Indoor positioning systems based on Received Signal Strength Indicator (RSSI) in Wireless Sensor Networks (WSNs) are commonly used. The position accuracy in these systems is highly affected by the wireless medium variability, and therefore, a precise calibration is necessary to translate the power measurements to corresponding distance between each pair of nodes. In this paper, we propose a calibration scheme that is tailored to Body Area Networks (BANs) applications. The a priori knowledge about the environment conditions in these applications can increase the accuracy of the localization system, improve its robustness to interference, and reduce the number of RSSI measurements which are required for the calibration process compared to the traditional calibration methods. We define a criterion to obtain the calibration scheme using different a priori knowledge for both the mapping table and the path-loss model parameters. For evaluation of our new calibration scheme, we conducted a series of experiments in a real-world indoor environment, focusing on a proximate environment that is commonly used in BANs. We showed that for a tracking application, calibration methods utilizing the a priori knowledge are superior in terms of localization accuracy over other existing calibration methods with relatively small number of offline measurements.