AT TOPPaper 9009 CS-T-DDOWN
Modeling of the SLICE UGS Network
Yoon,C.J. ITT Network Systems
Li,Chris ITT Network Systems
Liu,Y.J. ITT Network Systems
Lau,Connie ITT Network Systems
Cruz,Christine ITT Network Systems
A SLICE (Soldier Level Integrated Communications Environment) UGS (Unattended Ground Sensor) radio network consists of Pointer nodes (P nodes) and Recognition nodes (R nodes). The P nodes represent short range radios reporting the sensor data to their respective R nodes. The R nodes represent long range radios performing data fusion and correlation, image processing and target classification and send information to the Command and Control (C2) vehicle. The main purpose of the UGS network is to detect, identify, and track enemy targets. The UGS model developed in parallel with the dismounted version of the SLICE radio was a key to the success of the systems tests and associated demo because it allowed us to test and develop a full understanding of the field scenario beforehand. The planned paper describes the UGS OPNET model. In the UGS OPNET model, all layers of the radio’s protocol stack are modeled in detail. The model employs ITT’s code-sharing architecture whereby the intranet layer of the model uses the same intranet code developed for the physical platforms. With the inclusion of the actual system software in the model, the UGS software development team was able to use the model as a development tool for software testing and debugging before the same software was integrated with the UGS hardware. This application of the model has resulted in significant reduction of the development cost and risks throughout the UGS development cycle. In addition to serving as a vehicle for software development, the model was used for protocol verification & validation and network performance prediction. The model is validated in terms of message completion rates (MCR) collected during the Lab tests. The scenarios used in the Lab tests were used in the OPNET simulations as well. The MCR results obtained from the Lab tests and the model agreed to within 6 % for both data and image.