Dr. Stephen C. Stubberud Profile

Dr. Stephen C. Stubberud

Senior Principal Engineer

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Author

Publications (10)

Proceedings Article | 28 April 2010 Paper

Track-to-track association using a phase-only filter

Stephen Stubberud, Kathleen Kramer

Proceedings Volume 7697, 769709 (2010) https://doi.org/10.1117/12.849602

KEYWORDS: Phase only filters, Sensors, Optical correlators, Kinematics, Automatic target recognition, Image processing, Target recognition, Correlation function, Nonlinear optics, Physics

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Proceedings Article | 11 May 2009 Paper

Effects of measurement unobservability on neural extended Kalman filter tracking

Stephen Stubberud, Kathleen Kramer

Proceedings Volume 7336, 733605 (2009) https://doi.org/10.1117/12.818384

KEYWORDS: Neural networks, Sensors, Filtering (signal processing), Motion models, Error analysis, Detection and tracking algorithms, Motion estimation, Surveillance, Electronic support measures, Time metrology

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An important component of tracking fusion systems is the ability to fuse various sensors into a coherent picture of the scene. When multiple sensor systems are being used in an operational setting, the types of data vary. A significant but often overlooked concern of multiple sensors is the incorporation of measurements that are unobservable. An unobservable measurement is one that may provide information about the state, but cannot recreate a full target state. A line of bearing measurement, for example, cannot provide complete position information. Often, such measurements come from passive sensors such as a passive sonar array or an electronic surveillance measure (ESM) system. Unobservable measurements will, over time, result in the measurement uncertainty to grow without bound. While some tracking implementations have triggers to protect against the detrimental effects, many maneuver tracking algorithms avoid discussing this implementation issue. One maneuver tracking technique is the neural extended Kalman filter (NEKF). The NEKF is an adaptive estimation algorithm that estimates the target track as it trains a neural network on line to reduce the error between the a priori target motion model and the actual target dynamics. The weights of neural network are trained in a similar method to the state estimation/parameter estimation Kalman filter techniques. The NEKF has been shown to improve target tracking accuracy through maneuvers and has been use to predict target behavior using the new model that consists of the a priori model and the neural network. The key to the on-line adaptation of the NEKF is the fact that the neural network is trained using the same residuals as the Kalman filter for the tracker. The neural network weights are treated as augmented states to the target track. Through the state-coupling function, the weights are coupled to the target states. Thus, if the measurements cause the states of the target track to be unobservable, then the weights of the neural network have unobservable modes as well. In recent analysis, the NEKF was shown to have a significantly larger growth in the eigenvalues of the error covariance matrix than the standard EKF tracker when the measurements were purely bearings-only. This caused detrimental effects to the ability of the NEKF to model the target dynamics. In this work, the analysis is expanded to determine the detrimental effects of bearings-only measurements of various uncertainties on the performance of the NEKF when these unobservable measurements are interlaced with completely observable measurements. This analysis provides the ability to put implementation limitations on the NEKF when bearings-only sensors are present.

Proceedings Article | 17 April 2008 Paper

Incorporation of indirect evidence into an evidence accrual technique for higher level data fusion

Stephen Stubberud, Kathleen Kramer

Proceedings Volume 6968, 696811 (2008) https://doi.org/10.1117/12.776578

KEYWORDS: Filtering (signal processing), Fuzzy logic, Algorithm development, Data fusion, Taxonomy, Detection and tracking algorithms, Quality measurement, Process modeling, Standards development, Distance measurement

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Proceedings Article | 7 May 2007 Paper

Impact and point prediction using a neural extended Kalman filter with multiple sensors

Stephen Stubberud, Kathleen Kramer

Proceedings Volume 6567, 656702 (2007) https://doi.org/10.1117/12.713614

KEYWORDS: Sensors, Motion models, Neural networks, Filtering (signal processing), Systems modeling, Mathematical modeling, Motion estimation, Error analysis, 3D modeling, Knowledge management

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Proceedings Article | 3 May 2006 Paper

Impact time and point prediction: an analysis of the capabilities of a neural extended Kalman filter

Stephen Stubberud, Kathleen Kramer

Proceedings Volume 6235, 623502 (2006) https://doi.org/10.1117/12.663033

KEYWORDS: Motion models, Neural networks, Filtering (signal processing), Mathematical modeling, Detection and tracking algorithms, Motion estimation, Systems modeling, Sensors, Weapons, Data modeling

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Proceedings Article | 15 September 2005 Paper

Analysis of the NEKF performance in a multisensor environment

Stephen Stubberud, Kathleen Kramer

Proceedings Volume 5913, 591310 (2005) https://doi.org/10.1117/12.613061

KEYWORDS: Sensors, Neural networks, Filtering (signal processing), Error analysis, Motion models, Statistical modeling, Statistical analysis, Monte Carlo methods, Distance measurement, Kinematics

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Proceedings Article | 28 March 2005 Paper

Maneuver tracking using an adaptive Gaussian sum technique

Stephen Stubberud, Kathleen Kramer

Proceedings Volume 5813, (2005) https://doi.org/10.1117/12.601106

KEYWORDS: Motion models, Filtering (signal processing), Error analysis, Motion estimation, Motion analysis, Mathematical modeling, Data modeling, Algorithm development, Detection and tracking algorithms, Performance modeling

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Proceedings Article | 25 August 2003 Paper

Data fusion: a conceptual approach to level 2 fusion (situational assessment)

Stephen Stubberud, Peter Shea, Dale Klamer

Proceedings Volume 5096, (2003) https://doi.org/10.1117/12.484887

KEYWORDS: Data fusion, Algorithm development, Filtering (signal processing), Kinematics, Information fusion, Detection and tracking algorithms, Artillery, Manufacturing, Clouds, Mirrors

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Proceedings Article | 27 July 1999 Paper

Application of online measures to monitor and evaluate multiplatform fusion performance

Stephen Stubberud, Charlene Kowalski, Dale Klamer

Proceedings Volume 3720, (1999) https://doi.org/10.1117/12.357170

KEYWORDS: Sensors, Data fusion, Data processing, Quality measurement, Probability theory, Computing systems, Information theory, Filtering (signal processing), Data analysis, Reliability

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Proceedings Article | 12 March 1999 Paper

Fuzzy logic approach for data association

Stephen Stubberud, Kristi Lugo

Proceedings Volume 3719, (1999) https://doi.org/10.1117/12.341346

KEYWORDS: Fuzzy logic, Sensors, Data fusion, Error analysis, Algorithm development, Matrices, Data processing, Distance measurement, Stars, Fuzzy systems

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Showing 5 of 10 publications

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