Space-based Maneuver Detection and Characterization using Multiple Model Adaptive Estimation

An increasingly congested space environment requires real-time and dynamic space situational awareness (SSA) on both domestic and foreign space objects in Earth orbits. Current statistical orbit determination (SOD) techniques are able to estimate and track trajectories for cooperative spacecraft. However, a non-cooperative spacecraft performing unknown maneuvers at unknown times can lead to unexpected changes in the underlying dynamics of classical filtering techniques. Adaptive estimation techniques can be utilized to build a bank of recursive estimators with different hypotheses on a system\u27s dynamics. The current study assesses the use of a multiple model adaptive estimation (MMAE) technique for detecting and characterizing noncooperative spacecraft maneuvers using space-based sensors for spacecraft in close proximity. A series of classical and variable state multiple model frameworks are implemented, tested, and analyzed through maneuver detection scenarios using relative spacecraft orbit dynamics. Variable levels of noise, data availability, and target thrust profiles are used to demonstrate and quantify the performance of the MMAE algorithm using Monte Carlo methods. The current research demonstrates that adaptive estimation techniques are able to handle unknown changes in the dynamics while keeping comparable errors with respect to other classical estimation methods

Human Intention Inference using Fusion of Gaze and Motion Information

Enabling robots with the ability to quickly and accurately determine the intention of their human counterparts is a very important problem in Human-Robot Collaboration (HRC). The focus of this work is to provide a framework wherein multiple modalities of information, available to the robot through different sensors, are fused to estimate a human\u27s action intent. In this thesis, two human intention estimation schemes are presented. In both cases, human intention is defined as a motion profile associated with a single goal location. The first scheme presents the first human intention estimator to fuse information from pupil tracking data as well as skeletal tracking data during each iteration of an Interacting Multiple Model (IMM) filter in order to predict the goal location of a reaching motion. In the second, two variable structure IMM (VS-IMM) filters, which track gaze and skeletal motion, respectively, are run in parallel and their associated model probabilities fused. This method is advantageous over the first as it can be easily scaled to include more models and provides greater disparity between the most likely model and the other models. For each VS-IMM filter, a model selection algorithm is proposed which chooses the most likely models in each iteration based on physical constraints of the human body. Experimental results are provided to validate the proposed human intention estimation schemes

Dynamic Switching State Systems for Visual Tracking

This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together

Dynamic Switching State Systems for Visual Tracking

This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together

Orbit Estimation of Non-Cooperative Maneuvering Spacecraft

Due to the ever increasing congestion of the space environment, there is an increased demand for real-time situation awareness of all objects in space. An unknown spacecraft maneuver changes the predicted orbit, complicates tracking, and degrades estimate accuracies. Traditional orbit estimation routines are implemented, tested, and compared to a multiple model format that adaptively handles unknown maneuvers. Multiple Model Adaptive Estimation is implemented in an original way to track a non-cooperative satellite by covariance inflation and filtering-through a maneuver. Parameters for successful instantaneous maneuver reconstruction are analyzed. Variable State Dimension estimation of a continuously maneuvering spacecraft is investigated. A requirements based analysis is performed on short arc orbital solutions. Large covariance propagation of potential maneuvers is explored. Using ground-based radars, several thousand simulations are run to develop new techniques to estimate orbits during and after both instantaneous and continuous maneuvers. The new methods discovered are more accurate by a factor of 700 after only a single pass when compared to non-adaptive methods. The algorithms, tactics, and analysis complement on-going efforts to improve Space Situational Awareness and dynamic modeling

Dynamic Switching State Systems for Visual Tracking

This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together

Using Interacting Multiple Model Filters to Indicate Program Risk

Technology development has increased exponentially. Program managers are pushed to accelerate development. There are many resources available to program managers that enable acceleration, such as: additional resources in the form of funding, people and technology. There are also negative impacts to acceleration, such as: inclusion, inexperience program managers, and communication. This research seeks to identify the limit to which a program or project can be accelerated before the program manager begins to accept an unacceptable amount of pre-determined risk. This research will utilize estimation algorithms used by sensor systems to estimate the current and future state of objects in space. The most common estimation algorithm used is the Kalman filter developed by Kalman (Bar-Shalom, Rong Li, & Kirubarajan, 2001). This research will examine the use of two Kalman filters in for the form of an Interacting Multiple Model (IMM) in order to predict the future state of the program. Traditional multiple model filters use Bayesian technique to adaptively switch between different motion models implemented in the filter structure (USA Patent No. 7030809, 2005). These logic designs rely upon a predefined Markov Switching Matrix (MSM). If the future state approaches a predetermined acceptable level of risk, the MSM will indicate to the program manager that the project has potentially reached a level of unacceptable risk

AN INFORMATION THEORETIC APPROACH TO INTERACTING MULTIPLE MODEL ESTIMATION FOR AUTONOMOUS UNDERWATER VEHICLES

Accurate and robust autonomous underwater navigation (AUV) requires the fundamental task of position estimation in a variety of conditions. Additionally, the U.S. Navy would prefer to have systems that are not dependent on external beacon systems such as global positioning system (GPS), since they are subject to jamming and spoofing and can reduce operational effectiveness. Current methodologies such as Terrain-Aided Navigation (TAN) use exteroceptive imaging sensors for building a local reference position estimate and will not be useful when those sensors are out of range. What is needed are multiple navigation filters where each can be more effective depending on the mission conditions. This thesis investigates how to combine multiple navigation filters to provide a more robust AUV position estimate. The solution presented is to blend two different filtering methodologies utilizing an interacting multiple model (IMM) estimation approach based on an information theoretic framework. The first filter is a model-based Extended Kalman Filter (EKF) that is effective under dead reckoning (DR) conditions. The second is a Particle Filter approach for Active Terrain Aided Navigation (ATAN) that is appropriate when in sensor range. Using data collected at Lake Crescent, Washington, each of the navigation filters are developed with results and then we demonstrate how an IMM information theoretic approach can be used to blend approaches to improve position and orientation estimation.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Context Exploitation in Data Fusion

Complex and dynamic environments constitute a challenge for existing tracking algorithms. For this reason, modern solutions are trying to utilize any available information which could help to constrain, improve or explain the measurements. So called Context Information (CI) is understood as information that surrounds an element of interest, whose knowledge may help understanding the (estimated) situation and also in reacting to that situation. However, context discovery and exploitation are still largely unexplored research topics. Until now, the context has been extensively exploited as a parameter in system and measurement models which led to the development of numerous approaches for the linear or non-linear constrained estimation and target tracking. More specifically, the spatial or static context is the most common source of the ambient information, i.e. features, utilized for recursive enhancement of the state variables either in the prediction or the measurement update of the filters. In the case of multiple model estimators, context can not only be related to the state but also to a certain mode of the filter. Common practice for multiple model scenarios is to represent states and context as a joint distribution of Gaussian mixtures. These approaches are commonly referred as the join tracking and classification. Alternatively, the usefulness of context was also demonstrated in aiding the measurement data association. Process of formulating a hypothesis, which assigns a particular measurement to the track, is traditionally governed by the empirical knowledge of the noise characteristics of sensors and operating environment, i.e. probability of detection, false alarm, clutter noise, which can be further enhanced by conditioning on context. We believe that interactions between the environment and the object could be classified into actions, activities and intents, and formed into structured graphs with contextual links translated into arcs. By learning the environment model we will be able to make prediction on the target\u2019s future actions based on its past observation. Probability of target future action could be utilized in the fusion process to adjust tracker confidence on measurements. By incorporating contextual knowledge of the environment, in the form of a likelihood function, in the filter measurement update step, we have been able to reduce uncertainties of the tracking solution and improve the consistency of the track. The promising results demonstrate that the fusion of CI brings a significant performance improvement in comparison to the regular tracking approaches