Jinwhan Kim

Comparison Between Nonlinear Filtering Techniques for Spiraling Ballistic Missile State Estimation

During the reentry to the atmosphere, certain ballistic missiles are known to undergo violent spiraling motions induced by aerodynamic resonance between roll and yaw/pitch modes. Successful interception of such spiraling targets is critically dependent on the performance of the target state estimator. Strong nonlinearities involved in the system dynamics and measurement equations together with sensor noise make this a challenging estimation task. The performance of an extended Kalman filter (EKF), an unscented Kalman filter (UKF), and a particle filter (PF) designed for this estimation problem is compared in this paper. Additionally, a hybrid Rao-Blackwellized PF (RBPF) approach combining the EKF and the PF is also considered. Simulation results are provided to support the conclusions from the present study.

Particle Filter for Ballistic Target Tracking with Glint Noise

The performance of ballistic target interception is critically dependent on the performance of the target state estimation. The estimation performance then strongly depends on the accuracy of the measurement model. The Gaussian uncertainty distribution has commonly been used for representing the statistical properties of sensor noise, due to its mathematical simplicity and effectiveness. However, seeker sensor measurements are often corrupted by glint noise which is highly non-Gaussian, and conventional Gaussian filtering algorithms are known to show unsatisfactory performance in the presence of glint noise. This research proposes the use of a particle filter for ballistic target tracking in a glint noise environment. The target tracking performance of the particle filter is compared with that of the extended Kalman filter.

Integral sliding mode controller for precise manoeuvring of autonomous underwater vehicle in the presence of unknown environmental disturbances

We propose an integral sliding mode controller (ISMC) to stabilse an autonomous underwater vehicle (AUV) which is subject to modelling errors and often suffers from unknown environmental disturbances. The ISMC is effective in compensating for the uncertainties in the hydrodynamic and hydrostatic parameters of the vehicle and rejecting the unpredictable disturbance effects due to ocean waves, tides and currents. The ISMC is comprised of an equivalent controller and a switching controller to suppress the parameter uncertainties and external disturbances, and its closed-loop system is exponentially stable. Numerical simulations were performed to validate the proposed control approach, and experimental tests using Cyclops AUV were carried out to demonstrate its practical feasibility.

Comparison between Three Spiraling Ballistic Missile State Estimators

During the reentry to the atmosphere, certain ballistic missiles are known to undergo violent spiraling motions induced by aerodynamic resonance between roll and yaw/pitch modes. Successful interception of such a spiraling target is critically dependent on the performance of the target state estimator. Strong nonlinearities involved in the system dynamics and measurement equations together with sensor noise make this a challenging estimation task. The performance of the Extended Kalman Filter, the Unscented Kalman Filter, and the Particle Filter designed for this estimation problem is compared in this paper. Additionally, a hybrid Rao-Blackwellized Particle Filter approach combining the Extended Kalman Filter and the Particle Filter is also considered. Simulation results are provided to support the conclusions from the present study. Nomenclature T X = North position of the target T Y = East position of the target T Z = Down position of the target m X = North position of the missile m Y = East position of the missile m Z = Down position of the missile z , y , x G = Gravitational acceleration components of the target z , y , x A = Aerodynamic acceleration components of the target ω = Spiraling frequency of the target ϕ = Aerodynamic roll angle of the target 1

Efficient image mosaicing for multi-robot visual underwater mapping

Robotic platforms have advanced greatly in terms of their remote sensing capabilities, including obtaining optical information using cameras. Alongside these advances, visual mapping has become a very active research area, which facilitates the mapping of areas inaccessible to humans. This requires the efficient processing of data to increase the final mosaic quality and computational efficiency. In this paper, we propose an efficient image mosaicing algorithm for large area visual mapping in underwater environments using multiple underwater robots. Our method identifies overlapping image pairs in the trajectories carried out by the different robots during the topology estimation process, being this a cornerstone for efficiently mapping large areas of the seafloor. We present comparative results based on challenging real underwater datasets, which simulated multi-robot mapping.

Rapid Estimation of Impaired Aircraft Aerodynamic Parameters

An estimation of aerodynamic models of impaired aircraft using an innovative differential vortex-lattice method tightly coupled with extended Kalman filters is discussed. The proposed approach significantly reduces the order of the estimation problem by exploiting the prior knowledge about the undamaged aircraft and the detected information on the approximate location and extent of damage. Three different extended Kalman filter formulations are developed and their comparative analyses are performed through numerical simulations. Algorithms given in this paper can be used as the basis for online derivation of an aircraft performance model, which can then form the basis for designing safe landing guidance laws for damaged aircraft.

Stochastic Feedback Controller Design Considering the Dual Effect

A new approach for the problem of nonlinear stochastic control is presented. A typical procedure for stochastic control takes a two step approach by separating estimation and control - estimate the system state using noise corrupted measurements, and control the system based on the estimated state. However, for nonlinear stochastic control systems and adaptive control systems with uncertain parameters, control and estimation are often not separable, since control inputs can afiect not only the system state, but also the quality of state estimation. Dynamic programming and search-based methods are general solution techniques for solving this type control problem, however, those solution techniques are often infeasible for real-time applications due to the huge computational requirements even for small problems. In this paper, a new suboptimal control algorithm is presented, which can be implemented as an online feedback controller for a stochastic control problem of moderate dimension. For an application example, automated docking of a nonholonomic vehicle using a bearing-only sensor is considered. The results from a series of numerical simulations are shown.

Motion Models for use with the Maneuvering Ballistic Missile Tracking Estimators

Ballistic target interception requires high accuracy in order to achieve kinetic hit-to-kill performance. Since the task becomes more challenging when the target performs complex maneuvers, highly accurate state estimators are indispensable for successful ballistic target interception. This research focuses on developing filter motion models which can reproduce a wide variety of target motions which include intelligent evasive maneuvers. The proposed filtering technique is expected to improve tracking performance for a maneuvering target undergoing unpredictable motions, which will eventually reduce the miss distance of the ballistic target interception. The feasibility of the developed motion models is demonstrated through numerical tracking simulations, and the performance is compared with that of conventional filter motion models.

Precision navigation and mapping under bridges with an unmanned surface vehicle

Navigation relative to the surrounding physical structures and obstacles is an important capability for safe vehicle operation. This capability is particularly useful for unmanned surface vehicles (USVs) operating near large structures such as bridges, waterside buildings, towers and cranes, where global positioning system signals are restricted or unavailable due to the line-of-sight restrictions. This study proposes a high-precision navigation technique using dead-reckoning sensors and lidars, which enables building a parameterized map of artificial bridge structures and estimating the vehicle’s position relative to the parameterized map simultaneously. Also, three-dimensional reconstruction of the surrounding structures is carried out by fusing camera and lidar measurements for realistic 3D visual mapping which may facilitate automated surveys and inspection of structural safety. Field experiments using a newly developed USV system in a real-world bridge environment were performed to verify and demonstrate the performance of the proposed navigation and mapping algorithms. The field test results are presented and discussed in this paper.

Task space-based control of an underwater robotic system for position keeping in ocean currents

For an underwater vehicle-manipulator system, which consists of an underwater vehicle equipped with a manipulator, it is important to regulate the position of the manipulator’s end-effector with respect to a given target position in many interactive operations. This paper presents a task space-based approach for designing a controller that ensures that the end-effector of an underwater vehicle-manipulator system maintains its position in the presence of unknown ocean currents and uncertainties without the explicit use of a disturbance observer. A feedback linearizing control in task coordinates is used, and an extended Kalman filter (EKF) is employed as a state observer. The proposed approach can also be applied to dynamic positioning or controlled weathervaning of a surface ship whose motion is affected by environmental disturbances. To demonstrate the validity and effectiveness of the proposed approach, numerical simulations and experimental tests were carried out and their results are shown. Graphical Abstract