In this paper, we propose a maximum likelihood estimator for received signal strength (RSS) based indoor localization systems by exploiting gamma shadow fading model. In order to investigate the validity of proposed method in a realistic environment, we develop a testbed based on Wi-Fi technology. Through experimental analyses, we first demonstrate the gamma distribution is a good fit to lognormal distribution, and both of them can sufficiently accurately characterize the empirical RSS observations. Then, we observe that gamma distribution is worth investigating for indoor localization compared to lognormal model because it provides superior accuracy. We further analyze the impacts of uncertainties of considered distributions' parameters on the localization performance via simulations.