Traffic Flow and Feature Aided Optimizing Track (TFOT)

Period of Performance: 06/01/2016 - 12/19/2016

$80K

Phase 1 SBIR

Recipient Firm

Daniel H. Wagner, Assoc., Inc.
559 West Uwchlan Avenue Array
Exton, PA 19341
Firm POC
Principal Investigator

Abstract

The goal of this Traffic Flow and Feature Aided Optimized Tracking (TFOT) project is to develop an efficient and effective target tracker that, using all available data, in particular traffic flow data and non-kinematic features/attributes, can: (1) Generate more persistent, accurate, and actionable tracks, and (2) Be relatively easily integrated into existing airborne (or UAS control) systems. A key enabler for these capabilities is the ability of the advanced (1) Kalman Filter algorithms, (2) multiple hypothesis data association, and (3) Bayesian Network (BN)-based Feature Aided Track Association (FATA) [3] algorithms in TFOT to effectively utilize:Whatever intelligence is available concerning normal traffic flow in the area of interest, andAny available sensor non-kinematic (i.e., feature/attribute) data in order to improve data association accuracy, and thus target tracking.