Terrain Relative Navigation for Guided Descent on Titan

Titan's dense atmosphere, low gravity, and high winds at high altitudes create descent times of >90 minutes with standard entry/descent/landing (EDL) architectures and result in large unguided landing ellipses, with 99% values of ~110×110 km and 149×72 km in recent Titan lander proposals. Enabling precision landing on Titan could increase science return for the types of missions proposed to date and make additional types of landing sites accessible, opening up new possibilities for science investigations. Precision landing on Titan has unique challenges, because the hazy atmosphere makes it difficult to see the surface and because it requires guided descent with divert ranges that are one to two orders of magnitude larger than needed for other target bodies, i.e. up to on the order of 100 km. It is conceivable that such a divert capability could be provided economically by a parafoil or other steerable aerodynamic decelerator deployed several 10s of km above the surface. The long descent times lead to large inertial navigation errors, hence a need for terrain relative navigation (TRN). This would require a TRN capability that can operate at such altitudes, despite challenges of seeing the surface sufficiently clearly and of depending on map products that are two orders of magnitude lower in spatial resolution than those for Mars and airless bodies. This paper addressed the TRN problem for Titan guided descent, assuming parafoil deployment at an altitude around 40 km. We define a notional sensor suite including an inertial measurement unit (IMU), a radar altimeter, and two descent cameras, with spectral responses in the visible/near infrared (VNIR) (~0.5 to 1 um) and short wave infrared (SWIR) (~2.0 to 2.1 um), Due to the low resolution of current Titan map products, we define two altitude regimes (above and below ~20 km) that need different navigation techniques. Map matching is applicable in the upper regime, but challenging or infeasible in the lower one. Feature tracking with decent imagery is desirable in the lower regime, but challenging in the upper one. We derive image contrast requirements for TRN from prior literature and create models of achievable image contrast by radiative transfer modeling; this shows that the requirements should be achievable for a SWIR descent camera in the upper regime, and that a VNIR descent camera is preferable in the lower regime. We then develop algorithms for map matching and feature tracking with descent images and test these with synthetic images created from Cassini/Huygens data sets and our radiative transfer model. We also introduce new possibilities for TRN based on the potential to discriminate some specific types of terrain onboard in descent imagery, such as lake vs adjacent ground and dune vs interdune. We use sensor measurement noise models in simulations of state estimation with an extended Kalman filter that includes coordinates of a set of tracked features in the state vector. Case studies were done for two notional landing sites, one in a site with only dry ground and one in a Titan lake district. In both cases, the filter error model shows 3σ position error at touchdown on the order of 2 km. More work is needed to validate these results with higher fidelity camera models and larger data sets, but this is very promising.