Real Time Aircraft Position Estimation Utilizing Apache Storm

Abstract

Each year, the amount of data that is produced in the digital universe is continuously increasing, so much so that some sources claim that the amount of data is doubled every two years. This type of data can be characterized as a form of Big Data in which it is described as containing the 3V’s properties: Volume, Velocity, and Variety. In terms of data processing, technologies like Apache Storm have emerged to provide a distributed real time computation system. Being able to process and provide insightful meaning of Big Data in a timely manner has become quite challenging. In some intended environments where seconds matter, real time processing might not be good enough and only provides an operator minimal time to react. This thesis proposes the use of Apache Storm’s real time processing engine that uses a Kalman Filter to provide estimation. The proposed approach provides a flexible architecture that leverages the real time processing engine for quick response but also provides an additional layer for estimation. By providing estimation, this allows for an operator to have more time to react based on trends seen within the data. Specifically, this thesis focuses on providing estimation to a location of an aircraft. One use case of this estimation solution could be utilized in preventing air traffic collisions. The estimation algorithm presented in this thesis is suited for predicting locations; however the architecture presented allows for the customization of different estimations for desired use case. Various test cases were executed to evaluate the overall system performance and determine if the proposed system would be viable to provide real time situational awareness and estimation. For the tests cases focused on the performance of Apache Storm, overall there was no degraded performance that present limitations on the proposed architecture. As for the estimation performance, the further out that the estimation was to predict the location there was an increasing associated error in estimating that location. An additional factor in contributing to error is the flight path of the aircraft.