Results 41 to 50 of about 1,121,998 (275)
Distributed Computation Particle PHD filter
CoRR, 2015 Particle probability hypothesis density filtering has become a promising means for multi-target tracking due to its capability of handling an unknown and time-varying number of targets in non-linear non-Gaussian system. However, its computational complexity grows linearly with the number of measurements and particles assigned to each target, and this ...
Junjie Wang 0005 +3 more
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Leida xuebao, 2019 Under the clutter background condition, the existing particle filter pre-detection tracking algorithm based on Probability Hypothesis Density (PHD) filtering is not accurate enough to estimate the number of targets in dense multi-objectives.
PEI Jiazheng +4 more
doaj +1 more source
IET Radar, Sonar & Navigation, 2022 Non‐cooperative bistatic radar refers to the passive bistatic radar using a non‐cooperative radar as the illuminator of opportunity. Limited by the non‐cooperation and bistatic configuration, multi‐target tracking of the non‐cooperative bistatic radar is
Sen Wang, Qinglong Bao, Jiameng Pan
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Joint PHD Filter and Hungarian Assignment Algorithm for Multitarget Tracking in Low Signal-to-Noise Ratio [PDF]
Radioengineering, 2023 Multitarget tracking (MTT) for image processing in low signal-to-noise ratio (SNR) is difficult and computationally expensive because the distinction between the target and the background is small. Among the current MTT algorithms, Random Finite Set (RFS)
S. Xiao +4 more
doaj
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
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Comparison of implementations of Gaussian mixture PHD filters
2010 13th International Conference on Information Fusion, 2010 The Probability Hypothesis Filter, which propagates the first moment, or intensity function, of a point process has become more and more popular to address multi-tracking problems. Under linear-Gaussian assumptions, the intensity function takes the form of a mixture of Gaussian kernels.
Pace, Michele +2 more
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An 'active' passive-filter topology for low power DC/AC inverters [PDF]
, 2011 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents a new output passive filter for voltage source inverter applications which is based on a shunt connected single tuned filter topology ...
Arman, Mahmoud Fayez
core +3 more sources
Particle Probability Hypothesis Density Filter Based on Pairwise Markov Chains
Algorithms, 2019 Most multi-target tracking filters assume that one target and its observation follow a Hidden Markov Chain (HMC) model, but the implicit independence assumption of the HMC model is invalid in many practical applications, and a Pairwise Markov Chain (PMC)
Jiangyi Liu +3 more
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Tumour–host interactions in Drosophila: mechanisms in the tumour micro‐ and macroenvironment
Molecular Oncology, EarlyView.This review examines how tumour–host crosstalk takes place at multiple levels of biological organisation, from local cell competition and immune crosstalk to organism‐wide metabolic and physiological collapse. Here, we integrate findings from Drosophila melanogaster studies that reveal conserved mechanisms through which tumours hijack host systems to ...
José Teles‐Reis, Tor Erik Rusten
wiley +1 more source
Cubature Information SMC-PHD for Multi-Target Tracking
Sensors, 2016 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.
Zhe Liu, Zulin Wang, Mai Xu
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