State Estimation
In this section, we discuss the functionality that is required for state estimation in Tudat. Before starting this section, make sure to go through our page on Propagation Setup, since the state estimation function requires the full functionality from state propagation. At the moment, the estimation functitonality of Tudat is limited to the use of batch least-squares. A broad range of parameters (initial translational and rotational state; single-, multi- and hybrid-arc states; numerous physical properties of the environment) from a diverse set of available observations is supported.
Estimation Inputs
In addition to the inputs required for state propagation, the following needs to be set up to perform an estimation.
Parameter setup: definition of the parameters that are to be estimated, as discussed here in the context of variational equation propagation
Link end setup: define the stations/spacecraft involved in an observation, and define their role for a particular observable (receiver, transmitter, etc.), described here
Observation model setup: define the type and properties of a given (idealized) observation model, such as range, Doppler, etc., here
Simulating observations OR Loading observations: depending on whether you are considering estimation in a simulated environment (in which case you simulate the observables in TudatPy, described here) or using real data (in which case you load them from files), you must create/load observations, and define properties such as times, noise, etc..
Estimation settings: define the a priori knowledge, convergence criteria, etc., described here
Simulation/Analysis & Output
Once all the settings are in place, the solution can be generated: the (simulated) observations can be fit to the dynamical model that has been defined to perform the fitting. Alternatively, the same functionality can be used for a covariance analysis only, in which case no fit is attempted. Details are provided on this page