Many Army missions rely on effective sensing capabilities that provide intelligence on the adversary, and protect friendly forces from enemy detection. Sensors that are stationary (microphones, geophones, and ground-based radars) and moving (cameras on unattended aerial vehicles and ground vehicles) assist operations such as persistent surveillance of small, forward-operating bases, and rapid covert troop maneuvers in the air and on the ground. When advantageous, sensing is often performed in multiple signal modalities including visible, infrared, acoustic, seismic, radiofrequency, chemical, and biological.

Environmental Awareness of Sensor and Emitter Employment (EASEE) is a software framework that provides a single environment for analyzing sensor performance involving many different signal modalities. This ability for multimodal signal analysis enables EASEE to also perform higher-level data synthesis needed to answer critical sensing questions such as the sensor types and locations best suited for accomplishing mission objectives within the constraints of a particular environment.

The EASEE software design is formulated within the conceptual framework of object-oriented programming in the Java language. Random environmental effects on transmitted and received features are accounted for by representing them as random variables. Parametric descriptions of these random variables are programmed in EASEE within Java classes. These Java classes are denoted as signal models, and instances of them are signal model objects. Signal model objects are key in the architecture of EASEE, since they are what are actually transmitted, received, and processed.

Specifically, the feature generator produces a signal model object that is inputted and then altered by the feature propagator and feature sensor before it is finally analyzed by the feature processor to produce an inference, which represents desired information derived from the data features such as probability of detection or error of target location estimates.

Signal model classes contain methods for the various statistical operations necessary for making probabilistic predictions on sensor performance, including setting the mean and variance; converting from mean and variance to unique parameter values; computing the pdf, cdf, and quantile; and summing a random variable described by an instance of the class with another one.

This work was done by Kenneth K. Yamamoto, D. Keith Wilson, and Chris L. Pettit of the Cold Regions Research and Engineering Laboratory for the U.S. Army Corps of Engineers. ARL-0123

This Brief includes a Technical Support Package (TSP).
Probability and Statistics in Sensor Performance Modeling

(reference ARL-0123) is currently available for download from the TSP library.

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