Extended Emitter Target Tracking Using GM-PHD Filter
نویسندگان
چکیده
منابع مشابه
Extended Emitter Target Tracking Using GM-PHD Filter
If equipped with several radar emitters, a target will produce more than one measurement per time step and is denoted as an extended target. However, due to the requirement of all possible measurement set partitions, the exact probability hypothesis density filter for extended target tracking is computationally intractable. To reduce the computational burden, a fast partitioning algorithm based...
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 کاملExtended Target Tracking using a Gaussian-Mixture PHD filter
This paper presents a Gaussian-mixture implementation of the PHD filter for tracking extended targets. The exact filter requires processing of all possible measurement set partitions, which is generally infeasible to implement. A method is proposed for limiting the number of considered partitions and possible alternatives are discussed. The implementation is used on simulated data and in experi...
متن کاملExtended target tracking using PHD filters
The world in which we live is becoming more and more automated, exemplified by the numerous robots, or autonomous vehicles, that operate in air, on land, or in water. These robots perform a wide array of different tasks, ranging from the dangerous, such as underground mining, to the boring, such as vacuum cleaning. In common for all different robots is that they must possess a certain degree of...
متن کاملMulti-target pitch tracking of vibrato sources in noise using the GM-PHD filter
Probabilistic approaches to tracking often use single-source Bayesian models; applying these to multi-source tasks is problematic. We apply a principled multi-object tracking implementation, the Gaussian mixture probability hypothesis density filter, to track multiple sources having fixed pitch plus vibrato. We demonstrate high-quality filtering in a synthetic experiment, and find improved trac...
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: PLoS ONE
سال: 2014
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0114317