Data Assimilation by Conditioning on Future Observations

Conventional recursive filtering approaches, designed for quantifying the state of an evolving uncertain dynamical system with intermittent observations, use a sequence of (i) an uncertainty propagation step followed by (ii) a step where the associated data is assimilated using Bayes' rule. In this paper we switch the order of the steps to: (i) one step ahead data assimilation followed by (ii) uncertainty propagation. This route leads to a class of filtering algorithms named \emph{smoothing filters}. For a system driven by random noise, our proposed methods require the probability distribution of the driving noise after the assimilation to be biased by a nonzero mean. The system noise, conditioned on future observations, in turn pushes forward the filtering solution in time closer to the true state and indeed helps to find a more accurate approximate solution for the state estimation problem

Vehicle infrastructure cooperative localization using Factor Graphs

Highly assisted and Autonomous Driving is dependent on the accurate localization of both the vehicle and other targets within the environment. With increasing traffic on roads and wider proliferation of low cost sensors, a vehicle-infrastructure cooperative localization scenario can provide improved performance over traditional mono-platform localization. The paper highlights the various challenges in the process and proposes a solution based on Factor Graphs which utilizes the concept of topology of vehicles. A Factor Graph represents probabilistic graphical model as a bipartite graph. It is used to add the inter-vehicle distance as constraints while localizing the vehicle. The proposed solution is easily scalable for many vehicles without increasing the execution complexity. Finally simulation indicates that incorporating the topology information as a state estimate can improve performance over the traditional Kalman Filter approac

Helical automatic approaches of helicopters with microwave landing systems

A program is under way to develop a data base for establishing navigation and guidance concepts for all-weather operation of rotorcraft. One of the objectives is to examine the feasibility of conducting simultaneous rotorcraft and conventional fixed-wing, noninterfering, landing operations in instrument meteorological conditions at airports equipped with microwave landing systems (MLSs) for fixed-wing traffic. An initial test program to investigate the feasibility of conducting automatic helical approaches was completed, using the MLS at Crows Landing near Ames. These tests were flown on board a UH-1H helicopter equipped with a digital automatic landing system. A total of 48 automatic approaches and landings were flown along a two-turn helical descent, tangent to the centerline of the MLS-equipped runway to determine helical light performance and to provide a data base for comparison with future flights for which the helical approach path will be located near the edge of the MLS coverage. In addition, 13 straight-in approaches were conducted. The performance with varying levels of state-estimation system sophistication was evaluated as part of the flight tests. The results indicate that helical approaches to MLS-equipped runways are feasible for rotorcraft and that the best position accuracy was obtained using the Kalman-filter state-estimation with inertial navigation systems sensors