In this paper, we aim to perform scalable multi-target particle filter tracking. Previously, the authors presented an approach to track initiation and deletion which maintains an existence probability on each track, including a “search track” which represents the existence probability and state distribution of an unconfirmed track. This approach was seen to perform well even in cases of low det...

A new method has been developed to approximate one Gaussian mixture by another in a process that generalizes the idea of importance re-sampling in a particle filter. This algorithm is being developed as part of an effort to generalize the concept of a particle filter. In a traditional particle filter, the underlying probability density function is described by particles: Dirac delta functions w...

The particle filter for nonlinear state estimation of discrete time dynamic stochastic systems is treated. The functional sampling density of the particle filter strongly affecting estimate quality is studied. The density is given by weighted mixture of the transition probability density functions. The weights are calculated using distance of two reference variable probability density functions...

We have developed a new nonlinear filter that is superior to particle filters in five ways: (1) it exploits smoothness; (2) it uses an exact solution of the Fokker-Planck equation in continuous time; (3) it uses a convolution to compute the effect of process noise at discrete times; (4) it uses the adjoint method to compute the optimal density of points in state space to represent the smooth co...

An improvement of the standard “particle filter” (PF) Monte Carlo Bayesian estimator is presented and compared with an existing improved reweighted filter in a target tracking example. The PF updates the probability density function (pdf) of the state, represented as the density of state samples (particles). Each particle is time-updated by applying to the state equation a sample from the forci...

In this work we focus on the task to localize and track multiple non-cooperative targets by a passive antenna array and an optical sensor. Both sensor systems are mounted on a UAV and obtain bearing measurements from the targets, where the number of targets is unknown. To solve the localization and tracking problem, the imprecise but unique bearing data collected from the antenna array has to b...

uxiliary Sequential Importance Resampling Particle Filter is a recursive Bayesian filtering for nonlinear systems with non-Gaussian noise which uses the Monte Carlo method for calculating the posterior probability density functions. In this filter to estimate the system state, the current observations are used to approximate the proposed distribution function and causes particles to be located ...

We propose a novel online multi-object visual tracker using Gaussian mixture Probability Hypothesis Density (GM-PHD) filter and deep appearance learning. The GM-PHD has linear complexity with the number of objects observations while estimating states cardinality time-varying objects, however, it is susceptible to miss-detections does not include identity objects. use visual-spatio-temporal info...

This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets and estimates the unknown number of targets. Furthermore, it is capable to deal with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. The box-PHD filter reduces the numbe...