Research and comparison of algorithms based on multi-modal fusion

Abstract

Abstract A multi-modal fusion algorithm is an important method for information fusion on multi-modal data provided by different sensors. It can make full use of the advantages of multiple sensors and improve the accuracy and robustness of data processing and decision-making. This paper aims to study the performance difference between the extended Kalman filter (EKF) algorithm and other algorithms in multi-modal fusion and explore a method to fuse multiple algorithms further to improve the accuracy of fusion results. The author uses the classic test set data set for experiments to evaluate the performance of different algorithms. By comparing and analyzing the performance of each algorithm in data fusion tasks, we can get their advantages and disadvantages in different scenarios. Among them, the extended Kalman filter algorithm is a classical algorithm based on Bayesian filtering, which can estimate the system state through recursive state estimation and covariance update. In addition, the author also uses linear regression, random forest, and other algorithms to compare. Then, the author uses several test sets to evaluate the performance of each algorithm. Through a comprehensive analysis of the MSE and MAE errors output by the algorithm, the applicability, advantages, and disadvantages of each algorithm in different scenarios are obtained.