This paper investigates the hybrid source localization problem using the four
radio measurements - time of arrival (TOA), time difference of arrival (TDOA),
received signal strength (RSS) and angle of arrival (AOA). First, after
invoking tractable approximations in the RSS and AOA models, the maximum
likelihood estimation (MLE) problem for the hybrid TOA-TDOA-RSS-AOA data model
is derived. Then, in the MLE, which has the least-squares objective, weights
determined using the range-based characteristics of the four heterogeneous
measurements, are introduced. The resultant weighted least-squares problem
obtained, which is non-smooth and non-convex, is solved using the principle of
the majorization-minimization (MM), leading to an iterative algorithm that has
a guaranteed convergence. The key feature of the proposed method is that it
provides a unified framework where localization using any possible merger out
of these four measurements can be implemented as per the
requirement/application. Extensive numerical simulations are conducted to study
the estimation efficiency of the proposed method. The proposed method employing
all four measurements is compared against a conventionally used method and also
against the proposed method employing only limited combinations of the four
measurements. The results obtained indicate that the hybrid localization model
improves the localization accuracy compared to the heterogeneous measurements.
The integration of different measurements also yields good accuracy in the
presence of non-line of sight (NLOS) errors