This is the fifth part of a series of papers that provide a comprehensive survey of techniques
for tracking maneuvering targets without addressing the so-called measurement-origin …

In this paper, an optimization-based adaptive Kalman filtering method is proposed. The
method produces an estimate of the process noise covariance matrix Q by solving an …

Estimation for discrete-time stochastic systems with parameters varying in a continuous
space is considered in this paper. Justified by an analysis of model approximation, a novel …

A scheme is proposed to detect, identify, and estimate failures, including abrupt total, partial,
and multiple failures, in a dynamic system. The new approach, named IM ^3 L, is developed …

Multiple-model approach provides the state-of-the-art solutions to many problems involving
estimation, filtering, control, and/or modeling. One of the most important problems in the …

The widely used Bayesian filtering has a solid theoretical foundation and an efficient
computational architecture, but it suffers from first-order Markovianity and the inability to …

The aim of this thesis is to present a collection of multiple model (MM) algorithms capable of
single or multiple target tracking by solving one or both target motion and measurement …

摘要通过梳理**年信息融合理论的发展, 分析了复杂目标跟踪系统中存在的非线性, 多模式,
深耦合, 网络化, 高维数和未知扰动输入等问题, 指出现阶段目标跟踪系统中联合优化的必要性 …

The traditional multiple model cardinalized probability hypothesis density (MMCPHD) filter
uses a fixed model set for all targets. It is clearly inefficient and may cause the decrease of …

A new approach, referred to as best model augmentation (BMA), for variable-structure
multiple-model (VSMM) estimation is presented. Here the original set of models is …