This is the second paper in a series on a new initiative of wireless tomography. The goal is to combine two areas: wireless communication and radio tomography. This paper studies wireless tomography from a system engineering's point of view. Machine learning and waveform diversity will be applied to wireless tomography. The potential system architecture for wireless tomography will also be given.

Wireless tomography, a novel approach to remote sensing, is proposed in Part I of this series. The methodology, literature review, related work, and system engineering are presented. Concrete algorithms and hardware platforms are implemented to demonstrate this concept. Self-cohering tomography is studied in depth. More research will be reported, following this initiative.

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