Using WLAN Infrastructure for Angle-of-Arrival Indoor User Location

TLDR: Investigation of the potential for future multiple antenna wireless local area network technologies such as 802.11n to perform indoor network-based positioning using angle of arrival (AOA) estimation shows that with the use of a specialized channel parameter estimation algorithm, quality AOA estimates for positioning might be achieved with low computational complexity.

Abstract: This paper investigates the potential for future multiple antenna wireless local area network technologies such as 802.11n to perform indoor network-based positioning using angle of arrival (AOA) estimation. A multiple-input multiple-output (MIMO) channel measurement system is used to determine the statistical accuracy of the indoor AOA estimation when performed at an 802.11n wireless access point (AP). Two different channel parameter estimation algorithms are used to perform AOA estimation; a simple maximum-likelihood (ML) scheme and the space-alternating generalized expectation-maximization (SAGE) technique. Results indicate that general channel parameter estimation algorithms, such as SAGE, are ill suited to estimate AOA for positioning purposes. However, results show that with the use of a specialized channel parameter estimation algorithm, such as the simple ML algorithm, quality AOA estimates for positioning might be achieved with low computational complexity. A positioning simulation that incorporates the AOA estimates from the simple ML algorithm achieves a positioning accuracy of 1.7 m with the help of an extended Kalman filter.