However, currently, systems may be dynamic with measurements made from moving platforms at consistent time intervals such as in autonomous navigation. Until the middle 20th century, systems were generally static, and measurements were constant with respect to time. Kalman filter can be considered an extension of Gauss’ original development of least squares to estimate unknown parameters of a model. The efficiency of the Kalman filter is due to its inexpensive computational requirements, well-designed recursive properties, representation of the optimal estimator for one-dimensional linear systems assuming Gaussian error statistics, and suitability for real-time implementation. The Kalman filter or the linear quadratic estimation (LQE) is nevertheless one of the most significant and common sensor and data fusion algorithms today. Kalman who in 1960 published his famous research “A new approach to linear filtering and prediction problems”. Kalman filter is named with respect to Rudolf E. Bashar Alsadik, in Adjustment Models in 3D Geomatics and Computational Geophysics, 2019 10.1 Introduction
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