Application of Sigma Point Particle Filter Method for Passive State Estimation in Underwater

Bearings-only tracking (BOT) plays a vital role in underwater surveillance. In BOT, measurement is tangentially related to state of the system. This also corrupted with noise due turbulent environment. Hence estimation process using BOT becomes nonlinear. necessitates use nonlinear filtering algorithms place traditional linear filters like Kalman filter. general, these utilize assumption measurements being Gaussian for estimation. The cannot be always because highly unstable sea These problems indicate necessity development non-Gaussian particle filter (PF) tracking. However, PF suffers from severe sample degeneracy and impoverishment it tedious select an appropriate technique resampling. To overcome difficulties implementation, strategy combining another unscented proposed target’s detailed analysis same presented comparison other combinations simulation results obtained Matlab.