Multiple Target Tracking (MTT) of Objects Exhibiting Significant Nonlinearities
Small Business Information
11600 Sunrise Valley Drive, Suite # 210, Reston, VA, 20191
VP, New Technology
VP, New Technology
AbstractABSTRACT: This Small Business Innovation Research Phase II project will develop novel nonlinear filtering and joint decision and estimation methods for robust and persistent tracking of multiple targets with nonlinearities in measurement and dynamic model. The key innovations in this effort include i) powerful measures of nonlinearity and non-Gaussianity, ii) efficient and accurate nonlinear point estimators that outperform widely-used extended Kalman filter and unscented filter, iii) improved methods for mesh adaptation for efficient approximate density estimation based on a numerical solution of Fokker-Planck equation, iv) mathematical models defining a continuum of multi-target data association problems in complexity and problem space, and v) ensemble tracking based on a novel joint density and (point) estimation (JDE) framework. The Phase I effort demonstrated the proof of concept by developing core technologies that form the basis of the proposed algorithms. The Phase II effort will be focused towards refinement, advancement, and integration of these enabling technologies and will include development of new algorithms and measures to handle the issues identified during Phase I, transitioning of technology and prototyping, integration of technologies into an existing ISR system, detailed quantitative and qualitative evaluation of the system and its components, and demonstration of technologies in operationally representative scenarios. BENEFIT: Automatic interpretation of sensory data has been a persistent topic of research in the areas of information theory, sensor fusion, computer vision, pattern recognition, machine learning, and psychology, but is still in a very primitive state. Robust detection and persistent tracking of targets in the scene is a critical first step towards enabling systems capable to interpret activities in the scene and provide timely situational awareness and effective forensic analysis capabilities to the analysts. The technologies proposed here enable persistent tracking of multiple targets with nonlinear dynamic and observation models. The proposed tracking techniques are generic and can be applied to many ISR sensors and applications that are characterized by complex models and uncertainties in the data, for example, urban surveillance from WAMI sensors, GMTI and AMTI tracking, 2D/3D particle tracking, and dynamic analysis of cellular organism in microbiology domain. The proposed technologies can also be used to enable real-time and forensic analysis tasks that find a lot of applications in the domains of geospatial intelligence (GEOINT), persistent surveillance, and video analysis. These benefits include: (a) Timely Situational Awareness: Timely availability of reliable intelligence is critical to support military missions and objectives. Knowledge regarding the trajectories of targets, their interactions with each other and other scene elements is a critical part of intelligence and its automated extraction from surveillance data will help expedite the dissemination of relevant intelligence to the commanders in the field. (b) Reliable Inference: Existing automated video surveillance and monitoring tools allow users to specify security rules in the form of tripwires and exclusion zones on maps or images. Persistent tracking of targets will enable users to receive alerts with high precision thus increasing the reliability of the overall system and user-confidence on the output of the intelligent system. (c) Comprehensive Video Analysis: Robust target tracking in videos is a step closer towards comprehensive video analysis, i.e., automatic extraction of a description of the elements, activities, and events in the scene.
* information listed above is at the time of submission.