The Model Research on Location Fusion Algorithm with Big Data Selection and Accuracy Correction

Abstract

As the requirements of smart, reliable and precise location for a vehicle, the model of fusion location algorithm with big data selection and accuracy correction is established to achieve reliable and low-cost fusion location. In this paper, the data of simple inertial navigation and the data of various positioning system sources with different errors are intelligently selected, and Kalman filtering is used to fuse the location information by the function of algorithm model, and the four kinds of location information of GPS, SINS, DR and TDOA are chosen to simulate fusion algorithm. The simulation results in MATLAB environment show that the proposed fusion location algorithm model is efficient to improve the accuracy and reliability of the location algorithm.

OES-Fed: a federated learning framework in vehicular network based on noise data filtering

“… Seth, Swain & Mishra (2018) used the traditional Kalman filter to estimate the position and trajectory of a single target in motion, and obtained the actual trajectory by connecting the center of the obtained moving object image. For the processing of vehicle-related data in the IoV, Zhang et al (2018) used Kalman filter for the selection of data from simple inertial navigation and data from various positioning system sources with different errors, which can effectively improve accuracy and reliability. Exponential smoothing is a way to simplify the classification process.…”

“…Since federated learning is an emerging field, its use in handling noise data is rarely covered. So this article refers to Ahmed et al (2020) , Ye et al (2020) , Li, Wang & Guan (2019) , Xu et al (2022) , Seth, Swain & Mishra (2018) , Zhang et al (2018) for a comparative analysis of federated learning algorithms applied to different domains with the mechanism proposed in this article, as shown in Table 1 .…”

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

“…In that paper, researchers map heterogeneous data in different formats to a unified embedded vector space with deep restricted Boltzmann machine, achieving the efficient fusion of heterogeneous data sources. Furthermore, Zhang et al have published several recent works related to Big Data Fusion techniques using ensemble learning and Neural Networks as their core of research [156,157]. As a matter of fact, ensemble learning can also be conceived as a fusion of decisions made by the constituent models in the ensemble.…”