Extended Target Tracking Using Gaussian Processes
نویسندگان
چکیده
منابع مشابه
Extended 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 کاملExtended Target Tracking using a Gaussian-Mixture PHD filter, Report no. LiTH-ISY-R-3028
This paper presents a Gaussian-mixture implementation of the PHD lter for tracking extended targets. The exact lter 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 experiment...
متن کاملExtended and Unscented Gaussian Processes
We present two new methods for inference in Gaussian process (GP) models with general nonlinear likelihoods. Inference is based on a variational framework where a Gaussian posterior is assumed and the likelihood is linearized about the variational posterior mean using either a Taylor series expansion or statistical linearization. We show that the parameter updates obtained by these algorithms a...
متن کاملMulti-Target Tracking Using an Improved Gaussian Mixture CPHD Filter
The cardinalized probability hypothesis density (CPHD) filter is an alternative approximation to the full multi-target Bayesian filter for tracking multiple targets. However, although the joint propagation of the posterior intensity and cardinality distribution in its recursion allows more reliable estimates of the target number than the PHD filter, the CPHD filter suffers from the spooky effec...
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: IEEE Transactions on Signal Processing
سال: 2015
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2015.2424194