant a credit product, banks are using credit scoring more and more often. Scoring models, which are an essential part of credit scoring, are being developed in order to select those clients who will repay their debt. For lenders, high effectiveness of selection based on the scoring model is the primary attribute, so it is crucial to gauge its statistical quality. Several textbooks regarding assessing statistical quality of scoring models are available, there is however no full consistency between names and definitions of particular measures. In this article, the most common statistical measures for assessing quality of scoring models, such as the pseudo Gini index, Kolmogorov‑Smirnov statistic, and concentration curve are reviewed and their statistical characteristics are discussed. Furthermore, the author proposes the application of the well‑known distribution similarity index as a measure of discriminatory power of scoring models. The author also attempts to standardise names and formulas for particular measures in order to finally contrast them in a comparative analysis of credit scoring models.


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