Remcoms proposed solution creates a radiofrequency (RF) line-of-sight (LOS) service for the Next Generation Open Mission Planning (NOMS) framework. The rapidity with which profiles of terrain and other environmental data can be acquired often gates the rate at which weapons systems analyses can be performed, due to the basic environmental requirements for determining LOS. Optical LOS determination depends on the terrain between endpoints and atmospheric refraction effects, while radio LOS depends on terrain, dielectric characteristics of the terrain being traversed, and atmospheric characteristics. A service can be established that provides simple yes/no answers based on environmental data and signal criteria. Alternatively, the service could return the environment data itself in a format that lends itself to efficient processing. A service that provides such responses at a high rate could benefit any application or service that requires such data by improving overall performance in terms of rapidity of responses and reducing the time to complete a full analysis. Remcom has developed methods to rapidly extract environment data that lies between two endpoints from gridded data overlaid on a WGS84 spheroid. These methods can be applied to extract terrain data in multiple 2D and 3D formats. Such formats can potentially incorporate WGS-84 datum information that gives Earth curvature in the extraction region, which can be used to adjust for optical and RF refraction with fast vector math that determines LOS, horizon, and RF propagation paths. Remcoms proposed approach includes a number of key elements to optimize messaging and calculation time. This includes the concept of an "elastic" search in which previous requests prime the service to respond even more quickly to requests and determining LOS coverage over an area from a single location. High-throughput LOS analysis concepts make use of requests in batches to amortize the communications overhead over each batch. Data streaming reduces overhead, while binary data formats are used to reduce message sizes. Producing a microservice for NOMS will require a thorough understanding of NOMS business practices, the cloud architecture being used and how a service is provisioned to operate in that cloud. The Phase I effort will focus on achieving that understanding and applying our experience and proposed approaches to provide an optimal solution that integrates well into this framework.
