A Bayesian Model Based on Link Distribution Features for Multitarget Passive Localization in Visible Light Sensing

Passive localization using visible light sensing has been considered as a promising solution for indoor human detection. A major challenge is to avoid false target positioning in multi-target positioning scenarios. Besides, a reasonable probable target area model is also critical to the accuracy of positioning and counting targets. In this article, a novel passive localization scheme is proposed to locate multiple targets. This scheme introduces a rectangular probable target area model, which is used to calculate a rectangular area containing the potential location of the target to be located. Compared with existing models, it is more suitable for positioning scenarios under low-density deployment of sensing nodes. Furthermore, we present an improved successive cancellation (SC) algorithm to excluding false target localization. To determine the authenticity of targets by the SC algorithm, a Bayesian model is introduced to optimize the SC algorithm according to the multidimensional shad-owed link information of candidate targets. Numerous simulation results show that the proposed multi-target passive localization scheme can improve the problem of false target localization in multi-target localization scenarios. And it also can achieve outstanding performance in localization accuracy.