Robust Smoothing Cardinalized Probability Hypothesis Density Filter-Based Underwater Multi-Target Direction-of-Arrival Tracking with Uncertain Measurement Noise [PDF]
EntropyIn view of the typical multi-target scenarios of underwater direction-of-arrival (DOA) tracking complicated by uncertain measurement noise in unknown underwater environments, a robust underwater multi-target DOA tracking method is proposed by ...
Xinyu Gu +4 more
doaj +2 more sources
The Gaussian Mixture Probability Hypothesis Density Filter
IEEE Transactions on Signal Processing, 2006 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wing-Kin Ma
exaly +2 more sources
Trajectory probability hypothesis density filter [PDF]
2018 21st International Conference on Information Fusion (FUSION), 2018 Published in the Proceedings of the 21st International Conference on Information Fusion (FUSION)
Ángel F. García-Fernández +1 more
openaire +4 more sources
Research on Multi-Target Motion Estimation Method Based on Generalized Probability Hypothesis Density [PDF]
Hangkong bingqi, 2021 Aiming at the development needs of air-to-air operations, this paper proposes a multi-target motion estimation method based on generalized probability hypothesis density. Multi-scale analysis is introduced into based on the Faster-RCNN algorithm, and the
Yu Meng, Xu Yanke, Hu Jiaqian
doaj +1 more source
Box-particle probability hypothesis density filtering [PDF]
IEEE Transactions on Aerospace and Electronic Systems, 2014 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 of dealing with three sources of uncertainty: stochastic, set-theoretic, and data association ...
Marek Schikora +4 more
openaire +5 more sources
Arbitrary clutter extended target probability hypothesis density filter
IET Radar, Sonar & Navigation, 2021 Based on the random finite set (RFS) framework and the probability hypothesis density (PHD) filter, the extended target PHD (ET‐PHD) filter is proposed for multiple extended target tracking.
Xinglin Shen +4 more
doaj +1 more source
A Sector-Matching Probability Hypothesis Density Filter for Radar Multiple Target Tracking
Applied Sciences, 2023 The development of high-tech, dim, small targets, such as drones and cruise missiles, brings great challenges to radar multi-target tracking (MTT), making it necessary to extend the beam dwell time to obtain a high signal-to-noise ratio (SNR).
Jialin Yang +6 more
doaj +1 more source
Computation-distributed probability hypothesis density filter [PDF]
EURASIP Journal on Advances in Signal Processing, 2016 Particle probability hypothesis density filtering has become a promising approach for multi-target tracking due to its capability of handling an unknown and time-varying number of targets in a nonlinear, non-Gaussian system. However, its computational complexity linearly increases with the number of obtained observations and the number of particles ...
Junjie Wang 0005 +4 more
openaire +1 more source
Adaptive grid‐driven probability hypothesis density filter for multi‐target tracking
IET Signal Processing, 2021 The probability hypothesis density (PHD) filter and its cardinalised version PHD (CPHD) have been demonstratedasa class of promising algorithms for multi‐target tracking (MTT) with unknown,time‐varying number of targets.
Jinlong Yang, Jiuliu Tao, Yuan Zhang
doaj +1 more source
Unscented Auxiliary Particle Filter Implementation of the Cardinalized Probability Hypothesis Density Filters [PDF]
AUT Journal of Electrical Engineering, 2017 The probability hypothesis density (PHD) filter suffers from lack of precise estimation of the expected number of targets. The Cardinalized PHD (CPHD) recursion, as a generalization of the PHD recursion, remedies this flaw and simultaneously propagates ...
M. R. Danaee, F. Behnia
doaj +1 more source