USA flag logo/image

An Official Website of the United States Government

Feature Based Machine Leaning for Multiple Target Detection and Debris…

Award Information

Department of Defense
Award ID:
Program Year/Program:
2013 / SBIR
Agency Tracking Number:
Solicitation Year:
Solicitation Topic Code:
Solicitation Number:
Small Business Information
601 Hutton St Suite 109 Raleigh, NC -
View profile »
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No
Phase 1
Fiscal Year: 2013
Title: Feature Based Machine Leaning for Multiple Target Detection and Debris Mitigation
Agency: DOD
Contract: HQ0147-13-C-7523
Award Amount: $99,977.00


In this research effort, Vadum will demonstrate the feasibility of a machine learning approach to address the problem of debris mitigation and improve multiple target discrimination. This algorithm is a very fast, highly accurate multi-class approach based upon the concepts of bagging (bootstrap aggregation), boosting and random subspace projection. This algorithm will allow for de-emphasis (probabilistic soft decisions) or suppression (hard decisions) of uninteresting scatterers, while maintaining ballistic missile target tracks within the BMDS (Ballistic Missile Defense System) threat environment. The approach inherently manages large data sets, high dimensionality, missing features and sample outliers while being cautious of over-fitting. The approach has been applied in the research areas of: malware/phishing/spam detection, ovarian cancer detection, protein interaction prediction, real-time human pose recognition and general feature selection. This proposal presents the novel application of this approach to ballistic target detection and debris mitigation.

Principal Investigator:

Eric Fails
Sr. Engineer
(919) 341-8241

Business Contact:

Gary Edge
Chief Executive Officer
(919) 341-8241
Small Business Information at Submission:

601 Hutton St STE 109 Raleigh, NC -

EIN/Tax ID: 320118186
Number of Employees:
Woman-Owned: No
Minority-Owned: No
HUBZone-Owned: No