A Gentle Approach to Multi-Sensor Fusion Data Using Linear Kalman Filter
<|reference_start|>A Gentle Approach to Multi-Sensor Fusion Data Using Linear Kalman Filter: This research paper delves into the Linear Kalman Filter (LKF), highlighting its importance in merging data from multiple sensors. The Kalman Filter is known for its recursive solution to the linear filtering problem in discrete data, making it ideal for estimating states in dynamic systems by reducing noise in measurements and processes. Our focus is on linear dynamic systems due to the LKF's assumptions about system dynamics, measurement noise, and initial conditions. We thoroughly explain the principles, assumptions, and mechanisms of the LKF, emphasizing its practical application in multi-sensor data fusion. This fusion is essential for integrating diverse sensory inputs, thereby improving the accuracy and reliability of state estimations. To illustrate the LKF's real-world applicability and versatility, the paper presents two physical examples where the LKF significantly enhances precision and stability in dynamic systems. These examples not only demonstrate the theoretical concepts but also provide practical insights into implementing the LKF in multi-sensor data fusion scenarios. Our discussion underscores the LKF's crucial role in fields such as robotics, navigation, and signal processing. By combining an in-depth exploration of the LKF's theoretical foundations with practical examples, this paper aims to provide a comprehensive and accessible understanding of multi-sensor data fusion. Our goal is to contribute to the growing body of knowledge in this important area of research, promoting further innovations and advancements in data fusion technologies and encouraging their wider adoption across various scientific and industrial fields.<|reference_end|>
@article{veysi2024a,
    title={A Gentle Approach to Multi-Sensor Fusion Data Using Linear Kalman Filter},
    author={Parsa Veysi, Mohsen Adeli, Nayerosadat Peirov Naziri, and Ehsan Adeli},
    journal={arXiv preprint arXiv:2407.13062},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13062},
    primaryClass={cs.CY cs.CE}
}