Angle of arrival estimation system for radiation pattern reconfigurable antenna with modified gaussian membership function

Abstract

This research focuses on developing angle of arrival (AOA) estimation system (AES) through incoming received signal strength indication (RSSI). Proposed AES is developing on a single board computer (SBC) using an open source GNU Linux operating system (OS). The good AOA estimation systems must able to covered 360° with many estimation points. However, previous AOA estimation systems prototype suffers from a major drawback to achieve 360° angle covered due to limitation of antenna radiation pattern. Therefore, radiation pattern reconfigurable (RPR) antenna operates at 2.45 GHz that capable to cover the wide AOA estimation angle is introduced. Nevertheless, the RPR antenna on AES prototype provides lack of estimation points. Thus, this thesis infuses Fuzzy Inference System (FIS) to further improve decision making and increase the number of AOA estimation points. The FISAES is the first effort in realizing the advantages of FIS with only three sets of RPR antenna abilities without any intelligent algorithm support to cover 360° angle estimation. The FIS-AES algorithm is develop by Python 2.7 programming supported by SciKit library. The proposed Fuzzy Inference System of AOA estimation system (FIS-AES) successfully increases number of estimation from nine to 18 estimation points. Four types of membership function (MF) are trained to obtain response between fuzzifier and defuzzfier of FIS-AES algorithm. A novel MF based Gaussian-MF curve named as the Pattern-MF is introduced. The response between fuzzifier and defuzzfier of FIS-AES algorithm of proposed Pattern-MF approximately ~80% to ~85%, which is the highest compared than existed MF in SciKit library. Moreover, adopted the FIS offers more AOA estimation points, thus it helps FIS-AES capable to improve the absolute error of AOA estimation and root mean square error (RMSE) is ±5° and less than 10 respectively. The investigation of SBC performance is important to verify that SBC competent to act as the main platform of AES. The SBC performance is verified in terms of CPU and memory utilization. In this work, the Raspberry-Pi has successfully completed all tasking with average CPU and average memory utilization less than 10% and less than 31% respectively for S11 measurement and less than 10% and less than 37% respectively for AES measurement. With all capabilities demonstrated and discussed, the FIS-AES have great potential as one of the best options for realizing applications such as localization system man computer interaction.