A Variational Bayesian algorithm for Extended Target Tracking with Unknown Measurement Noise

2019 ◽  
Author(s):  
Tianli Ma ◽  
Yan Wang ◽  
Chaobo Chen ◽  
Kai Cao
Keyword(s):  
Target Tracking ◽  
Measurement Noise ◽  
Bayesian Algorithm ◽  
Variational Bayesian ◽  
Extended Target

scholarly journals A Quadratic Interpolation-Based Variational Bayesian Algorithm for Measurement Information Lost in Underwater Navigation

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yuan Yang ◽  
Jiacheng Tang ◽  
Haoqian Huang ◽  
Xiaoguo Zhang ◽  
Tingting Zhang ◽  
...  
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Measurement Noise ◽  
Measurement Information ◽  
Bayesian Algorithm ◽  
Navigation Problem ◽  
Variational Bayesian ◽  
Error Covariance ◽  
Quadratic Interpolation ◽  
Traditional Algorithm ◽  
Underwater Navigation

The main challenges of sequential estimations of underwater navigation applications are the internal/external measurement noise and the missing measurement situations. A quadratic interpolation-based variational Bayesian filter (QIVBF) is proposed to solve the underwater navigation problem of measurement information missing or insufficiency. The quadratic interpolation is used to improve the observed vector for the precision and stability of sequential estimations when the environment is changed or the measurement information is lost. The state vector, the predicted error covariance matrix, and the measurement noise matrix are derived based on the variational Bayesian method. Simulation results demonstrate the superiority of the proposed QIVBF compared with the traditional algorithm under the condition of measurement information lost by autonomous underwater vehicles.

scholarly journals A Robust SMC-PHD Filter for Multi-Target Tracking with Unknown Heavy-Tailed Measurement Noise

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3611
Author(s):  
Yang Gong ◽  
Chen Cui
Keyword(s):  
Target Tracking ◽  
Sequential Monte Carlo ◽  
Measurement Noise ◽  
Particle Simulation ◽  
Target Number ◽  
Student T Distribution ◽  
Multi Target Tracking ◽  
Heavy Tailed ◽  
Scale Matrix ◽  
T Distribution

In multi-target tracking, the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter is a practical algorithm. Influenced by outliers under unknown heavy-tailed measurement noise, the SMC-PHD filter suffers severe performance degradation. In this paper, a robust SMC-PHD (RSMC-PHD) filter is proposed. In the proposed filter, Student-t distribution is introduced to describe the unknown heavy-tailed measurement noise where the degrees of freedom (DOF) and the scale matrix of the Student-t distribution are respectively modeled as a Gamma distribution and an inverse Wishart distribution. Furthermore, the variational Bayesian (VB) technique is employed to infer the unknown DOF and scale matrix parameters while the recursion estimation framework of the RSMC-PHD filter is derived. In addition, considering that the introduced Student- t distribution might lead to an overestimation of the target number, a strategy is applied to modify the updated weight of each particle. Simulation results demonstrate that the proposed filter is effective with unknown heavy-tailed measurement noise.

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