Surveillance of underwater for maritime warfare is traditionally being carried out by bearingsonly tracking from many decades.The measurements used for state estimation here are nonlinear.Also the noise in the measure...Surveillance of underwater for maritime warfare is traditionally being carried out by bearingsonly tracking from many decades.The measurements used for state estimation here are nonlinear.Also the noise in the measurements and the process cannot be always Gaussian.The traditional nonlinear filtering algorithms like extended Kalman filter and modified gain extended Kalman filter use the linearisation of the system.The unscented Kalman filter(UKF)uses the sigma point approach based on Gaussian distribution to deal with nonlinearity.The particle filter(PF)uses the randomly generated particles based on the pdf of the state.PF is highly complex to implement and it also suffers from sample impoverishment.Hence,ensemble Kalman filter(EnKF)which is a simplified form of PF will be tried out for bearings-only tracking in this research work.The performance of EnKF is compared with PF and UKF and the results obtained using these filters in Matlab are presented.展开更多
Purpose-From many decades,bearings-only tracking(BOT)is the interested problem for researchers.This utilises nonlinear filtering methods for state estimation as there is only information about the target,i.e.bearing i...Purpose-From many decades,bearings-only tracking(BOT)is the interested problem for researchers.This utilises nonlinear filtering methods for state estimation as there is only information about the target,i.e.bearing is a nonlinear measurement.The measurement bearing is tangentially related to the target state vector.There are many nonlinear filtering algorithms developed so far in the literature.Design/methodology/approach-In this research work,the recently developed nonlinear filtering algorithm,i.e.shifted Rayleigh filter(SRF),is applied to BOT.Findings-The SRF is tested for two-dimensional BOT against various scenarios.The simulation results emphasise that the SRF performs well when compared to the standard nonlinear filtering algorithm,unscented Kalman filter(UKF).Originality/value-SRF utilises the nonlinearities present in the bearing measurement through the use of moment matching.The SRF is able to produce the solution in highly noisy environment,long ranges and high dimension tracking.展开更多
文摘Surveillance of underwater for maritime warfare is traditionally being carried out by bearingsonly tracking from many decades.The measurements used for state estimation here are nonlinear.Also the noise in the measurements and the process cannot be always Gaussian.The traditional nonlinear filtering algorithms like extended Kalman filter and modified gain extended Kalman filter use the linearisation of the system.The unscented Kalman filter(UKF)uses the sigma point approach based on Gaussian distribution to deal with nonlinearity.The particle filter(PF)uses the randomly generated particles based on the pdf of the state.PF is highly complex to implement and it also suffers from sample impoverishment.Hence,ensemble Kalman filter(EnKF)which is a simplified form of PF will be tried out for bearings-only tracking in this research work.The performance of EnKF is compared with PF and UKF and the results obtained using these filters in Matlab are presented.
文摘Purpose-From many decades,bearings-only tracking(BOT)is the interested problem for researchers.This utilises nonlinear filtering methods for state estimation as there is only information about the target,i.e.bearing is a nonlinear measurement.The measurement bearing is tangentially related to the target state vector.There are many nonlinear filtering algorithms developed so far in the literature.Design/methodology/approach-In this research work,the recently developed nonlinear filtering algorithm,i.e.shifted Rayleigh filter(SRF),is applied to BOT.Findings-The SRF is tested for two-dimensional BOT against various scenarios.The simulation results emphasise that the SRF performs well when compared to the standard nonlinear filtering algorithm,unscented Kalman filter(UKF).Originality/value-SRF utilises the nonlinearities present in the bearing measurement through the use of moment matching.The SRF is able to produce the solution in highly noisy environment,long ranges and high dimension tracking.
