Gaussian Mixture Cardinalized Probability Hypothesis Density(GM-CPHD): A Distributed Filter Based on the Intersection of Parallel Inverse Covariances [PDF]
Sensors, 2023 A distributed GM-CPHD filter based on parallel inverse covariance crossover is designed to attenuate the local filtering and uncertain time-varying noise affecting the accuracy of sensor signals.
Liu Wang, Guifen Chen, Guangjiao Chen
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Passive Tracking of Multiple Underwater Targets in Incomplete Detection and Clutter Environment [PDF]
Entropy, 2021 A major advantage of the use of passive sonar in the tracking multiple underwater targets is that they can be kept covert, which reduces the risk of being attacked.
Xiaohua Li, Bo Lu, Wasiq Ali, Haiyan Jin
doaj +2 more sources
An Efficient Implementation Method for Distributed Fusion in Sensor Networks Based on CPHD Filters [PDF]
Sensors, 2023 A highly efficient implementation method for distributed fusion in sensor networks based on CPHD filters is proposed to address the issues of unknown cross-covariance fusion estimation and long fusion times in multi-sensor distributed fusion. This method
Liu Wang, Guifen Chen
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The CPHD Filter With Target Spawning [PDF]
IEEE Transactions on Signal Processing, 2017 In its classical form, the cardinalized probability hypothesis density (CPHD) filter does not model the appearance of new targets through spawning, yet there are applications for which spawning models more appropriately account for newborn objects when compared to spontaneous birth models.
Daniel S Bryant +2 more
exaly +4 more sources
The GM-JMNS-CPHD Filtering Algorithm for Nonlinear Systems Based on a Generalized Covariance Intersection [PDF]
SensorsSome fusion criteria in multisensor and multitarget motion tracking cannot be directly applied to nonlinear motion models, as the fusion accuracy applied in nonlinear systems is relatively low.
Zhixuan Xu +3 more
doaj +2 more sources
IEEE Transactions on Aerospace and Electronic Systems, 2016 The single sensor probability hypothesis density (PHD) and cardinalized probability hypothesis density (CPHD) filters have been developed in the literature using the random finite set framework. The existing multisensor extensions of these filters have limitations such as sensor order dependence, numerical instability or high computational requirements.
Santosh Nannuru +2 more
exaly +3 more sources
SMC-CPHD Filter with Adaptive Survival Probability for Multiple Frequency Tracking
Applied Sciences, 2022 We propose a sequential Monte Carlo-based cardinalized probability hypothesis density (SMC-CPHD) filter with adaptive survival probability for multiple frequency tracking to enhance the tracking performance.
Sun Young Kim +2 more
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A CPHD Filter for Tracking With Spawning Models [PDF]
IEEE Journal on Selected Topics in Signal Processing, 2013 In some applications of multi-target tracking, appearing targets are suitably modeled as spawning from existing targets. However, in the original formulation of the cardinalized probability hypothesis density (CPHD) filter, this type of model is not supported; instead appearing targets are modeled by spontaneous birth only.
Lennart Svensson, Lars Hammarstrand
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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
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A Study on Distributed Multi-Sensor Fusion for Nonlinear Systems Under Non-Overlapping Fields of View [PDF]
SensorsTo explore how varying viewpoints influence the accuracy of distributed fusion in asynchronous, nonlinear visual-field systems, this study investigates fusion strategies for multi-target tracking. The primary focus is on how different sensor perspectives
Liu Wang +5 more
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