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Featured researches published by S.M. Kogon.


international conference on acoustics speech and signal processing | 1999

Clutter mitigation techniques for space-based radar

S.M. Kogon; Daniel J. Rabideau; Richard M. Barnes

The mission of a ground moving target indication (GMTI) radar, as its name implies, is to detect and classify ground-based vehicles, even ones with very low velocities. This type of radar can provide a wide area of coverage and frequent updates of a specific area of interest if the radar is placed on a satellite with a low Earth orbit. However because of the large footprint of the radar on the ground and the high satellite velocity target signals must compete with very strong, nearby clutter. This paper describes how space-time adaptive processing (STAP) can be used for the purposes of clutter rejection in order to perform the GMTI function. In addition, we confront several important issues for a space-based radar such as pulse repetition frequency (PRF) selection, the choice of a STAP algorithm, and the number of spatial channels. These results are quantified in terms of clutter cancellation and angle accuracy.


ieee radar conference | 1999

A signal processing architecture for space-based GMTI radar

Daniel J. Rabideau; S.M. Kogon

Ground moving target indicator (GMTI) radars detect and classify targets with low velocities. Placing such radars in the Earths orbit can provide wide area coverage with high revisit rates. However, because of the radars large footprint (on the ground) and high velocity, target signals must compete with extremely intense nearby clutter. Requirements on antenna aperture, bandwidth, coverage rate, and computational complexity all play significant roles in shaping the radars signal processing chain. This paper describes a signal processing architecture that rejects interference. By addressing issues such as aperture configuration, bandwidth-induced decorrelation, adaptive training, and degree-of-freedom requirements, a multistage space-time adaptive processing (STAP) architecture is constructed.


asilomar conference on signals, systems and computers | 2003

Eigenvectors, diagonal loading and white noise gain constraints for robust adaptive beamforming

S.M. Kogon

In this paper, the use of Initial diagonal loading in addition to a white noise gain constraint (WNGC) is investigated for robust adaptive beamforming. The WNGC controls both the amount of self-nulling loss and the resolving capability. The fundamental problem with WNGC is the setting of the maximum WNG must vary to achieve satisfactory self-nulling protection for different levels of array errors. Since array errors are usually not known precisely, the WNGC is set overly conservative to avoid self-nulling losses causing degraded resolution performance. Initial diagonal loading is a means of preventing self-nulling losses on weak signals and allows the WNGC setting to be much more aggressive, thus improving resolution. The philosophy behind the approach is to use initial diagonal loading to protect weak signals and the WNGC to preserve other stronger signals for which a small amount of self-nulling loss is tolerated. In addition, the performance of this robust ABF approach using WNGC with initial diagonal loading is compared to robust ABF methods that model steering vector uncertainties.


asilomar conference on signals, systems and computers | 2001

STAP adaptive weight training using phase and power selection criteria

S.M. Kogon; M.A. Zatman

This paper addresses the issue of adaptive weight training in space-time adaptive processing (STAP) algorithms for airborne radar in non-homogeneous clutter environments, while avoiding the inclusion of target signals in the training. A common method of ensuring STAP clutter cancellation performance in the presence of strong clutter discretes is to train the STAP adaptive weights using the returns with the largest power for a given Doppler bin. The presumption is that the strongest returns are from the strongest clutter. Many times, however, targets are also present whose inclusion in the STAP weight training results in significant target self-nulling as well as a degradation in clutter mitigation performance. A new STAP training method is presented that excises targets from the training set based on an interferometric measurement of phase for each potential STAP training sample. The resulting training method based on both phase and power selection criteria is shown to offer significant performance gains on experimental data.


asilomar conference on signals, systems and computers | 2012

A generalized likelihood ratio test for SAR CCD

Michael Newey; Gerald R. Benitz; S.M. Kogon

Coherent change detection (CCD) is a powerful technique for detecting minute disturbances in synthetic aperture radar (SAR) imagery. Coherent change detection uses a test statistic to compare, pixel by pixel, two or more SAR images of the same scene. Coherent change detection can detect very small disturbances, not normally visible in SAR or optical imagery, such as footprints or vehicle tracks. The literature describes a number of different detection statistics, the choice which will effect both the contrast of the detected disturbances and the amount of false or uninteresting detections. We present a generalized likelihood ratio test for change detection. Our effort improves upon previous work by incorporating noise in our models, and by optimizing the likelihood parameters separately at each pixel. We compare results from the GLRT with the standard coherence metric on a number of different examples of collected synthetic aperture radar data. The results show that the GLRT provides a useful improvement to the CCD processing.


asilomar conference on signals, systems and computers | 2012

Probabilistic three-pass SAR Coherent Change Detection

Jarred Barber; S.M. Kogon

Coherent Change Detection (CCD) is a powerful technique for detecting fine scene changes between two Synthetic Aperture Radar (SAR) images taken at different times. SAR CCD imagery can detect ground disturbances caused by vehicles or other activities that are invisible in optical or traditional SAR imagery [1]. One problem with the extreme sensitivity of CCD is the presence of false alarms (clutter) introduced by phenomena such as low SNR (esp. radar shadows) and vegetation [2]. This paper proposes a method for combining two CCD images, generated from three SAR passes of the same area, to cancel out false alarm regions and show only changes from man-made activities of interest, such as vehicle tracks.


asilomar conference on signals, systems and computers | 2002

Experimental results for passive sonar arrays with eigenvector-based adaptive beamformers

S.M. Kogon

In this paper, we compare the performance of several eigenvector-based adaptive beamforming (ABF) algorithms on experimental data collected with a passive sonar array. The first eigenvector-based ABF algorithm considered is dominant mode rejection (DMR) with a fixed, bearing independent diagonal loading level. Then, we look at two robust forms of DMR, DMR with a white noise gain constraint and DMR with eigenvector/beam association and excision. All algorithms are applied to experimental data and performance is evaluated in terms of beamformer output power and broadband detection. These ABF algorithms are also compared to conventional non-adaptive beamforming (CBF). The findings show a significant performance improvement for all ABF algorithms over CBF. Furthermore, the results show that less robust, more aggressive ABF achieves detection performance similar to eigenvector/beam association and excision algorithm, while both algorithms slightly outperform the more robust white noise gain constrained DMR.


ieee radar conference | 2013

False alarm mitigation techniques for SAR CCD

Michael Newey; Jarred Barber; Gerald R. Benitz; S.M. Kogon

Coherent Change Detection (CCD) is a powerful technique for detecting fine scene changes between two Synthetic Aperture Radar (SAR) images taken at different times. SAR CCD imagery can detect ground disturbances that are invisible in optical or traditional SAR imagery, such as footprints or vehicle tracks [1]. One particular problem with the extreme sensitivity of CCD is the presence of false alarms (clutter) introduced by phenomena such as low SNR (esp. radar shadows) and vegetation [2]. We present two methods to improve the sensitivity of the detector while reducing the amount of false-alarms. The first uses a generalized likelihood ratio test for change detection which incorporates noise explicitly in its models. The second combines two CCD images, generated from three SAR passes of the same area, to cancel out false alarm regions and show only changes from man-made activities of interest, such as vehicle tracks. We show results from each algorithm on real data, and find that the algorithms are effective at reducing the amount of false alarms while increasing the sensitivity of our detector.


IEE Proceedings - Radar, Sonar and Navigation | 1998

Exploiting coherent multipath for mainbeam jammer suppression

S.M. Kogon; Douglas B. Williams; E.J. Holder


Archive | 2004

Adaptive weight training for post-Doppler STAP algorithms in non-homogeneous clutter

S.M. Kogon

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Daniel J. Rabideau

Massachusetts Institute of Technology

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Douglas B. Williams

Georgia Institute of Technology

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Gerald R. Benitz

Massachusetts Institute of Technology

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Jarred Barber

Massachusetts Institute of Technology

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Michael Newey

Massachusetts Institute of Technology

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E.J. Holder

Georgia Tech Research Institute

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Michael Zatman

Massachusetts Institute of Technology

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Richard M. Barnes

Massachusetts Institute of Technology

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