Linear closed-form estimator for sensor localisation using RSS and AOA measurements

Abstract

Using the hybrid received signal strength (RSS) and angle of arrival (AOA) measurements, a position estimation model is proposed for sensor localisation in three-dimensional plane. Then the unconstraint linear least square (ULLS) and constraint linear least square (CLLS) estimators are designed to obtain the closed-form solutions to the positions of source nodes by considering the known transmit power. When the transmit power is unavailable, a global linear least square (GLLS) estimator is also put forward to estimate the positions of source nodes along with the transmit power. The simulations show that the computational complexity of the proposed linear estimators is greatly lower than that of the convex semidefinite programming (SDP) method. When the measurement noises are small, the linear ULLS, CLLS and GLLS estimators perform better than that of the SDP method. Due to the exploiting of constraint condition, the accuracy performance of the CLLS estimator can approach the Cramer-Rao lower bound (CRLB) of position estimation.