Development of a ground truth simulator and application of a generalized multiple-model adaptive estimation approach to tune a state estimation filter

In this thesis, a maritime scenario simulator is developed and a data processing/filtering algorithm is applied to estimate the ground truth of the simulated scenario from noisy measurements and system model for the Hierarchical High Level Information Fusion Technologies (HLIFT ) project. HLIFT is an adaptable information fusion framework which takes as input Levels 0/1 (local) data and performs fusion at Levels two and three (distributed, and network centric) hierarchically, in different stages, to provide realtime situational/impact assessment efficiently while avoiding the overload of information to the human decision maker. First, a simulator is developed that imitates a naval threat from an incoming vessel (such as a cargo ship containing a weapon of mass destruction), included in a group of non-threatening vessels. The developed simulations are used as evaluation metrics and performance platforms providing an operational utility assessment tool for the HLIFT algortithm. Next, a Generalized Multiple-Model Adaptive Estimation (GMMAE) technique is used to estimate the unknown parameters invloved with a Probability Data Association Filter (PDAF) which includes a Kalman Filter (KF). The properly tuned state estimator is used to provide estimates of the ground truth data from the noisy sensor measurements and incomplete system model. These estimates are used as inputs to the HLIFT algorithm and can be tested against the known ground truth to gauge filter performance. A demonstration of the process is provided in the simulation section. Introduction The Hierarchical High Level Information Fusion Technologies (HLIFT ) project addresses the progression of information from Level 2 to Level 3 fusion obtaining an advanced multi-intelligent system for hierarchical high-level decision-making processes (fusion levels are defined in reference [9]). Specifically, the system focuses on processing and interpreting sensor data in the maritime domain. As technology advances and the number of sensors in all platforms increases, the human decision makers are being overwhelmed with data. This leads to slowed response times as well as misguided decisions and overlooked warnings. HLIFT proposes an original means of filtering superfluous information between the three stages of high level fusion (local, distributed, and network centric) so the amount of data distributed is minimized but remains complete and comprehensive. The process allows human decision makers to provide quick, well-informed decisions for prompt action against threats to the United States. The structure of the overall HLIFT project is shown in Figure 1: The simulation stage provides “ground truth” kinematics and contextual information not available to the human decision makers. The data processing and filtering stage adds noise to the ground truth data to represent the “sensed truth” and is analogous to the data provided by existing maritime sensors. The noisy measurement data is then filtered using state estimation techniques to provide the “estimated truth. ” The HLIFT algorithm processes the estimated truth data to provide an estimate of the possible threats involved with the scenario. In this work the state estimation techniques for filtering the sensor data are Kalman Filtering (EKF) [4], Generalized Multiple-Model Adaptive Estimation (GMMAE)[3], and Probability Data Association Filtering (PDAF)[14]. The EKF is derived for integrating noisy sensor measurements with imperfect system models to generate state estimates. The EKF position and attitude estimate accuracies are based on the filter tuning which requires knowledge of the statistical nature of both the measurement noise and modeling errors. In practice the former is known approximately i Figure 1: Project Hierarchy from a priori testing on the sensors being used, however the latter is difficult to characterize leading to the classical tuning of the filter problem. The PDAF is utilized when the target being tracked enters a clutter situation (surrounded by other vessels that could be picked up by the sensors). In this case, it is possible that the source of a measurement is not the target being tracked. The PDAF assigns a probability that the source of the