发表机构
The University of Texas at El Paso（埃尔帕索德克萨斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CCOD框架，通过重构DARE输入直接预测稳态估计误差协方差，确定满足估计不确定性边界的最大观测抽取因子，在保证性能的同时降低了传感器融合的计算与通信成本。
AI 中文摘要
观测抽取常被用于状态估计中，以降低传感、通信和计算需求，但降低测量同化频率会增大估计不确定性。因此选择合适的观测抽取因子需要准确预测估计器性能。离散代数里卡蒂方程（DARE）可提供标准线性时不变卡尔曼滤波器的稳态估计误差协方差，但不适用于采用抽取测量更新的估计器。现有方法通过提升系统表示或周期里卡蒂方程公式解决该问题，二者均会增加计算复杂度。本文提出协方差约束观测抽取（CCOD）框架，通过等效抽取系统和过程噪声矩阵重构DARE输入，该矩阵可捕捉测量更新间的协方差增长。此重构可通过单次DARE评估直接预测稳态估计误差协方差，无需增大系统维度或求解耦合周期里卡蒂方程。所得协方差预测用于确定满足规定估计不确定性边界的最大观测抽取因子。对高维线性时不变系统和空间目标跟踪应用的验证表明，该方法可准确预测稳态估计器性能，同时降低满足指定协方差约束所需的测量同化频率。
英文摘要
Observation decimation is frequently employed in state estimation to reduce sensing, communication, and computational requirements, but decreasing the measurement assimilation frequency increases estimation uncertainty. Selecting an appropriate observation decimation factor therefore requires accurately predicting the resulting estimator performance. While the discrete algebraic Riccati equation (DARE) provides the steady-state estimation-error covariance for standard linear time-invariant Kalman filters, it is not directly applicable to estimators employing decimated measurement updates. Existing approaches address this limitation through lifted system representations or periodic Riccati equation formulations, both of which incur additional computational complexity. This paper presents a covariance-constrained observation decimation (CCOD) framework that reformulates the DARE inputs using equivalent decimated system and process-noise matrices that capture covariance growth between measurement updates. The proposed reformulation enables direct prediction of the steady-state estimation-error covariance through a single DARE evaluation without increasing the system dimension or solving coupled periodic Riccati equations. The resulting covariance prediction is used to determine the maximum observation decimation factor that satisfies a prescribed estimation uncertainty bound. Validation using a high dimensional linear time-invariant system and a space object tracking application demonstrates that the proposed approach accurately predicts steady-state estimator performance while reducing the measurement assimilation frequency required to satisfy specified covariance constraints.
