Multi-sensor filtering fusion meets censored measurements under a constrained network environment: advances, challenges and prospects

Multi-sensor filtering fusion (MSFF) is a fascinating subject in the realm of networked filtering due to its advantage of effectively integrating sensor outputs from multiple sources. Owing to the massive usage of low-cost commercial and off-the-shelf sensors, MSFF could be easily prone to a very special kind of measurement nonlinearity named censored measurements. Meanwhile, taking into account the limited network resources, data transmission in a networked environment is unavoidably subject to communication constraints. As such, it would be quite interesting to examine the impacts from both censored measurements and communication constraints onto MSFF and moreover, develop certain suitable MSFF schemes to accurately reconstruct system states of interest. In this paper, we aim to provide a bibliographical review on MSFF problems with censored measurements under a constrained network environment. Canonical MSFF approaches are first surveyed and subsequently, the mathematical models and handling strategies of the censored measurements are systematically recaped. Later on, typical communication constraints are introduced in detail according to their respective engineering backgrounds, occurring manners and modelling strategies. In addition, latest MSFF progress is discussed at great length and the underlying challenges are also clearly highlighted. Finally, general concluding remarks along with possible future directions are explicitly pointed out.

Automated summary

Multi-sensor filtering fusion (MSFF) is a fascinating subject in the realm of networked filtering due to its advantage of effectively integrating sensor outputs from multiple sources. This paper aims to provide a bibliographical review on MSFF problems with censored measurements under a constrained network environment, including canonical approaches, mathematical models, handling strategies, communication constraints, latest progress, challenges, and future directions.