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Intelligence, Surveillance, and Reconnaissance (ISR) sensing platforms are becoming increasingly complex. Consequently, the fidelity of collected data is continuing to increase, along with the number of deployable sensors that retrieve these data, such as those found on Remotely Piloted Aircraft (RPAs). There are numerous, critical challenges when designing ISR systems because the technology and the human are tightly integrated, resulting in interdependent performance and behaviors. Predicting operator error can inform more effective means of managing erroneous decisions, but current methods of doing so are impractical because of the effort required to construct operator models. We explored human performance in a target detection task by conducting a human-in-the-loop experiment that examined the performance of operators who simultaneously monitored four simulated RPA video feeds and determined the presence of targets at points of interest (POIs). The results of this experiment confirm that performance varies significantly across certain flight conditions (e.g., combinations of altitude, speed, aspect angle). A statistical model was constructed from the human data to predict operator error in new situations. In future work, the model predictions will be integrated with an automated flight planner that will adjust RPA air tasking orders in real time and intelligently revisit POIs when human error is likely.

Objective The objective of the CARRECT software is to make cutting edge statistical methods for reducing bias in epidemiological studies easy to use and useful for both novice and expert users. Introduction Analyses produced by epidemiologists and public health practitioners are susceptible to bias from a number of sources including missing data, confounding variables, and statistical model selection. It often requires a great deal of expertise to understand and apply the multitude of tests, corrections, and selection rules, and these tasks can be time-consuming and burdensome. To address this challenge, Aptima began development of CARRECT, the Collaborative Automation Reliably Remediating Erroneous Conclusion Threats system. When complete, CARRECT will provide an expert system that can be embedded in an analyst’s workflow. CARRECT will support statistical bias reduction and improved analyses and decision making by engaging the user in a collaborative process in which the technology is transparent to the analyst. Methods Older approaches to imputing missing data, including mean imputation and single imputation regression methods, have steadily given way to a class of methods known as “multiple imputation” (hereafter “MI”; Rubin 1987). Rather than making the restrictive assumption that the data are missing completely at random (MCAR), MI typically assumes the data are missing at random (MAR). There are two key innovations behind MI. First, the observed values can be useful in predicting the missing cells, and thus specifying a joint distribution of the data is the first step in implementing the models. Second, single imputation methods will likely fail not only because of the inherent uncertainty in the missing values but also because of the estimation uncertainty associated with generating the parameters in the imputation procedure itself. By contrast, drawing the missing values multiple times, thereby generating m complete datasets along with the estimated parameters of the model properly accounts for both types of uncertainty (Rubin 1987; King et al. 2001 ). As a result, MI will lead to valid standard errors and confidence intervals along with unbiased point estimates. In order to compute the joint distribution, CARRECT uses a bootstrapping-based algorithm that gives essentially the same answers as the standard Bayesian Markov Chain Monte Carlo (MCMC) or Expectation Maximization (EM) approaches, is usually considerably faster than existing approaches and can handle many more variables. Results Tests were conducted on one of the proposed methods with an epidemiological dataset from the Integrated Health Interview Series (IHIS) producing verifiably unbiased results despite high missingness rates. In addition, mockups ( Figure 1 ) were created of an intuitive data wizard that guides the user through the analysis processes by analyzi...