A proposal of the Signal Processing Group has been selected by the Office of Naval Research (ONR)
|The proposal “A new sequential Monte Carlo framework for tracking of nonlinear complex dynamical systems” of the GTS Group has been selected by the Office of Naval Research (ONR). This proposal was submitted in response to Broad Agency Announcement 15-001 of long-range Office of Naval Research scientific projects that was published on 29 September 2014.|
Many problems related to environmental sensing, situation awareness and information fusion boil down to the ability of efficiently tracking complex nonlinear, high-dimensional stochastic dynamical systems. Examples abound, ranging from classical multi-target tracking in battlefield scenarios to weather/environmental forecasting for tactical planning. The algorithms for prediction and tracking of random dynamical systems are collectively termed stochastic filters. Most of these techniques seek numerical approximations, since closed form solutions do not exist for general nonlinear systems. This is the case of particle filters (PFs), which are recursive (online) methods based on statistical Monte Carlo principles. While PFs can be applied to any dynamical system, they are often criticized as computationally heavy and inefficient in high-dimensional models, precisely because of their reliance on Monte Carlo integration. However, although a number of deterministic methods have been recently proposed (e.g., cubature, optimal transportation or deterministic flow filters) as potential replacements of PFs, none of them has e↵ectively overcome the dimensionality/complexity problem yet. In this proposal, we advocate the development of a new particle filtering framework (including an extended methodological setting and the theoretical tools for its analysis) that still has sequential Monte Carlo integration at its core but is endowed with a number of features that address directly the key issues of dimensionality and complexity. Such features include the partitioning of high-dimensional state spaces (a divide and conquer approach), the prevention of the degeneracy phenomenon in importance samplers and the ‘automatic stabilization’ of the tracker.We aim at developing both the methodological and the theoretical aspects of the new framework, and to apply the resulting algorithms to selected problems related to the tracking of multiple and/or complex targets.The design of new and efficient nonlinear trackers for multiple and/or complex targets is relevant to several focus areas of the US Naval Science & Technology Strategic Plan, including, at least, Assure Access to the Maritime Battlespace (focus area #1), Autonomy and Unmanned Systems (f. a. #2) and Expeditionary and Irregular Warfare (f. a. #3). We will specifically address the application of the new methodology to two problems: the joint tracking of a large number of targets and the forecasting of complex meteorological phenomena for tactical planning.
This research will be partially carried out in collaboration with Prof. Petar M. Djuric, from the Department of Electrical and Computer Engineering of Stony Brook University (NY).
PI and CO-PI: Joaquín Míguez and Mónica F. Bugallo.
The Department of the Navy’s Office of Naval Research provides the science and technology necessary to maintain the Navy and Marine Corps’ technological advantage. Through its affiliates, ONR is a leader in science and technology with engagement in 50 states, 55 countries, 634 institutions of higher learning and non-profit institutions over 960 industry partners. ONR through its commands including headquarters, ONR Global and the Naval Research Lab in Washington, D.C., employs more than 3,800 people, comprising uniformed, civilian and contract personnel.