基于先验知识与递归卡尔曼滤波的智能体状态与输入联合估计
Joint State and Input Estimation of Agent Based on Recursive Kalman Filter Given Prior Knowledge
AI总结：
本文提出基于EM算法和先验知识约束的统一框架，联合估计连续与离散输入和状态，实验显示其在方差和决策准确率上显著优于KF与RKF。
摘要：
现代自主系统面向许多具有挑战性的场景，智能体在其中会遭遇意外事件和复杂任务。控制指令中存在的扰动噪声以及未知输入会对机器人性能产生负面影响。以往关于输入与状态联合估计的研究分别在没有任何先验信息的情况下研究了连续和离散情形。本文将连续和离散输入情形结合到一个基于期望最大化（EM）算法的统一理论中。通过引入事件的先验知识作为约束，构建不等式优化问题来确定增益矩阵或动态权重，从而实现具有更低方差和更准确决策能力的最优输入估计。最后，实验的统计结果表明，在连续空间中，我们的算法相比KF方差改善了81%，相比RKF改善了47%；在离散空间中，我们的输入估计器在正确决策概率方面取得了显著提升，辨识能力也通过实验进行了分析。
英文摘要：
Modern autonomous systems are purposed for many challenging scenarios, where agents will face unexpected events and complicated tasks. The presence of disturbance noise with control command and unknown inputs can negatively impact robot performance. Previous research of joint input and state estimation separately studied the continuous and discrete cases without any prior information. This paper combines the continuous and discrete input cases into a unified theory based on the Expectation-Maximum (EM) algorithm. By introducing prior knowledge of events as the constraint, inequality optimization problems are formulated to determine a gain matrix or dynamic weights to realize an optimal input estimation with lower variance and more accurate decision-making. Finally, statistical results from experiments show that our algorithm owns 81\% improvement of the variance than KF and 47\% improvement than RKF in continuous space; a remarkable improvement of right decision-making probability of our input estimator in discrete space, identification ability is also analyzed by experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
