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Environmental Awareness for Sensor and Emitter Employment (EASEE) is a flexible, object-oriented software design
for predicting environmental effects on the performance of battlefield sensors and detectability of signal emitters. Its
decision-support framework facilitates many sensor and emitter modalities and can be incorporated into battlespace
command and control (C2) systems. Other potential applications include immersive simulation, force-on-force
simulation, and virtual prototyping of sensor systems and signal-processing algorithms. By identifying and encoding
common characteristics of Army problems involving multimodal signal transmission and sensing into a flexible software
architecture in the Java programming language, EASEE seeks to provide an application interface enabling rapid
integration of diverse signal-generation, propagation, and sensor models that can be implemented in many client-server
environments. Its explicit probabilistic modeling of signals, systematic consideration of many complex environmental
and mission-related factors affecting signal generation and propagation, and computation of statistical metrics
characterizing sensor performance facilitate a highly flexible approach to signal modeling and simulation. EASEE aims
to integrate many disparate statistical formulations for modeling and processing many types of signals, including
infrared, acoustic, seismic, radiofrequency, and chemical/biological. EASEE includes objects for representing sensor
data, inferences for target detection and/or direction, signal transmission and processing, and state information (such as
time and place). Various transmission and processing objects are further grouped into platform objects, which fuse data
to make various probabilistic predictions of interest. Objects representing atmospheric and terrain environments with
varying degrees of fidelity enable modeling of signal generation and propagation in diverse and complex environments.
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