  Device-free localization (DFL) based on pyroelectric infrared (PIR) sensors
has attracted much attention due to its advantages of low cost, low power
consumption, and privacy protection. However, most existing PIR-based DFL
methods require high deployment density to achieve high localization accuracy.
Recently, a few works proposed that the deployment density can be reduced by
deeply analyzing the analog output of PIR sensors. However, these methods can
not well handle the localization task in multi-person scenarios yet. In this
paper, we propose a novel neural network for PIR-based multi-person
localization, which appropriately leverages a series of domain knowledge.
Specifically, the proposed network consists of two modules: one is for
determining the number of persons and another is for determining their
locations. Meanwhile, the module of person counting is further designed as a
two-stage network: one stage is for signal separation and another is for
single-person detection. The module for localization is also designed as a
two-stage network: one stage is for signal extraction and another is for
single-person localization. Through the above methods, we succeed to remarkably
reduce the deployment density of the traditional PIR-based method by about
76\%, while maintaining the localization accuracy.
