Application of Firefly Algorithm to UWB Indoor Positioning

Indoor positioning systems have been playing an important role in positioning technology research. This kind of technology can be used in many fields in our daily life. For example, people can use it to help firefighters find people in the fire scene, help managers to monitor robots' track in a factory, and so on. And there are many technologies that could be used to locate people or items indoors, such as Bluetooth, ZigBee, and Ultra-wideband (UWB). UWB technology has characteristics of high precision and low power consumption. In this project, we compare the performance of other technologies with UWB in an indoor positioning system, especially their precision in indoor positioning. Some channel characteristics such as Time of Arrival (TOA), direction of arrival (DOA), received signal strength (RSS) will be measured to analyze the position and motion of people or items indoors. In this paper, we improve the performance of a swarm intelligence algorithm in order to make it have higher efficiency and more accuracy to locate indoor items with static states. And then, for dynamic items, we use the Kalman filter algorithm and try to improve its performance to make the track we get in the positioning system closer to the actual track. Through simulation of position and track of items, we could prove the high precision of this indoor positioning system.