Multi-Target State Extraction for the SMC-PHD Filter
نویسندگان
چکیده
منابع مشابه
Multi-Target State Extraction for the SMC-PHD Filter
The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter has been demonstrated to be a favorable method for multi-target tracking. However, the time-varying target states need to be extracted from the particle approximation of the posterior PHD, which is difficult to implement due to the unknown relations between the large amount of particles and the PHD peaks representing pot...
متن کاملCubature Information SMC-PHD for Multi-Target Tracking
In multi-target tracking, the key problem lies in estimating the number and states of individual targets, in which the challenge is the time-varying multi-target numbers and states. Recently, several multi-target tracking approaches, based on the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter, have been presented to solve such a problem. However, most of these approaches...
متن کاملBox-Particle PHD Filter for Multi-Target Tracking
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...
متن کاملConvergence of the SMC Implementation of the PHD Filter
The probability hypothesis density (PHD) filter is a first moment approximation to the evolution of a dynamic point process which can be used to approximate the optimal filtering equations of the multiple-object tracking problem. We show that, under reasonable assumptions, a sequential Monte Carlo (SMC) approximation of the PHD filter converges in mean of order p ≥ 1, and hence almost surely, t...
متن کاملClutter Removal in Sonar Image Target Tracking Using PHD Filter
In this paper we have presented a new procedure for sonar image target tracking using PHD filter besides K-means algorithm in high density clutter environment. We have presented K-means as data clustering technique in this paper to estimate the location of targets. Sonar images target tracking is a very good sample of high clutter environment. As can be seen, PHD filter because of its special f...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Sensors
سال: 2016
ISSN: 1424-8220
DOI: 10.3390/s16060901