Heave estimation from gyros and accelerometers. Induced heave. H1.3c Kalman Bayesian estimation Apply Kalman filtering methods to a filtering State representation of a dynamic observation process. dynamic observation equation, observability Define the parameters of a Kalman Filter (I) Continuous, Semi-discrete and in relation with sensors performances and discrete Kalman filtering dynamic model uncertainty. Optimal smoothing Differentiate between stationary and observation processes H1.3d Aided INS and GNSS loosely and Describe the role of aiding sensors to inertial tightly coupled solutions. reduce INS navigation drift. navigation Velocity and ranging aided INS navigation. Apply appropriate settings to filtering and Dynamic and aided alignment smoothing for aided navigation solutions. (I) of INS by Kalman filtering. INS solutions from IMU and other sensors by Kalman filtering and smoothing. H1.4 Subsea positioning H1.4a Acoustic Long base line Describe the signal structure and positioning Short baseline observables of mobile and acoustic principles Ultra-short baseline positioning devices. Doppler velocity log Transponders Relate observables and platform (A) Acoustic modems orientation to relative positions through Subsea INS observation equations. column structure H1.4b Acoustic Explain how acoustic positioning Acoustic ray multipath positioning observables, orientation and surface Time synchronization systems positioning data are used to achieve subsea rover spatial
Heave estimation from gyros and accelerometers. Induced heave. H1.3c Kalman filtering (I) Bayesian estimation State representation of a dynamic observation equation, observability Continuous, Semi-discrete and discrete Kalman filtering Optimal smoothing Apply Kalman filtering methods to a dynamic observation process. Define the parameters of a Kalman Filter in relation with sensors performances and dynamic model uncertainty. Differentiate between stationary and observation processes H1.3d Aided inertial navigation (I) INS and GNSS loosely and tightly coupled solutions. Velocity and ranging aided INS navigation. Dynamic and aided alignment of INS by Kalman filtering. INS solutions from IMU and other sensors by Kalman filtering and smoothing. Describe the role of aiding sensors to reduce INS navigation drift. Apply appropriate settings to filtering and smoothing for aided navigation solutions. H1.4 Subsea positioning H1.4a Acoustic positioning principles (A) Long base line Short baseline Ultra-short baseline Doppler velocity log Transponders Acoustic modems Subsea INS column structure Acoustic ray multipath Time synchronization Describe the signal structure and observables of mobile and acoustic positioning devices. Relate observables and platform orientation to relative positions through observation equations. H1.4b Acoustic positioning systems Explain how acoustic positioning observables, orientation and surface positioning data are used to achieve subsea rover spatial
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