Bluetooth low energy (BLE) beacons can be easily deployed to be used by a wide range of indoor localization techniques. BLE-beacon-based indoor localization techniques use the received signal strength indicator (RSSI) of the beacon signals. The variations of RSSI in an indoor environment is unpredictable and make the indoor localization challenging. Earlier researches considered that the distribution of RSSI in shadowing pathloss model is a normal (Gaussian) in the logarithmic scale. In an indoor environment, signals arrive at the receiver via various paths each having RSSI with Gaussian distribution. In this paper, we show that the aggregated magnitude of the indoor multipath signals at the receiver can also be modeled with Nakagami-m distribution. We investigate the performance of a BLE-based indoor localization technique using the maximum likelihood estimation (MLE) and the shadowing pathloss model with Nakagami-m distribution. We compare the location estimation performance when Gaussian distribution is used. We also collect data from a BLE test-bed and analyze the distribution of the RSSI measurements. The root means square error of the estimated position error for Nakagami-m based MLE solution is consistently lower than that of the Gaussian-based technique. In the computational time comparison, Nakagami-m distribution also shows a better performance speeding up the location estimation process by almost eight times.