Particle filter and Gaussian mixture implementations of random finite set filters have been proposed to tackle the issue of jointly estimating the number of targets and their states. The Gaussian mixture PHD (GM-PHD) filter has a closed-form expression for the PHD for linear and Gaussian target models, and extensions using the extended Kalman filter or unscented Kalman Filter have been develope...

Structural system identification using recursive methods has been a research direction of increasing interest in recent decades. The two prominent methods, including the Extended Kalman Filter (EKF) and the Particle Filter (PF), also known as the Sequential Monte Carlo (SMC), are advantageous in this field. In this study, the system identification of a shake table test of a 4-story steel struct...

This paper deals with the problem of maneuvering target tracking in wireless tracking service. It results in a mixed linear/non-linear Models estimation problem. For maneuvering tracking systems, these problems are traditionally handled using the extended Kalman filter or Particle filter. In this paper, Marginalized Particle Filter is presented for applications in such problem. The algorithm ma...

In this paper, we propose a new particle filter based on sequential importance sampling. The algorithm uses a bank of unscented filters to obtain the importance proposal distribution. This proposal has two very "nice" properties. Firstly, it makes efficient use of the latest available information and, secondly, it can have heavy tails. As a result, we find that the algorithm outperforms standar...

The particle filter has emerged as a useful tool for problems requiring dynamic state estimation. The efficiency and accuracy of the filter depend mostly on the number of particles used in the estimation and on the propagation function used to re-allocate these particles at each iteration. Both features are specified beforehand and are kept fixed in the regular implementation of the filter. In ...

This letter is concerned with the development of a general scheme for box particle filtering. It based on likelihood computation, most crucial step estimation strategy. The proposed filter takes advantages from strong aspects various existing filters and adds an interesting reinforced computation method that enhances results. An overview Box Particle Filters discussions assumptions used in lite...

This paper develops a novel approach for multi-target tracking, called box-particle intensity filter (box-iFilter). The approach is able to cope with unknown clutter, false alarms and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. The box-iFilter reduces the number of p...