标题： 自适应机器学习方法中使用粒子滤波进行状态估计：将 Q 学习和 NEAT 算法与噪声雷达测量相结合 显示英文标题

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

摘要： 可靠的状态估计对于在复杂、嘈杂环境中运行的自主系统至关重要。经典滤波方法，例如卡尔曼滤波器，在面对非线性动态或非高斯噪声时可能会遇到困难，而即使更加灵活的粒子滤波器在大规模领域中也常常会遭遇样本退化或计算成本高的问题。同时，自适应机器学习技术，包括Q学习和神经进化算法（如NEAT），严重依赖于准确的状态反馈来引导学习；当传感器数据不完美时，这些方法会出现收敛性能下降和次优表现的问题。本文提出了一种集成框架，将粒子滤波与Q学习和NEAT相结合，以明确解决噪声测量的挑战。通过改进基于雷达的观测值，我们的粒子滤波器驱动了更稳定的策略更新（在Q学习中）或控制器演化（在NEAT中），使强化学习和神经进化都能更快地收敛，获得更高的回报或适应度，并表现出更强的传感器不确定性鲁棒性。基于网格导航和模拟汽车环境的实验表明，在训练稳定性、最终性能和成功率方面相对于缺乏高级滤波技术的基线模型具有持续的优势。总体而言，这些发现强调了准确的状态估计不仅仅是预处理步骤，而是能够在受传感器噪声困扰的实际应用中显著提升自适应机器学习能力的关键组成部分。 显示英文摘要

摘要： Reliable state estimation is essential for autonomous systems operating in complex, noisy environments. Classical filtering approaches, such as the Kalman filter, can struggle when facing nonlinear dynamics or non-Gaussian noise, and even more flexible particle filters often encounter sample degeneracy or high computational costs in large-scale domains. Meanwhile, adaptive machine learning techniques, including Q-learning and neuroevolutionary algorithms such as NEAT, rely heavily on accurate state feedback to guide learning; when sensor data are imperfect, these methods suffer from degraded convergence and suboptimal performance. In this paper, we propose an integrated framework that unifies particle filtering with Q-learning and NEAT to explicitly address the challenge of noisy measurements. By refining radar-based observations into reliable state estimates, our particle filter drives more stable policy updates (in Q-learning) or controller evolution (in NEAT), allowing both reinforcement learning and neuroevolution to converge faster, achieve higher returns or fitness, and exhibit greater resilience to sensor uncertainty. Experiments on grid-based navigation and a simulated car environment highlight consistent gains in training stability, final performance, and success rates over baselines lacking advanced filtering. Altogether, these findings underscore that accurate state estimation is not merely a preprocessing step, but a vital component capable of substantially enhancing adaptive machine learning in real-world applications plagued by sensor noise.