TECHNOLOGY AREAS: Air Platform, Information Systems

ACQUISITION PROGRAM: PMA-262, Persistent Unmanned Aircraft Systems

RESTRICTION ON PERFORMANCE BY FOREIGN CITIZENS (i.e., those holding non-U.S. Passports): This topic is "ITAR Restricted." The information and materials provided pursuant to or resulting from this topic are restricted under the International Traffic in Arms Regulations (ITAR), 22 CFR Parts 120 - 130, which control the export of defense-related material and services, including the export of sensitive technical data. Foreign Citizens may perform work under an award resulting from this topic only if they hold the "Permanent Resident Card", or are designated as "Protected Individuals" as defined by 8 U.S.C. 1324b(a)(3). If a proposal for this topic contains participation by a foreign citizen who is not in one of the above two categories, the proposal will be rejected.

OBJECTIVE: Develop a method that will analyze different approaches of combining tracks from multiple disparate data sources and identify the approach that results in the best overall track accuracy within the processing and time constraints available.

DESCRIPTION: Autonomous air vehicles, such as Broad Area Maritime Surveillance (BAMS) or Firescout have the ability to track surface targets. They also often have multiple sensors and sources such as radar, electro-optical/infrared (EO/IR), and data streams from other sensors available to track targets. These data sources produce many tracks, sometimes numbering in the thousands, to combine. Furthermore, the various sources will differ in track count, accuracy, update rates, and uncertainty.

There are currently several algorithms for merging the tracks from these sensors/sources. However, there is no method for determining which sets of algorithms work best together to combine the tracks or how much better the track accuracy results will be using a given method. A model-based tool is required that will be able to show how the various sources should be combined to best improve the overall accuracy within the processing and time constraints available. The model must be sufficiently robust and must able to work with known data streams. Particular attention should be given to the primary sensors used today including radar, Automated Information Systems (AIS), and Electronic Surveillance Measures (ESM).

The state of the art is a set of various algorithms using processed sensor data with time tags, but unsatisfactory levels of accuracy and confidence. The desired outcome of this work is a tool that will use the existing algorithms, incorporate new algorithms, and combine, contrast and compare the results to produce the best possible identifications of targets at sea. The purpose of this work is to develop the methods for performing this synthesis of algorithmic output, including feed back to influence sensor processing to improve track merger success.

Various simulation tools to model the environment, targets, and sensors will be required.

The ability to filter tracks is required as well as ability to quickly create relevant scenarios and track evaluation methods. The tracking filtering tools should include Kalman filters and multi-track association algorithms. The capability of these tools as well as the visualization/analysis tools to analyze the tracking results should be discussed.

PHASE I: Determine the feasibility of the proposed approach by tailoring simulation for a specified problem/domain and develop a set of relevant scenarios. Provide data sets as the basis for further analysis.

PHASE II: Refine simulation based on government-provided information and produce refined data sets. Provide discussion and results to help validate model assumptions. Use data sets to provide comparison between the various track-to-track methodologies. Perform analysis to quantitatively show comparison tracking results. Perform initial approximation to quantify how data sources and methods relate. Demonstrate the prototype system.

NOTE: The prospective contractor(s) must be U.S. Owned and Operated with no Foreign Influence as defined by DOD 5220.22-M, National Industrial Security Program Operating Manual, unless acceptable mitigating procedures can and have been be implemented and approved by the Defense Security Service (DSS). The selected contractor and/or subcontractor must be able to acquire and maintain a secret level facility and Personnel Security Clearances, in order to perform on advanced phases of this contract as set forth by DSS and NAVAIR in order to gain access to classified information pertaining to the national defense of the United States and its allies; this will be an inherent requirement. The selected company will be required to safeguard classified material IAW DoD 5220.22-M during the advance phases of this contract.

PHASE III: Develop and transition the model into an analysis tool that will provide how best to combine tracks from specified sources to optimize tracker performance.

PRIVATE SECTOR COMMERCIAL POTENTIAL/DUAL-USE APPLICATIONS: Any commercial corporation that tracks shipments or vehicles using multiple data sources, counter-drug and counter-terrorism operations, or traffic monitoring networks could all potentially benefit from this product.

REFERENCES:
1. Asadi, M., Regazzoni, C.S. (2007). A Comparison of Different Approaches to Nonlinear Shift Estimation for Object Tracking. IEEE International Conference on Image Processing, Vol 3, 221-224.

2. Blanc, C., Trassoudaine, L., Gallice, J. (2005). EKF and particle filter track-to-track fusion: a quantitative comparison from radar/lidar obstacle tracks. The Eighth International Conference on Information Fusion, Vol 2, 1303-1310.

3. Blair, W.D., & Bar-Shalom, Y. Tracking maneuvering targets with multiple sensors: Does more data always mean better estimates? IEEE Transactions on Aerospace and Electronic Systems, 32(1), 450-456.

KEYWORDS: Data Fusion; Track-To-Track Tracking; Multi-Track Association; Modeling and Simulation; Track Accuracy; Sensors

** TOPIC AUTHOR (TPOC) **

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