Many outdoor sound propagation models exist, ranging from highly complex physics-based simulations to simplified engineering calculations, and more recently, highly flexible statistical learning methods. Several engineering and statistical learning models are evaluated by using a particular physics-based model, namely, a Crank-Nicholson parabolic equation (CNPE), as a benchmark. Narrowband transmission loss values predicted with the CNPE, based upon a simulated data set of meteorological, boundary, and source conditions, act as simulated observations. In the simulated data set sound propagation conditions span from downward refracting to upward refracting, for acoustically hard and soft boundaries, and low frequencies. Engineering models used in the comparisons include the ISO 9613-2 method, Harmonoise, and Nord2000 propagation models. Statistical learning methods used in the comparisons include bagged decision tree regression, random forest regression, boosting regression, and artificial neural network models. Computed skill scores are relative to sound propagation in a homogeneous atmosphere over a rigid ground. Overall skill scores for the engineering noise models are 0.6%, -7.1%, and 83.8% for the ISO 9613-2, Harmonoise, and Nord2000 models, respectively. Overall skill scores for the statistical learning models are 99.5%, 99.5%, 99.6%, and 99.6% for bagged decision tree, random forest, boosting, and artificial neural network regression models, respectively.
The Environmental Awareness for Sensor and Emitter Employment (EASEE) software, being developed by the U. S. Army Engineer Research and Development Center (ERDC), provides a general platform for predicting sensor performance and optimizing sensor selection and placement in complex terrain and weather conditions. It incorporates an extensive library of target signatures, signal propagation models, and sensor systems. A flexible object-oriented design supports efficient integration and simulation of diverse signal modalities. This paper describes the integration of modeling capabilities for radio-frequency (RF) transmission and radar systems from the U. S. Navy Electromagnetic Propagation Integrated Resource Environment (EMPIRE), which contains nearly twenty different realistic RF propagation models. The integration utilizes an XML-based interface between EASEE and EMPIRE to set inputs for and run propagation models. To accommodate radars, fundamental improvements to the EASEE software architecture were made to support active-sensing scenarios with forward and backward propagation of the RF signals between the radar and target. Models for reflecting targets were defined to apply a target-specific, directionally dependent reflection coefficient (i.e., scattering cross section) to the incident wavefields.
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