AOA Measurement based Localization Using RLS Algorithm under NLOS Environment

In localization problem, the estimation accuracy of a target location is one of the most significant issues. The global positioning system (GPS) that is the most widely used localization method can be disrupted by some disturbances under non-line-of-sight (NLOS) condition such as indoor environment and downtown. In this paper, we suggest a localization algorithm using the angle of arrival (AOA) measurements. AOA measurement based localization is one of the most efficient geolocation methods that use a wireless active signal. Moreover, AOA method has an advantage that only two base stations are required to estimate the target location. In all kinds of localization methods using wireless signal, the NLOS and measurement noise are the significant problems that decrease an estimation accuracy of a target location. In this paper, we suggest a Kalman filter based hypothesis test and a recursive least square scheme to overcome NLOS and measurement noise problem, respectively. Using Kalman filter based hypothesis test, the measurement data set from each base station can be identified whether it contains the NLOS noise or not. Also, recursive least square (RLS) scheme can obtain the precise location of a target with rapid calculation speed when additional measurement data is received from auxiliary base stations. Simulation result confirms the high estimation accuracy and computational speed of our proposed scheme.