Radar Sensor Fusion via Federated Unscented Kalman Filter

Fong, Li-Wei; Lou, Pi-Ching; Lu, Lidan; Cai, Peimao

doi:10.1109/icmic48233.2019.9068576

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…As a method to connect the silos of data, federated learning [88] enables participants to build models without sharing data to achieve swarm intelligence [89]. Under this framework, federated radars in Figure 7 use common data to train a model for multiple radars [90], while the federation of radars can realize model interactive and cooperative learning between different radars [91]. In federation of radars, data are aggregated in each client for local training.…”

Section: Federated Radarsmentioning

confidence: 99%

Parallel Radars: From Digital Twins to Digital Intelligence for Smart Radar Systems

Liu

Shen

Fan

et al. 2022

Sensors

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Radar is widely employed in many applications, especially in autonomous driving. At present, radars are only designed as simple data collectors, and they are unable to meet new requirements for real-time and intelligent information processing as environmental complexity increases. It is inevitable that smart radar systems will need to be developed to deal with these challenges and digital twins in cyber-physical systems (CPS) have proven to be effective tools in many aspects. However, human involvement is closely related to radar technology and plays an important role in the operation and management of radars; thus, digital twins’ radars in CPS are insufficient to realize smart radar systems due to the inadequate consideration of human factors. ACP-based parallel intelligence in cyber-physical-social systems (CPSS) is used to construct a novel framework for smart radars, called Parallel Radars. A Parallel Radar consists of three main parts: a Descriptive Radar for constructing artificial radar systems in cyberspace, a Predictive Radar for conducting computational experiments with artificial systems, and a Prescriptive Radar for providing prescriptive control to both physical and artificial radars to complete parallel execution. To connect silos of data and protect data privacy, federated radars are proposed. Additionally, taking mines as an example, the application of Parallel Radars in autonomous driving is discussed in detail, and various experiments have been conducted to demonstrate the effectiveness of Parallel Radars.

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