Dr. Tom Aridgides Profile

Dr. Tom Aridgides

Senior Staff Engineer at Lockheed Martin Maritime Systems & Sensors

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Author

Publications (26)

Proceedings Article | 19 May 2011 Paper

Practical optimal processing in hyperdimensional spaces via domain-reducing mappings

Manuel Fernández, Tom Aridgides, Firooz Sadjadi

Proceedings Volume 8049, 80490R (2011) https://doi.org/10.1117/12.885260

KEYWORDS: Digital filtering, Associative arrays, Data modeling, Signal to noise ratio, Image processing, Signal detection, Image filtering, Chemical elements, Nonlinear filtering, Signal processing

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Proceedings Article | 29 April 2010 Paper

Image-based ATR utilizing adaptive clutter filter detection, LLRT classification, and Volterra fusion with application to side-looking sonar

Tom Aridgides, Manuel Fernández

Proceedings Volume 7664, 76640R (2010) https://doi.org/10.1117/12.846596

KEYWORDS: Automatic target recognition, Detection and tracking algorithms, Image processing, Feature extraction, Digital filtering, Image fusion, Image classification, Feature selection, Algorithm development, Data processing

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Proceedings Article | 16 April 2010 Paper

A complex-domain adaptive order statistic filter and its application to signal detection in non-Gaussian noise and clutter

Manuel Fernández, Tom Aridgides

Proceedings Volume 7698, 769802 (2010) https://doi.org/10.1117/12.848550

KEYWORDS: Digital filtering, Nonlinear filtering, Electronic filtering, Linear filtering, Interference (communication), Signal processing, Signal to noise ratio, Gaussian filters, Image filtering, Associative arrays

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Proceedings Article | 5 May 2009 Paper

Recent ATR and fusion algorithm improvements for multiband sonar imagery

Tom Aridgides, Manuel Fernández

Proceedings Volume 7303, 73030C (2009) https://doi.org/10.1117/12.818542

KEYWORDS: Automatic target recognition, Detection and tracking algorithms, Image processing, Image fusion, Feature extraction, Algorithm development, Image classification, Feature selection, Digital filtering, Data processing

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Proceedings Article | 29 April 2008 Paper

Enhanced ATR algorithm for high resolution multi-band sonar imagery

Tom Aridgides, Manuel Fernández

Proceedings Volume 6953, 69530H (2008) https://doi.org/10.1117/12.773304

KEYWORDS: Detection and tracking algorithms, Automatic target recognition, Image processing, Feature extraction, Image fusion, Algorithm development, Data processing, Feature selection, Image classification, Digital filtering

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Proceedings Article | 26 April 2007 Paper

Automated target classification in high resolution dual frequency sonar imagery

Tom Aridgides, Manuel Fernández

Proceedings Volume 6553, 65530S (2007) https://doi.org/10.1117/12.717739

KEYWORDS: Detection and tracking algorithms, Image fusion, Image processing, Algorithm development, Image classification, Feature extraction, Target detection, Feature selection, Digital filtering, Data fusion

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Proceedings Article | 18 May 2006 Paper

Detailed investigation of cascaded Volterra fusion of processing strings for automated sea mine classification in very shallow water

Tom Aridgides, Manuel Fernández

Proceedings Volume 6217, 62171C (2006) https://doi.org/10.1117/12.663768

KEYWORDS: Detection and tracking algorithms, Image processing, Algorithm development, Feature extraction, Mining, Naval mines, Image fusion, Feature selection, Digital filtering, Data fusion

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Proceedings Article | 10 June 2005 Paper

Volterra fusion of processing strings for automated sea mine classification in shallow water

Tom Aridgides, Manuel Fernandez, Gerald Dobeck

Proceedings Volume 5794, (2005) https://doi.org/10.1117/12.602116

KEYWORDS: Detection and tracking algorithms, Image processing, Algorithm development, Feature extraction, Mining, Digital filtering, Naval mines, Data fusion, Image fusion, Feature selection

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Proceedings Article | 21 September 2004 Paper

Improved processing-string fusion-approach investigation for automated sea-mine classification in shallow water

Tom Aridgides, Manuel Fernandez, Gerald Dobeck

Proceedings Volume 5415, (2004) https://doi.org/10.1117/12.541007

KEYWORDS: Detection and tracking algorithms, Image processing, Mining, Feature extraction, Data fusion, Digital filtering, Algorithm development, Image fusion, Naval mines, Image classification

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An improved sea mine computer-aided-detection/computer-aided-classification (CAD/CAC) processing string has been developed. This robust automated processing string involves the fusion of the outputs of unique mine classification algorithms. The overall CAD/CAC processing string consists of pre-processing, adaptive clutter filtering (ACF), normalization, detection, feature extraction, optimal subset feature selection, feature orthogonalization, classification and fusion processing blocks. The range-dimension ACF is matched both to average highlight and shadow information, while also adaptively suppressing background clutter. For each detected object, features are extracted and processed through an orthogonalization transformation, enabling an efficient application of the optimal log-likelihood-ratio-test (LLRT) classification rule, in the orthogonal feature space domain. The classified objects of 4 distinct processing strings are fused using the classification confidence values as features and “M-out-of-N”, or LLRT-based fusion rules. The utility of the overall processing strings and their fusion was demonstrated with new shallow water high-resolution sonar imagery data. The processing string detection and classification parameters were tuned and the string classification performance was optimized, by appropriately selecting a subset of the original feature set. Two significant improvements were made to the CAD/CAC processing string by employing sub-image adaptive clutter filtering (SACF) and utilizing a repeated application of the subset feature selection/feature orthogonalization/LLRT classification blocks. It was shown that LLRT-based fusion of the CAD/CAC processing strings outperforms the “M-out-of-N” algorithms and results in up to a seven-fold false alarm rate reduction, compared to the best single CAD/CAC processing string results, while maintaining a high correct mine classification probability. Alternately, the fusion of the processing strings enabled correct classification of almost all mine targets, while simultaneously maintaining a very low false alarm rate. A novel investigation was also presented that illustrates and provides insights on the increased performance gains provided by utilizing LLRT-based fusion of all the different combinations of 2, 3 or 4 distinct processing strings.

Proceedings Article | 11 September 2003 Paper

Recent processing string and fusion algorithm improvements for automated sea mine classification in shallow water

Tom Aridgides, Manuel Fernandez, Gerald Dobeck

Proceedings Volume 5089, (2003) https://doi.org/10.1117/12.487775

KEYWORDS: Detection and tracking algorithms, Feature extraction, Data fusion, Algorithm development, Mining, Image processing, Naval mines, Image fusion, Feature selection, Image classification

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Proceedings Article | 11 September 2003 Paper

Measures for evaluating sea mine identification processing performance and the enhancements provided by fusing multisensor/multiprocess data via an M-out-of-N voting scheme

Manuel Fernandez, Tom Aridgides

Proceedings Volume 5089, (2003) https://doi.org/10.1117/12.487774

KEYWORDS: Mining, Palladium, Sensors, Land mines, Data modeling, Error analysis, Astatine, Naval mines, Performance modeling, System identification

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Proceedings Article | 13 August 2002 Paper

Fusion approach investigation for sea mine classification in very shallow water

Tom Aridgides, Manuel Fernandez, Gerald Dobeck

Proceedings Volume 4742, (2002) https://doi.org/10.1117/12.479114

KEYWORDS: Detection and tracking algorithms, Image processing, Data fusion, Feature extraction, Algorithm development, Mining, Image fusion, Naval mines, Land mines, Digital filtering

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Proceedings Article | 18 October 2001 Paper

Side-scan sonar imagery fusion for sea mine detection and classification in very shallow water

Tom Aridgides, Manuel Fernandez, Gerald Dobeck

Proceedings Volume 4394, (2001) https://doi.org/10.1117/12.445440

KEYWORDS: Detection and tracking algorithms, Image fusion, Data fusion, Feature extraction, Algorithm development, Image processing, Image classification, Naval mines, Digital filtering, Feature selection

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Proceedings Article | 22 August 2000 Paper

Adaptive subspace-based background normalizer for sea mine classification

Manuel Fernandez, Tom Aridgides

Proceedings Volume 4038, (2000) https://doi.org/10.1117/12.396267

KEYWORDS: Digital filtering, Filtering (signal processing), Mining, Target detection, Electronic filtering, Optical filters, Linear filtering, Naval mines, Spatial resolution, Signal processing

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Proceedings Article | 22 August 2000 Paper

Fusion of sea mine detection and classification processing strings for sonar imagery

Tom Aridgides, Manuel Fernandez, Gerald Dobeck

Proceedings Volume 4038, (2000) https://doi.org/10.1117/12.396266

KEYWORDS: Detection and tracking algorithms, Algorithm development, Image processing, Image fusion, Data fusion, Feature extraction, Image classification, Naval mines, Digital filtering, Image filtering

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Proceedings Article | 2 August 1999 Paper

Adaptive nonstationary filtering procedure for sea mine classification

Manuel Fernandez, Tom Aridgides

Proceedings Volume 3710, (1999) https://doi.org/10.1117/12.357087

KEYWORDS: Digital filtering, Image processing, Palladium, Naval mines, Electronic filtering, Image filtering, Numerical stability, Linear filtering, Monte Carlo methods, Radar

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Proceedings Article | 2 August 1999 Paper

Adaptive clutter suppression, sea mine detection/classification, and fusion processing string for sonar imagery

Tom Aridgides, Manuel Fernandez, Gerald Dobeck

Proceedings Volume 3710, (1999) https://doi.org/10.1117/12.357084

KEYWORDS: Image processing, Sensors, Image fusion, Target detection, Digital filtering, Land mines, Data fusion, Feature extraction, Filtering (signal processing), Naval mines

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Proceedings Article | 4 September 1998 Paper

Adaptive clutter suppression and fusion processing string for sea mine detection and classification in sonar imagery

Tom Aridgides, Manuel Fernandez, Gerald Dobeck

Proceedings Volume 3392, (1998) https://doi.org/10.1117/12.324196

KEYWORDS: Image fusion, Digital filtering, Image processing, Target detection, Mining, Image classification, Sensors, Land mines, Data fusion, Filtering (signal processing)

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An advanced, automatic, adaptive clutter suppression, pre- and post-detection level fusion, sea mine detection and classification processing string has been developed and tested with sonar imagery data. The overall string includes preprocessing, adaptive clutter filtering (ACF), normalization, detection, feature extraction and classification processing blocks. The ACF is a multi- dimensional adaptive linear FIR filter, optimal in the Least Squares sense, and is applied to low resolution data. It performs simultaneous background clutter suppression and preservation of an average peak target signature (normalized shape of mine highlights -- computed a priori using training set data). After data alignment, using a 3-dimensional ACF enables simultaneous multiple frequency data fusion and clutter suppression in the composite frequency-range- crossrange domain. Following 2-d normalization, the detection consists of thresholding, clustering of exceedances and limiting the number of detections. Finally, features are extracted from high resolution data and a orthogonalization transformation is applied to the features, enabling an efficient application of the optimal log-likelihood-ratio-test (LLRT) classification rule. Various fusion approaches were investigated including pre-detection fusion using the 3-d ACF string, post-detection fusion of the 2d ACF strings and an overall fusion of the two previous strings. The utility of the various processing strings was demonstrated with two new shallow water high resolution sonar imagery data sets. The overall ACF, pre- and post-detection level fusion, feature orthogonalization, LLRT-based classification processing string provided mine classification capability and false alarm rate performance exceeding the one of an expert sonar operator. A wide-sense stationary covariance model was utilized in the ACF algorithm design, significantly reducing the algorithm implementation complexity, thus enabling an easy implementation of the overall processing string in real-time.

Proceedings Article | 4 September 1998 Paper

Adaptive order-statistic filters for sea mine classification

Manuel Fernandez, Tom Aridgides

Proceedings Volume 3392, (1998) https://doi.org/10.1117/12.324197

KEYWORDS: Digital filtering, Linear filtering, Nonlinear filtering, Electronic filtering, Image filtering, Naval mines, Image processing, Signal processing, Data processing, Palladium

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Proceedings Article | 22 July 1997 Paper

Adaptive three-dimensional range-crossrange-frequency filter processing string for sea mine classification in side-scan sonar imagery

Tom Aridgides, Manuel Fernandez, Gerald Dobeck

Proceedings Volume 3079, (1997) https://doi.org/10.1117/12.280847

KEYWORDS: Digital filtering, Image processing, Target detection, Feature extraction, Mining, Filtering (signal processing), Image filtering, Image classification, 3D acquisition, Data processing

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Proceedings Article | 31 May 1996 Paper

Adaptive-filter/feature-orthogonalization processing string for optimal LLRT mine classfication in side-scan sonar imagery

Tom Aridgides, Peter Libera, Manuel Fernandez, Gerald Dobeck

Proceedings Volume 2765, (1996) https://doi.org/10.1117/12.241214

KEYWORDS: Digital filtering, Image processing, Detection and tracking algorithms, Mining, Filtering (signal processing), Data processing, Feature extraction, Target detection, Land mines, Signal processing

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Proceedings Article | 20 June 1995 Paper

Adaptive filter for mine detection and classification in side-scan sonar imagery

Tom Aridgides, Diana Antoni, Manuel Fernandez, Gerald Dobeck

Proceedings Volume 2496, (1995) https://doi.org/10.1117/12.211345

KEYWORDS: Digital filtering, Detection and tracking algorithms, Filtering (signal processing), Image filtering, Optical filters, Land mines, Target detection, Linear filtering, Signal processing, Spatial filters

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Proceedings Article | 1 August 1991 Paper

Adaptive 4-D IR clutter suppression filtering technique

Tom Aridgides, Manuel Fernandez, D. Randolph, David Ferris

Proceedings Volume 1481, (1991) https://doi.org/10.1117/12.45647

KEYWORDS: Digital filtering, Filtering (signal processing), Optical filters, Detection and tracking algorithms, Electronic filtering, Sensors, Target detection, Signal processing, Spatial filters, Infrared imaging

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Proceedings Article | 1 August 1991 Paper

Optimal subpixel-level IR frame-to-frame registration

Manuel Fernandez, Tom Aridgides, D. Randolph, David Ferris

Proceedings Volume 1481, (1991) https://doi.org/10.1117/12.45652

KEYWORDS: Optical filters, Image registration, Signal processing, Data processing, Sensors, Digital filtering, Target detection, Detection and tracking algorithms, Infrared sensors, Infrared imaging

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Proceedings Article | 1 October 1990 Paper

Adaptive three-dimensional spatio-temporal filtering techniques for infrared clutter suppression

A. Aridgides

Proceedings Volume 1305, (1990) https://doi.org/10.1117/12.2321787

KEYWORDS: Digital filtering, Filtering (signal processing), 3D acquisition, Detection and tracking algorithms, Electronic filtering, Spatial filters, Signal processing, Optical filters, Target detection, Algorithm development

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The detection of weak targets with an infrared surveillance system is often complicated not only by a severe clutter environment but also by background and platform motion effects. Conventional sequentially applied algorithms combining frame—to—frame registration, clutter rejection filtering, and adaptive thresholding detection simply overwhelm the track processor in a weak target scenario, due to the required lowering of detector block thresholds. To address this problem, we have developed a 3—D filter/"track—before—detect" signal processing approach in which an adaptive spatio—temporal filter is used for clutter suppression and a Viterbi "track—before—detect" block is used for noncoherent target integration. This paper discusses a 3—D adaptive filtering technique which combines time and spatial filtering (in both azimuth and elevation directions) to achieve simultaneous frame—to-frame registration, background clutter suppression, and target preservation/enhancement. In addition, this 3-D filtering procedure whitens the data, thus greatly facilitating the "track-before—detect" processing block task. Unlike other commonly employed procedures, this technique neither entails the suboptimal sequential application of filtering procedures (e.g., spatial followed by temporal filtering) nor demands very accurate subpixel-level registration or exact knowledge of the target's velocity characteristics. The only requirements are that data frames should be roughly aligned (so the offsets are contained within the filter window) and that the assumption of the moving target indicator (MTI) is valid. In this paper simulation results of the 3—D filtering procedure using real, scanned sensor array data are presented, and the procedure performance and implementation complexity are traded off versus adaptive spatial filtering, adaptive temporal, and sequentially applied time/spatial filtering techniques. Also, modification and simulation results are presented for an extension of the 3—D adaptive spatiotemporal filtering technique, which accommodates both MTI and non-MTI case scenarios.

Proceedings Article | 1 October 1990 Paper

Procrustes algorithm for multisensor track fusion

Manuel Fernandez, Tom Aridgides, John Evans

Proceedings Volume 1306, (1990) https://doi.org/10.1117/12.21621

KEYWORDS: Detection and tracking algorithms, Sensors, Sensor fusion, Matrices, Automatic tracking, Chemical elements, Filtering (signal processing), Data modeling, Target detection, Target recognition

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The association or "fusion" of multiple-sensor reports allows the generation of a highly accurate description of the environment by enabling efficient compression and processing of otherwise unwieldy quantities of data. Assuming that the observations from each sensor are aligned in feature space and in time, this association procedure may be executed on the basis of how well each sensor's vectors of observations match previously fused tracks. Unfortunately, distance-based algorithms alone do not suffice in those situations where match-assignments are not of an obvious nature (e.g., high target density or high false alarm rate scenarios). Our proposed approach is based on recognizing that, together, the sensors' observations and the fused tracks span a vector subspace whose dimensionality and singularity characteristics can be used to determine the total number of targets appearing across sensors. A properly constrained transformation can then be found which aligns the subspaces spanned individually by the observations and by the fused tracks, yielding the relationship existing between both sets of vectors ("Procrustes Problem"). The global nature of this approach thus enables fusing closely-spaced targets by treating them--in a manner analogous to PDA/JPDA algorithms - as clusters across sensors. Since our particular version of the Procrustes Problem consists basically of a minimization in the Total Least Squares sense, the resulting transformations associate both observations-to-tracks and tracks-to--observations. This means that the number of tracks being updated will increase or decrease depending on the number of targets present, automatically initiating or deleting "fused" tracks as required, without the need of ancillary procedures. In addition, it is implicitly assumed that both the tracker filters' target trajectory models and the sensors' observations are "noisy", yielding an algorithm robust even against maneuvering targets. Finally, owing to the fact that Procrustes Association yields the optimal linear associator, the combined sensor and fused track information minimizes tracking Kalman Filter residuals, hence providing very accurate track updates.

Showing 5 of 26 publications

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