Eigen Value Analysis in Lower Bounding Uncertainty of Kalman Filter Estimates
In this paper we are concerned with the error-covariance lower-bounding problem in Kalman filtering: a sensor releases a set of measurements to the data fusion/estimation center, which has a perfect knowledge of the dynamic model, to allow it to estimate the states, while preventing it to estimate the states beyond a given accuracy. We propose a measurement noise manipulation scheme to ensure lower-bound on the estimation accuracy of states. Our proposed method ensures lower-bound on the steady state estimation error of Kalman filter, using mathematical tools from eigen value analysis.
Code (0)
등록된 구현이 없습니다.
Tasks
State EstimationSimilar Papers 제목 키워드 기반
An Improved and More Accurate Expression for a PDF Related to Eigenvalue-Based Spectrum Sensing
Cooperative spectrum sensing based on the limiting eigenvalue ratio of the covariance matrix offers superior detection performance and overcomes the noise uncertainty problem. While an exact expression exists, it is comp…
Eigenvalue Calibration for Semantic Embeddings of Large Language Models
Uncertainty quantification is central to the reliable deployment of large language models (LLMs), and eigenvalues of semantic embeddings have recently emerged as a key tool in state-of-the-art methods. However, conventio…
A note on the smallest eigenvalue of the empirical covariance of causal Gaussian processes
We present a simple proof for bounding the smallest eigenvalue of the empirical covariance in a causal Gaussian process. Along the way, we establish a one-sided tail inequality for Gaussian quadratic forms using a causal…
Gaussian ProcessesEigenvalue and Generalized Eigenvalue Problems: Tutorial
This paper is a tutorial for eigenvalue and generalized eigenvalue problems. We first introduce eigenvalue problem, eigen-decomposition (spectral decomposition), and generalized eigenvalue problem. Then, we mention the o…
BIG-bench Machine LearningMatrix Factorization / DecompositionTowards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles
In this paper, we propose a novel form of the loss function to increase the performance of LiDAR-based 3d object detection and obtain more explainable and convincing uncertainty for the prediction. The loss function was …
3D Object DetectionAutonomous Vehiclesobject-detectionObject Detection+1