Open AccessApplication of Sigma Point Particle Filter Method for Passive State Estimation in Underwater
Author(s) -
G. Naga Divya,
S. Koteswara Rao
Publication year - 2021
Publication title -
defence science journal/defence science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.198
H-Index - 32
eISSN - 0976-464X
pISSN - 0011-748X
DOI - 10.14429/dsj.71.16284
Subject(s) - particle filter , control theory (sociology) , underwater , kalman filter , extended kalman filter , noise (video) , nonlinear filter , nonlinear system , filter (signal processing) , tracking (education) , ensemble kalman filter , computer science , unscented transform , invariant extended kalman filter , engineering , algorithm , filter design , artificial intelligence , physics , computer vision , geography , psychology , pedagogy , control (management) , archaeology , quantum mechanics , image (mathematics)
Bearings-only tracking (BOT) plays a vital role in underwater surveillance. In BOT, measurement is tangentially related to state of the system. This measurement is also corrupted with noise due to turbulent underwater environment. Hence state estimation process using BOT becomes nonlinear. This necessitates the use of nonlinear filtering algorithms in place of traditional linear filters like Kalman filter. In general, these nonlinear filters utilize the assumption of measurements being corrupted with Gaussian noise for state estimation. The measurements cannot be always corrupted with Gaussian noise because of the highly unstable sea environment. These problems indicate the necessity for development of nonlinear non-Gaussian filters like particle filter (PF) for underwater tracking. However, PF suffers from severe problems like sample degeneracy and impoverishment and also it is tedious to select an appropriate technique for resampling. To overcome these difficulties in PF implementation, the strategy of combining PF with another filter like unscented Kalman filter is proposed for target’s state estimation. The detailed analysis of the same is presented in comparison with other particle filter combinations using the simulation results obtained in Matlab.