A Novel Method for Passive Objective Adaption Localization using Sensing Link

The passive object localization problem (POLP) aims to detect the location of the target. This task requires the target does not have any device to receive signal or transfer. For this reason, the data type used for the passive object localization is limited in WSN. The great challenge is how to use rare data to estimate the location in the large area. In this paper, based on our preliminary work, we propose a novel approach to localize objects which is referred as SLAL(Sensing Link Adaptive Localization) in which we can judge the location attribute by the adaption rule, calculate the target position by the diffraction model and point out the area by the RSSI affected. Compared with the traditional methods, our major contribution is that study the diffraction model and the affected degree of RSSI, ensure the localization accuracy with the link length extending. Experimental results demonstrate that the localization accuracy of SLAL outperforms previous methods. Laid the foundation for improve the accuracy in further.