Eigen Value Analysis in Lower Bounding Uncertainty of Kalman Filter Estimates

受控噪声下多源鸟瞰图检测的不确定性感知后期融合算法评估

Evaluation of an Uncertainty-Aware Late Fusion Algorithm for Multi-Source Bird's Eye View Detections Under Controlled Noise

本文提出统一卡尔曼融合（UniKF）算法，通过受控噪声评估框架隔离融合过程，在多源BEV检测中实现定位误差最多降低3倍、尺寸误差降低2倍，并保持99.5%至100%的精确率和召回率。

Inference of Spatio-Temporal Functions over Graphs via Multi-Kernel Kriged Kalman Filtering

Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes, given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel-based kriged Kalman filter that accounts for the spatio-temporal variations, and offers efficient online reconstruction, even for dynamically evolving network topologies. The kernel-based learning framework bypasses the need for statistical information by capitalizing on the smo…

Tight Bounds for Uncertain Time-Correlated Errors with Gauss-Markov Structure

Safety-critical navigation applications require that estimation errors be reliably quantified and bounded. This can be challenging for linear dynamic systems if the process noise or measurement errors have uncertain time correlation. In many systems (e.g., in satellite-based or inertial navigation systems), there are sources of time-correlated sensor errors that can be well modeled using Gauss-Markov processes (GMP). However, uncertainty in the GMP parameters, particularly in the correlation time constant, can cause misleading error estimation. In this paper, we develop new time-correlated mod…

一种无需积分的粒子流滤波方法

An integration-free approach for particle flow filtering

本文提出了一种无需积分的粒子流滤波方法，通过将ODE转换为特定的本征空间，推导出闭合形式的代数表达式，并证明其等价于精确的卡尔曼测量更新，从而在非线性测量模型中实现了高效且稳定的粒子更新。