Real-time rss-based indoor navigation for autonomous UAV flight

Sangjun Lee, Purdue University


Navigation for the autonomous flight of Unmanned Aerial Vehicles (UAVs) in an indoor space has attracted much attention recently. One of the main goals of an indoor navigation system is developing an alternative method to obtain position information that can replace or complement the global positioning system. While much research has focused on vision-based indoor navigation systems, this paper aims to develop a Received Signal Strength (RSS)-based navigation system, which is a more cost effective alternative. Then, the position and attitude of a UAV can be computed by the fusion of RSS measurements and measurements from the onboard inertial measurement unit. In order to improve the estimation accuracy, we first consider a mathematical model of the RSS-based navigation system and formulate optimization problems to compute the parameter values which minimize the RSS measurement error. Using the optimal parameters, an autonomous flight system is developed whose estimator and controller components are designed to work well with the RSS-based navigation system. Simulations and experiments using a quadrotor demonstrate the feasibility and performance of the proposed RSS-based navigation system for UAVs operating in indoor environments.




Hwang, Purdue University.

Subject Area

Aerospace engineering|Mechanical engineering

Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server