Enhanced Wireless Localization Based on Orientation-Compensation Model and Differential Received Signal Strength

The orientation- and device-dependent received signal strength (RSS) diversity has become a challenge for improving performance of localization using low-cost Internet of Things (IoT) devices. The existing orientation-compensation methods are based on the principle of database matching; thus, they are susceptible to performance degradation if the testing node orientation does not exist in the training data. To alleviate this issue, the orientation-compensation model (OCM) is proposed to compensate for the orientation-dependent RSS diversity. In contrast to the existing orientation-compensation methods that involve only the device orientation, the proposed method presents the device and anchor (i.e., base station) deflection angles and uses these angles as variables for the OCM. The introduction of these angles can significantly reduce the requirement for device orientations in the training data. Furthermore, based on the characteristics of the OCM and differential RSS (DRSS), this paper proposes an OCM/DRSS integrated data-processing strategy. This strategy is demonstrated to be effective in enhancing localization in IoT applications that have multiple devices with three-dimensional orientation diversity.