<ul><li>Reliable state estimation is essential for autonomous systems operating in complex, noisy environments.</li><li>Classical filtering approaches, such as the Kalman filter, struggle with nonlinear dynamics and non-Gaussian noise.</li><li>An integrated framework is proposed that combines particle filtering with Q-learning and NEAT algorithms to address the challenge of noisy measurements.</li><li>Experiments show that the approach results in improved training stability, final performance, and success rates over baselines lacking advanced filtering.</li></ul>

State Estimation Using Particle Filtering in Adaptive Machine Learning Methods: Integrating Q-Learning and NEAT Algorithms with Noisy Radar Measurements

State Estimation Using Particle Filtering in Adaptive Machine Learning Methods: Integrating Q-Learning and NEAT Algorithms with Noisy Radar Measurements

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