<p>State estimation is the process of inferring the internal state of a system based on measurable information, such as sensor data. Since the development of the Kalman filter in the 1960s, a representative state estimation algorithm, the applicability of state estimation has been extended from linear systems with Gaussian noise to nonlinear and non-Gaussian systems. In recent years, accurate state estimation has emerged as a key requirement across various application, such as renewable energy sources, lithium-ion batteries, unmanned aerial vehicles, and autonomous vehicles. The significance of nonlinear state estimation algorithms has been highlighted by the increasing complexity of these systems. This paper presents the fundamental principles of representative nonlinear state estimation algorithms and reviews the recent trends in related research.</p>