Dr. David R. Iny Profile

Dr. David R. Iny

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Proceedings Article | 21 September 2007 Paper

Theory and practical application of out of sequence measurements with results for multi-static tracking

David Iny

Proceedings Volume 6699, 66990A (2007) https://doi.org/10.1117/12.732223

KEYWORDS: Filtering (signal processing), Detection and tracking algorithms, Algorithm development, Time metrology, Target detection, Matrices, Mahalanobis distance, Radium, Monte Carlo methods, Data processing

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This paper addresses the out-of-sequence measurement (OOSM) problem associated with multiple platform tracking systems. The problem arises due to different transmission delays in communication of detection reports across platforms. Much of the literature focuses on the improvement to the state estimate by incorporating the OOSM. As the time lag increases, there is diminishing improvement to the state estimate. However, this paper shows that optimal processing of OOSMs may still be beneficial by improving data association as part of a multi-target tracker. This paper derives exact multi-lag algorithms with the property that the standard log likelihood track scoring is independent of the order in which the measurements are processed. The orthogonality principle is applied to generalize the method of Bar- Shalom in deriving the exact A1 algorithm for 1-lag estimation. Theory is also developed for optimal filtering of time averaged measurements and measurements correlated through periodic updates of a target aim-point. An alternative derivation of the multi-lag algorithms is also achieved using an efficient variant of the augmented state Kalman filter (AS-KF). This results in practical and reasonably efficient multi-lag algorithms. Results are compared to a well known ad hoc algorithm for incorporating OOSMs. Finally, the paper presents some simulated multi-target multi-static scenarios where there is a benefit to processing the data out of sequence in order to improve pruning efficiency.

Proceedings Article | 17 June 1996 Paper

Quantitative analysis of HRR NCTR performance drivers

David Iny, Martin Morici

Proceedings Volume 2747, (1996) https://doi.org/10.1117/12.243073

KEYWORDS: Statistical analysis, Target recognition, Data modeling, Radar, Databases, Error analysis, Composites, Performance modeling, Quantitative analysis, Statistical modeling

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Over the past several years Northrop Grumman has been developing Non-Cooperative Target Recognition (NCTR) technology using High Range Resolution (HRR) Radar data. Common to all NCTR efforts is the need to train classifier algorithms on limited sources of data. The classifier design must also address signature variations with aspect viewing angles and stores configurations. This paper will provide a methodology for quantifying training data segmentation issues including: (1) Degradation due to limited samples within an aspect zone; (2) Stability of scattering centers as a function of aspect angle; and (3) Stores variations. In a program supported by Wright Patterson AFB, Northrop Grumman has developed a detailed statistical model of the Airborne Radar Target Identification HRR signature data. The statistical model is based on a template alignment procedure. This model provides an analytic basis for predicting classifier performance using an associated distance metric. This paper will provide a brief discussion of our template classifier and apply the analytic model to the segmentation issues in the previous paragraph.

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