Data Fusion Using Bayesian Inference for Enhanced Positioning

In this text, the author discusses the method of using Bayesian inference for data fusion to enhance positioning results from CHAN and Taylor algorithms. However, this approach is not exclusive, and alternative data fusion techniques such as Kalman filtering and particle filtering could also be implemented. The author could further elaborate on the principles and applications of Bayesian inference, including its implementation through probabilistic updates using prior distributions and likelihood functions. Additionally, detailed explanations of CHAN algorithm's algebraic closed-form solution for time difference of arrival (TDoA) positioning and Taylor series expansion's iterative refinement approach would help readers better understand their respective advantages, limitations, and operational mechanisms. The discussion could be enriched by referencing relevant research outcomes and commercial applications, while addressing future research directions and challenges in multi-algorithm fusion. Such expansions would provide deeper insights, enabling readers to gain more comprehensive understanding and inspiration in this field.