Optimization-based State Estimation Research Articles

Generic Stability Implication From Full Information Estimation to Moving-Horizon Estimation

Optimization-based state estimation is useful for handling of constrained linear or nonlinear dynamical systems. It has an ideal form, known as full information estimation (FIE) which uses all past measurements to perform state estimation, and also a practical counterpart, known as moving-horizon estimation (MHE) which uses most recent measurements of a limited length to perform the estimation. This work reveals a generic link from robust stability of FIE to that of MHE, showing that the former implies at least a weaker robust stability of MHE which implements a long enough horizon. The implication strengthens to strict robust stability of MHE if the corresponding FIE satisfies a mild Lipschitz continuity condition. The revealed implications are then applied to derive new sufficient conditions for robust stability of MHE, which further reveals an intrinsic relation between the existence of a robustly stable FIE/MHE and the system being incrementally input/output-to-state stable.

LiDAR Inertial Mapping of Non-Manhattan Topologies for UAV-Based Aircraft Inspection in GNSS-Denied Environments

In this paper we propose and evaluate a high resolution intensity yielding adaption of a 3D mapping approach for airplane inspection by autonomous unmanned aerial vehicles. We solely use a 16 column laserscanner and an industrial-grade inertial measurement unit, without a priori knowledge of the inspection object dimensions or its surroundings. Our approach includes the introduction of a novel cylinder feature detection and an according modification of the optimization-based state estimation. Robustness of the point cloud registration is proven based on measurements in a GNSS-denied hangar environment with a Boeing 737-500, comprising only sparse plane and edge features and for highly dynamic movement involving rapid roll or pitch rotation. The approach is suitable for the given application to datasets with strong non-Manhattan characteristics, as opposed to several state of the art algorithms. By means of including the intensity information, 2D projections of the 3D registration result depict a remarkable level of detail and sharpness, resembling that of camera images. Thus allowing for complete coverage of the environment with high spatial resolution using the discrete points and voxel grids with small leaf sizes, the obtained results perspectively can be used for visually matching multi-channel camera images. By fusion of such visual information with the LiDAR generated projections and using the LiDAR inertial odometry, 3D defect maps of the aircraft surface can be constructed, enabling the automated localization of structural damages that are detectable using convolutional neural networks, as shown in previous publications.

An Optimization-Based State Estimation Framework for Large-Scale Natural Gas Networks

We propose an optimization-based state estimation framework to track internal space–time flow and pressure profiles of natural gas networks during dynamic transients. We find that the estimation problem is ill-posed (because of the infinite-dimensional nature of the states) and that this leads to instability of the estimator when short estimation horizons are used. To circumvent this issue, we propose moving horizon strategies that incorporate prior information. In particular, we propose a strategy that initializes the prior using steady-state information and compare its performance against a strategy that does not initialize the prior. We find that both strategies are capable of tracking the state profiles but we also find that superior performance is obtained with steady-state prior initialization. We also find that, under the proposed framework, pressure sensor information at junctions is sufficient to track the state profiles. We also derive approximate transport models and show that some of these can...

Optimization-based state estimation: Current status and some new results

This paper presents an overview of the fundamentals of moving horizon state estimation and full information estimation. The paper provides a new statement of robust global asymptotic stability (RGAS) for state estimation and establishes a new result using this definition, namely that full information is RGAS for the case of a nonlinear detectable system subject to β-convergent state and measurement disturbances. Two unsolved research problems are presented: (i) suboptimal moving horizon estimation and (ii) proving RGAS for the case of bounded rather than convergent disturbances.

A particle swarm optimization-based state estimation scheme for moving objects

The α − β and α − β − γ filters based on the Kalman-like estimation scheme have been recognized as outstanding tools for estimating position, velocity and acceleration signals of moving objects. Nevertheless, the performance of estimation heavily depends on the parameters α, β, and γ. In general, the choice of parameters is a trade-off multi-objective optimization problem between the tracking accuracy and noise suppression capability. In this paper, a weighted particle swarm optimization (PSO) method is proposed to help search globally optimal parameter values in parameter space to meet some specifications. A numerical example is employed to illustrate the developed optimum estimation scheme, which is capable of tracking the desired signals accurately and, at the same time, reducing the noise disturbance remarkably. In order to test practically the effectiveness of the estimation scheme, an experimental apparatus, named twin rotor system, will be used as a test bed to demonstrate its performance. The experimental results strongly suggest that the PSO-based state estimators are encouraging for practical applications.