Application of Variational Bayesian Filtering Based on T-Distribution in BDS Dynamic Ambiguity Resolution

In dynamic environments, the traditional relative positioning methods based on the Kalman filter model suffer from low accuracy and stability due to the influence of noise and outliers. This paper proposes a variational Bayesian filtering algorithm based on the combination of four-frequency observations from BDS (BeiDou Navigation Satellite System) and models the observation noise using the T-distribution to enhance the stability of filtering. Firstly, a geometrically correlated ambiguity resolution model is constructed based on the characteristics of the combined observations, effectively improving the precision of float ambiguity resolution and fixing rate. Moreover, considering the characteristics of outliers that are likely to occur in dynamic conditions, a T-distribution-based variational Bayesian filtering approach is employed to estimate the time-varying observation noise and system states. Experimental results demonstrate that the proposed method exhibits robustness and stability in dynamic short baseline scenarios, leading to further improvements in positioning accuracy, float ambiguity resolution precision, and fixing rate

Forest plot to meta-analyze ORR for neoadjuvant therapy with or without capecitabine.

<p>Forest plot to meta-analyze ORR for neoadjuvant therapy with or without capecitabine.</p