We review some advances of the particle filtering (PF) algorithm that have been achieved in the last decade in the context of target tracking, with regard to either a single target or multiple targets in the presence of false or missing data.

Apr 26, 2012 · In this estimation task, the joint (all-sensors) likelihood function (JLF) plays a central role as it epitomizes the measurements of all sensors. We propose a distributed method for computing, at each sensor, an approximation of the JLF by means of consensus algorithms.

In the standard particle filter (Algorithm 2), step one (prediction) is to randomly draw samples and step two is updating weights using a measurement and the predicted particle states from step one (see Algorithm 2).

May 15, 2024 · We investigate a novel adaptive multiple importance sampling (AMIS) algorithm combining parallel tempering Monte Carlo (PTMC) samplers to achieve the sampling-based consensus density fusion of the non-Gaussian local posteriors and estimate the global posterior locally on each node.

For the par-ticle filter, the commonly used distributed fusion method is the averaged consensus algorithm [10, 11, 12]. The crux of averaged consensus is to iteratively exchange in-formation among neighborhood sensors until all of them reach an agreement on some consensus states.

This paper introduces a distributed filter that allows the nodes of a sensor network to track the average of n sensor measurements using an average consensus based distributed filter called consensus filter.

In this paper, each mobile sensor runs a local particle ¿lter to estimate the position of a moving target. To improve the accuracy of the estimation and reduce the inter-sensor communication, a...

Fusion filter: The paper proposes a consensus/fusion based distributed implementation of the particle filter (CF/DPF) for non-linear systems with non-Gaussian excitation. In addition to the localized particle

Abstract: We describe in this paper novel consensus-based distributed particle filtering algorithms which are applied to cooperative blind equalization of frequency-selective channels in a network with one transmitter and multiple receivers. The proposed algorithms employ parallel consensus averaging iterations to evaluate the product of some ...

Jun 17, 2009 · This paper considers the problem of distributed particle filtering using consensus algorithms. The monitored environment may possess nonlinear dynamics, nonlinear measurements, and non-Gaussian process and observation noises.