This chapter provides an in‐depth look at sensor fusion methods and algorithms, starting with an introduction to the estimation philosophy that guides the design of fusion systems. It covers the Gauss‐Markov process model, which is pivotal in state estimation, and explores advanced state estimation techniques. The chapter integrates machine learning and artificial intelligence approaches, highlighting their role in sensor fusion. It concludes with multi‐sensor temporal and spatial extrinsic calibration, offering techniques for aligning data from multiple sensors in time and space with relevant problems and references.
Sensor Fusion Methods and Algorithms
2025-01-02
65 pages
Article/Chapter (Book)
Electronic Resource
English
sensor fusion , estimation philosophy , Gauss‐Markov process , state estimation , machine learning , AI , calibration , Kalman filter , particle filter , artificial neural networks (ANN) , convolutional neural networks (CNN) , recurrent neural networks (RNN) , genetic algorithms (GA) , adaptive neuro‐fuzzy inference (ANFIS)
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