Estimating nonlinear systems is crucial for many real-world applications. Researchers compared three popular methods for this: Extended Kalman Filter, Unscented Kalman Filter, and Particle Filter. Particle Filter, a newer method, is good at handling complex nonlinear problems. They tested these methods on growth and tracking models. Through simulations, they found that Particle Filter was more accurate and consistent, but also slower computationally.