Experimental validation of GPS-INS-STAR hybrid navigation system for space autonomy☆

The paper deals with the development of the multirate information fusion algorithm for the integrated navigation system. The system is designed for air-launch space launcher used within the conversion programs for non-military space exploration. The integrated navigation system includes a strapdown inertial navigation system (strapdown INS, SINS), CCDbased strapdown astronavigation system (ANS), multi-antenna combined GPS/GLONASS receiver i.e. SINS/ANS/GNSS is under consideration. Loosely coupled SINS/GNSS architecture is used. The innovation-based adaptive Kalman filter (KF) fuses navigation solutions feeded by the above systems to produce a more accurate and reliable navigation solution in the presence of non-stationary Gaussian noise. SINS/ANS/GNSS errors are the states of linear time-varying (LTV) state-space system. KF estimates the system state vector. First-order Gauss-Markov processes are used to model errors time-correlation. Correlation matrices of error shaping filters are adjusted according to body dynamics and environment. KF measurements are constructed as SINS, ANS, and GNSS navigation solution differences. Lagrange polynomials are used to interpolate data from each system. SINS/ANS/GNSS performance is compared to that of loosely coupled SINS/GNSS in numerical simulations. INS/ANS/GNSS implementations are reviewed.

Adaptive SINS/ANS/GNSS for air-launch space launcher: Algorithm design and performance analysis

The main goal of inverse problems is the reconstruction of unknown quantities, often they are located in an inaccessible part of space. Data assimilation, which is the integration of inverse problems techniques with dynamical systems with the task of forecasting the evolution of some quantity, has evolved strongly, with important applications in meteorology and atmospherical sciences.
Localization is an essential part of ensemble based assimilation schemes. The size of an ensemble is always much smaller than the dimension of the state space for real numerical predictions. It is necessary to ensure a sufficent number of degree's of freedom when generating the analysis ensemble and, thus, increase the rank of the set of analysis equations. Also, the small ensemble size gives an insufficent estimate of the background error correlations. Localization effectivly eliminates spurious correlations in the background ensemble between distant state variables.
The choice of the localization radius needs to depend on the number of ensemble members for the short-range forcasts used to calculate the background error for the analysis step as well as on the number of observations and the observation error. However, a challenge arises when the observation operator under consideration is non-local (e.g. satellite radiance data), the localization which is applicable to the problem can be severly limited, with strong effects on the quality of the assimilation step.
We study a transformation approach to change non-local operators to local operators in transformed space, such that localization becomes applicable. We interpret this approach as a generalized localization and study its general algebraic formulation. Examples are provided for a compact integral operator and a non-local matrix observation operator to demonstrate the feasibility of the approach and study the quality of the assimilation by transformation.
In particular, we apply the approach to temperature profile reconstruction from infrared measurements given by the IASI Infrared Sounder and show that the approach is feasible for this important data type in atmospheric analysis and forecasting. We also believe that our derivations will work in a similar way for particle filters.
If we do localization we have to adjust the localization radius to the scale of the problem. In this respect choosing large radius mean the data we have is important for large scale and vice versa. Thus localization is linked to the scale of the meteorological processes under consideration. Inherently when we choose localization, we chose scale. This leads to the topic of scales, which usually comes under the umbrella of multiscale methods.
The broad idea of a multiscale approach is to decompose your problem into different scales or levels and to use these decompositions either for constructing appropriate approximations or to solve smaller problems on each of these levels, leading to increased stability or increased efficiency. Our goal is to analyse the sequential multiscale approach applied to an inversion or state estimation problem. We work in a generic setup given by a Hilbert space environment. We work out the analysis both for an unregularized and a regularized sequential multiscale inversion. In general the sequential multiscale approach is not equivalent to a full solution, but we show that under appropriate assumptions we obtain convergence of an iterative sequential multiscale version of the method. For the regularized case we develop a strategy to appropriately adapt the regularization when an iterative approach is taken.
We demonstrate the validity of the iterative sequential multiscale approach by testing the method on an integral equation as it appears for atmospheric temperature retrieval from infrared satellite radiances.

On Localization and Multiscale in Data Assimilation

New Concept for Autonomous Rendezvous Approach Navigation and Guidance System using Only Target Image Information

The theoretical and experimental validation of the gps-ins-star hybrid navigation system concept

New Integration Scheme of GPS-INS Hybrid Navigation System for Maneuvering Spacecraft

Square root filter is formulated for the problem of ephemeris and attitude determination of earth orbital satellites using a scan vector from satellite to GCPs (Ground Control Points) obtained from remote sensing image data. Covariance analysis is also performed for various GCP location and high performance and stability of the square root filter is confirmed in satellite autonomous navigation.

H. Koyama

Papers

New Integration Scheme of GPS-INS Hybrid Navigation System for Maneuvering Spacecraft

Square root filtering formulation for satellite autonomous navigation

Square Root Filtering Formulation for Satellite Autonomous Navigation

Autonomous GPS-INS-Star satellite navigation system concept