Core Technologies

Core Technologies

Image Processing Technology

Image Processing

Numerica excels at the processing of non-resolved imagery collected by visual and infrared sensors, both day and night.

Tracking and Data Fusion

Numerica’s patented optimization-based approach to multiple hypothesis target tracking, entitled Multiple Frame Assignment (MFA), delivers robust real-time performance in stressful scenarios

Resource Management Technology

Resource Management

Numerica has worked in the area of resource management (i.e., sensor management and battle management) for over a decade.

Positioning, Navigation and Timing Technology

Positioning, Navigation and Timing

Positioning, Navigation, and Timing (PNT) is a critical capability to the United States military, government services, and commercial sector.

Read some of our peer-reviewed papers to see why we’re regarded as thought leaders in Air, Missile, and Space Defense, and Image Processing.

Space Defense

Space

Read some of our peer-reviewed papers to see why we’re regarded as thought leaders in Space Defense

DAYTIME GEO TRACKING WITH AQUILA

J. Shaddix, J. Brannum, A. Ferris, A. Hariri, A. Larson, T. Mancini, and J. Aristoff, “Daytime GEO tracking with ‘Aquila’: approach and results from a new ground-based SWIR small telescope system,” Proc. of the 2019 Advanced Maui Optical and Space Surveillance Technologies Conference, (Wailea, HI), Sep. 2019.

NON-TRADITIONAL DATA COLLECTION AND EXPLOITATION FOR IMPROVED GEO SSA VIA A GLOBAL NETWORK OF HETEROGENEOUS SENSORS

J. Aristoff, A. Ferris, A. Larson, A. Hariri, N. Singh, J. Horwood, J. Shaddix, K. Wilson, T. Lyons, and N. Dhingra, “Non-traditional data collection and exploitation for improved GEO SSA via a global network of heterogeneous sensors,” in Proc. of the 2018 Advanced Maui Optical and Space Surveillance Technologies Conference, (Wailea, HI), Sept. 2018.

COVARIANCE AND UNCERTAINTY REALISM IN SPACE SURVEILLANCE AND TRACKING

A.B. Poore, J. M. Aristoff, and J. T. Horwood, “Covariance and uncertainty realism in space surveillance and tracking.” Astrodynamics Innovation Committee. Tech. Rep. AD1020892, 2016

KRATOS: KOLLISION RISK ASSESSMENT TOOL IN ORBITAL ELEMENT SPACES

J. T. Horwood, N. Singh, J. M. Aristoff, and A. Bhopale, “KRATOS: Kollision Risk Assessment Tool in Orbital Element Spaces.” Proceedings of the 2016 Advanced Maui Optical and Space Surveillance Technologies Conference, Wailea, HI, September 2016

ATHENA: A DATA-DRIVEN ANOMALY DETECTION AND SPACE OBJECT CLASSIFICATION TOOL FOR SSA

N. Singh, J. T. Horwood, J. M. Aristoff, and J. Murray-Krezan, “Athena: a data-driven anomaly detection and space object classification tool for SSA,” Proc. of the 26th AAS/AIAA Space Flight Mechanics Meeting, (Napa, CA), Feb. 2016.

MULTIPLE FRAME ASSIGNMENT SPACE TRACKER (MFAST): RESULTS ON UCT PROCESSING

J. M. Aristoff, D. J. C. Beach, P. A. Ferris, J. T. Horwood, A. D. Mont, N. Singh, and A. B. Poore, “Multiple Frame Assignment Space Tracker (MFAST): results on UCT processing,” Proc. of the 2015 AIAA/AAS Astrodynamics Specialist Conference, (Vail, CO), Aug. 2015.

IMPLICIT RUNGE-KUTTA-BASED METHODS FOR FAST, PRECISE, AND SCALABLE UNCERTAINTY PROPAGATION

J. M. Aristoff, J. T. Horwood, and A. B. Poore, “Implicit Runge-Kutta-based methods for fast, precise, and scalable uncertainty propagation,” Celestial Mechanics and Dynamical Astronomy, vol. 122, pp. 169-182, 2015.

GAUSS VON MISES DISTRIBUTION FOR IMPROVED UNCERTAINTY REALISM IN SPACE SITUATIONAL AWARENESS

J. T. Horwood and A. B. Poore, “Gauss von Mises distribution for improved uncertainty realism in space situational awareness,” SIAM Journal of Uncertainty Quantification, vol. 2, pp. 276-304, 2014.

NONLINEAR UNCERTAINTY PROPAGATION IN ORBITAL ELEMENTS AND TRANSFORMATION TO CARTESIAN SPACE WITHOUT LOSS OF REALISM

J. M. Aristoff, J. T. Horwood, N. Singh, and A. B. Poore, “Nonlinear uncertainty propagation in orbital elements and transformation to Cartesian space without loss of realism,” Proc. of the 2014 AAS/AIAA Astrodynamics Specialist Conference, (San Diego, CA), Aug. 2014.

BEYOND COVARIANCE REALISM: A NEW METRIC FOR UNCERTAINTY REALISM

J. T. Horwood, J. M. Aristoff, N. Singh, A. B. Poore, and M. D. Hejduk, “Beyond covariance realism: a new metric for uncertainty realism,” Proc. of the SPIE, Signal and Data Processing of Small Targets, vol. 9092, (Baltimore, MD), May 2014.

A COMPARATIVE STUDY OF NEW NON-LINEAR UNCERTAINTY PROPAGATION METHODS FOR SPACE SURVEILLANCE

J. T. Horwood, J. M. Aristoff, N. Singh, and A. B. Poore, “A comparative study of new non-linear uncertainty propagation methods for space surveillance,” Proc. of the SPIE, Signal and Data Processing of Small Targets, vol. 9092, (Baltimore, MD), May 2014.

ORBIT AND UNCERTAINTY: A COMPARISON OF GAUSS-LEGENDRE-, DORMAND-PRINCE-, AND CHEBYSHEV-PICARD-BASED APPROACHES

J. M. Aristoff, J. T. Horwood, and A. B. Poore, “Orbit and uncertainty propagation: a comparison of Gauss-Legendre-, Dormand-Prince-, and Chebyshev-Picard-based approaches,” Celestial Mechanics and Dynamical Astronomy, vol. 117, pp. 13-28, 2013.

SPACE OBJECT MANEUVER DETECTION VIA A JOINT OPTIMAL CONTROL AND MULTIPLE HYPOTHESIS TRACKING APPROACH

N. Singh, J. T. Horwood, and A. B. Poore, “Space object maneuver detection via a joint optimal control and multiple hypothesis tracking approach,” Proc. of the 22nd AAS/AIAA Space Flight Mechanics Meeting, (Charleston, SC), Jan. 2012.

GAUSSIAN SUM FILTERS FOR SPACE SURVEILLANCE: THEORY AND SIMULATIONS

J. T. Horwood, N. D. Aragon, and A. B. Poore, “Gaussian sum filters for space surveillance: theory and simulations,” Journal of Guidance, Control, and Dynamics, vol. 34, pp. 1839-1851, 2011.

Air and Missile Defense

Air and Missile

Our growing list of patents demonstrates our contribution to the state of the art in Air and Missile Defense.

INFORMATION-BASED DATA PRIORITIZATION IN DISTRIBUTED TRACKING SYSTEMS

N. Coult, J. N. Knight, W. Leed, S. Danford, R. Paffenroth, and A. Poore, “Information-based data prioritization in distributed tracking systems,” Proc. of the SPIE, Signal and Data Processing of Small Targets, vol. 8393, Apr. 2012.

AMBIGUOUS DATA ASSOCIATION AND ENTANGLED ATTRIBUTE ESTIMATION

D. Trawick, P. C. Du Toit, R. C. Paffenroth, and G. J. Norgard, “Ambiguous data association and entangled attribute estimation,” Proc. of the SPIE, Signal and Data Processing of Small Targets, vol. 8393, Apr. 2012.

NETWORK-CENTRIC ANGLE ONLY TRACKING USING RANK-DEFICIENT FILTERING

N. Coult and J. N. Knight, “Network-centric angle only tracking using rank-deficient filtering,” Proc. of the SPIE, Signal and Data Processing of Small Targets, vol. 7698, Apr. 2010.

ASSOCIATION AMBIGUITY MANAGEMENT IN MIXED DATA DIMENSION TRACKING PROBLEMS

J. R. Thornbrue, J. N. Knight, and B. J. Slocumb, “Association ambiguity management in mixed data dimension tracking problems,” Proc. of the SPIE, Signal and Data Processing of Small Targets, vol. 7698, Apr. 2010.

A TRACKER ADJUNCT PROCESSING SYSTEM FOR RECONSIDERATION OF FIRM TRACKER DECISIONS

D. Trawick, B. Slocumb, and R. Paffenroth, “A tracker adjunct processing system for reconsideration of firm tracker decisions,” Proc. of the SPIE, Signal and Data Processing of Small Targets, vol. 7698, Apr. 2010.

A DISTRIBUTED DATABASE VIEW OF NETWORK TRACKING SYSTEMS

J. R. Thornbrue, J. N. Knight, and B. J. Slocumb, “Association ambiguity management in mixed data dimension tracking problems,” Proc. of the SPIE, Signal and Data Processing of Small Targets, vol. 7698, Apr. 2010.

MULTIPLE HYPOTHESIS CORRELATION IN TRACK-TO-TRACK FUSION MANAGEMENT

A. B. Poore, S. M. Gadaleta, and B. J. Slocumb, “Multiple hypothesis correlation in track-to-track fusion management,” Handbook on Modeling for Discrete Optimization (International Series in Operations Research & Management Science), Gautam Appa, Leonidas, (Boston, MA), 2006.

SURVEILLANCE RADAR RANGE-BEARING CENTROID PROCESSING, PART II: MERGED MEASUREMENTS

B. J. Slocumb and D. L. Macumber, “Surveillance radar range-bearing centroid processing, part II: merged measurements,” Proc. of the SPIE, Signal and Data Processing of Small Targets, vol. 6236, May 2006.

COMPLEXITY REDUCTION IN MHT/MFA TRACKING: PART II, HIERARCHICAL IMPLEMENTATION AND SIMULATION RESULTS

B. J. Slocumb and A. B. Poore, “Complexity reduction in MHT/MFA tracking: part II, hierarchical implementation and simulation results,” in Proc. of the SPIE, Signal and Data Processing of Small Targets, vol. 6236, Apr. 2006

SHORT-TERM AMBIGUITY ASSESSMENT TO AUGMENT TRACKING DATA ASSOCIATION INFORMATION

S. M. Gadaleta, S. M. Herman, S. A. Miller, F. Obermeyer, B. J. Slocumb, A. B. Poore, M. Levedahl, and B. Brewington, “Short-term ambiguity assessment to augment tracking data association information,” Proc. of the 8th International Conference on Information Fusion, (Philadelphia, PA), Jul. 2005.

A MULTIPLE MODEL SNR/RCS LIKELIHOOD RATIO SCORE FOR RADAR-BASED FEATURE-AIDED TRACKING

B. J. Slocumb and M. E. Klusman III, “A multiple model SNR/RCS likelihood ratio score for radar-based feature-aided tracking,” Proc. of the SPIE, Signal and Data Processing of Small Targets, vol. 5913, Sep. 2005.

FRAME BUILDING ALGORITHM FOR ELECTRONICALLY SCANNED ARRAY RADAR

B. J. Slocumb, M. E. Klusman III, “Frame-building algorithm for electronically scanned array radar,” Proc. of the SPIE, Signal and Data Processing of Small Targets, vol. 5913, Sep. 2005.

BATCH MAXIMUM LIKELIHOOD (ML) AND MAXIMUM A POSTERIORI (MAP) ESTIMATION WITH PROCESS NOISE FOR TRACKING APPLICATIONS

A. B. Poore, B. J. Slocumb, B. J. Suchomel, F. H. Obermeyer, S. M. Herman, and S. M. Gadaleta, “Batch maximum likelihood (ML) and maximum a posteriori (MAP) estimation with process noise for tracking applications,”Proc. of the SPIE, Signal and Data Processing of Small Targets, 2003.

TRACKING CLOSELY-SPACED, POSSIBLY UNRESOLVED, RAYLEIGH TARGETS: IDEALIZED RESOLUTION

W. D. Blair, B. J. Slocumb, G. C. Brown, and A. H. Register, “Tracking closely-spaced, possibly unresolved, Rayleigh targets: idealized resolution,” Proc. of the 2002 IEEE Aerospace Conference, vol. 4, 2002.

MULTIPLE FRAME CLUSTER TRACKING

S. Gadaleta, M. Klusman, A. B. Poore, and B. J. Slocumb, “Multiple frame cluster tracking,” Proc. of the SPIE, Signal and Data Processing of Small Targets, vol. 4728, 2002.

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