Comparisons of PHD Filter and CPHD Filter for Space Object Tracking
, 2014 The Probability Hypothesis Density (PHD) filter and the Cardinalized PHD (CPHD) filter are two computationally tractable approximate Bayesian multiobject filters within the Finite Set Statistics framework. The PHD filter estimates the intensity function;
Früh, Carolin +2 more
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The Labeled Square Root Cubature Information GM-PHD Approach for Multi Extended Targets Tracking. [PDF]
Sensors (Basel)Liu Z, Zhang S, Yang Z, Qu X, An J.
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Novel Multi-Target Tracking Method: PMBM Filter Combined SVD-SCKF with GP-Driven Measurements. [PDF]
Sensors (Basel)Jia W, Li B, Zhang J, Zhou Y.
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Multi-Target Tracking with Collaborative Roadside Units Under Foggy Conditions. [PDF]
Sensors (Basel)Shi T +6 more
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Robust Smoothing Cardinalized Probability Hypothesis Density Filter-Based Underwater Multi-Target Direction-of-Arrival Tracking with Uncertain Measurement Noise. [PDF]
Entropy (Basel)Gu X, Hou X, Zhang B, Yang Y, Du S.
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Secure Fusion with Labeled Multi-Bernoulli Filter for Multisensor Multitarget Tracking Against False Data Injection Attacks. [PDF]
Sensors (Basel)Yu Y, Liang Y.
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Distributed multi-robot active gathering for non-uniform agriculture and forestry information. [PDF]
Front Plant SciChen J +5 more
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Gaussian Mixture PHD Filter for Space Object Tracking
, 2013 A Gaussian mixture Probability Hypothesis Density (PHD) filter for multiple space object tracking is presented. The PHD filter is a computationally tractable approximate Bayesian multi-object filter based on finite set statistics.
Jah, Moriba K. +3 more
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