Social Doppler: A Framework for Mechatronic Perception Beyond Physical Radar

In traditional embedded mechatronic systems, sensors are mainly focused on capturing the geometric and dynamic states of the physical world. However, in typical Cyber Physical Social Systems scenarios such as urban transportation and service robotics, physical sensing alone is not sufficient to capture shifts in public attention, emotional changes, and the evolution of public opinion. This paper proposes a Social Radar Doppler perception framework designed for embedded systems. By drawing an analogy with the classical Doppler effect in radar systems, the framework establishes a quantifiable model for the speed, direction, and dynamic features of social emotions and topics. In addition, we introduce a closed-loop 6S embedded social radar perception architecture. This architecture includes modules for data acquisition, semantic integration, emotion tracking, and strategy feedback. This framework can be broadly applied to support early decision-making in socially intelligent agents and provides a new perceptual dimension for future systems that deeply integrate humans, machines, and society.