Maximum correlation coefficient estimation (MCORE): A new estimation philosophy for RSS based target localization.

This study proposes a new estimation philosophy i.e. Maximum Correlation Coefficient Estimation (MCORE) which defines a totally new objective function for received signal strength (RSS) based target localization. The transmit power and the path loss exponent can be simultaneously unknown in case of non-cooperative scenarios and instable environmental factors, which makes RSS based localization a challenging task. Previous studies depend on maximizing likelihood or posterior functions, minimizing nonlinear or weighted least squares objective functions and finally simplified or linearized versions of these methods. Unlike these studies, MCORE suggests to maximize the correlation coefficient between the measured and the estimated RSS values while estimating the location of the target. With MCORE, localization can be performed without having to determine the transmit power of the source and the path loss exponent. Simulations show that MCORE and Fast MCORE (fast version of MCORE proposed for stationary sensors) attain Cramer Rao Lower Bound with dramatically reduced execution times. Experiments with Xbee Modules and Keysight Handheld Analyzer show that MCORE is a feasible method for real RSS data. Finally, an important simulation about RSS based aircraft localization is presented to show that MCORE is quite successful in three dimensional RSS based localization. (C) 2020 Elsevier B.V. All rights reserved.