Results 71 to 80 of about 1,611 (218)
BP Neural Network–Based Kalman Filtering Method Under Multiple Cyberattacks
This paper proposes a Kalman‐gain‐driven neural Kalman filtering (KF) defense framework, termed KFDBP, for secure state estimation in cyber–physical systems (CPSs) under denial‐of‐service (DoS), spoofing, and replay attacks. Unlike end‐to‐end neural filtering approaches such as KalmanNet that directly learn state estimators or implicitly approximate ...
Zijing Li +7 more
wiley +1 more source
A new algorithm for continuous-discrete filtering with randomly delayed measurements [PDF]
This paper is a postprint of a paper submitted to and accepted for publication in IET Control Theory & Applications and is subject to Institution of Engineering and Technology Copyright.
Bhaumik, S, Date, P, Singh, A
core
The implementation of Kalman filter (KF) in tracking high‐dimensional, strongly correlated graph structured data is often complex and unstable. Meanwhile, in practical applications, the system may be subject to interference from non‐Gaussian noise and various cyberattacks.
Bingyu Yin, Xinmin Song, Wenling Li
wiley +1 more source
A Novel Weighted Unscented Kalman Filter for Dynamic Load Identification
To address the limitations of traditional unscented Kalman filter (UKF)‐based algorithms—which typically require either additional displacement measurements or iterative optimization for load identification—this study proposes a fast and convenient load excitation identification algorithm.
Yanzhe Zhang +4 more
wiley +1 more source
A comparison of sigma-point Kalman filters on an aerospace actuator [PDF]
This paper contains a comparison of several sigma-point Kalman filters, including the unscented Kalman filter (UKF), the cubature Kalman filter (CKF), and the central difference Kalman filter (CDKF).
Al-Shabi M +3 more
core +1 more source
A new method for generating sigma points and weights for nonlinear filtering [PDF]
In this paper, a new method termed as new sigma point Kalman filter (NSKF), is proposed for generating sigma points and weights for estimating the states of a stochastic nonlinear dynamic system.
Bhaumik, S +3 more
core +1 more source
ABSTRACT We develop efficient hyperreduction methods for projection‐based model reduction of nonlinear partial differential equations (PDEs) with a large number of parameters and/or large parametric extents. Our formulation is based on the empirical quadrature procedure (EQP), which solves an optimization problem that involves “residual‐matching ...
Adrian Humphry, Masayuki Yano
wiley +1 more source
Multiple-sensor Fusion Tracking Based on Square-root Cubature Kalman Filtering
Nonlinear state estimation and fusion tracking are always hot research topics for information processing. Compared to linear fusion tracking, nonlinear fusion tracking takes many new problems and challenges. Especially, the performances of fusion tracking, based on different nonlinear filters, are obviously different.
openaire +1 more source
A probabilistic diagnostic for Laplace approximations: Introduction and experimentation
Abstract Many models require integrals of high‐dimensional functions: for instance, to obtain marginal likelihoods. Such integrals may be intractable, or too expensive to compute numerically. Instead, we can use the Laplace approximation (LA). The LA is exact if the function is proportional to a normal density; its effectiveness therefore depends on ...
Shaun McDonald, Dave Campbell
wiley +1 more source
A New Approach to Adaptive Signal Processing
A unified linear algebraic approach to adaptive signal processing (ASP) is presented. Starting from just Ax=b, key ASP algorithms are derived in a simple, systematic, and integrated manner without requiring any background knowledge to the field ...
Anjum, Muhammad Ali Raza
core +2 more sources

