James Michels - Academia.edu (original) (raw)

Papers by James Michels

Research paper thumbnail of Stochastic resonance: An approach for enhanced medical image processing

2007 IEEE/NIH Life Science Systems and Applications Workshop, 2007

Research paper thumbnail of Noise Enhanced Signal Detection and Estimation

2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers, 2007

ABSTRACT

Research paper thumbnail of Distributed Detection with Data Fusion for Aircraft Flight Control Computer Malfunction Monitoring

AIAA Guidance, Navigation, and Control Conference and Exhibit, 2005

Timely and reliable detection of controller malfunction is a crucial task in all control systems.... more Timely and reliable detection of controller malfunction is a crucial task in all control systems. In flight control, it is even more crucial, since the cost of controller malfunction is potentially very high. Aircraft Flight Control Computers (FCCs) are typically implemented with redundant processing elements in order to mask random independent component failures. However, redundancy alone does not mask the

Research paper thumbnail of Sensor fusion enhancement via optimized stochastic resonance at local sensors

2007 10th International Conference on Information Fusion, 2007

This paper considers the decentralized fusion problem involving local sensor detection as well as... more This paper considers the decentralized fusion problem involving local sensor detection as well as the fusion of decisions transmitted over non-ideal transmission channels in a wireless sensor network. Prime emphasis is given to the enhancement of several fusion rules using a recently developed stochastic resonance methodology applied at the local sensors. Further, it is shown that the optimal form of the stochastic resonance probability mass density for the decentralized sensor fusion problem retains the same form as that previously developed for the single sensor case.

Research paper thumbnail of Improving Nonparametric Detectors via Stochastic Resonance

2006 40th Annual Conference on Information Sciences and Systems, 2006

ABSTRACT

Research paper thumbnail of Can addition of noise improve distributed detection performance?

2006 9th International Conference on Information Fusion, 2006

Stochastic resonance (SR), a nonlinear physical phenomenon in which the performance of some nonli... more Stochastic resonance (SR), a nonlinear physical phenomenon in which the performance of some nonlinear systems can be enhanced by adding suitable noise, has been observed and applied in many areas. However, it has not been shown whether or not this phenomenon plays a role in distributed detection. It seems counterintuitive that adding additional noise to the received decisions at the fusion center can improve detection performance. However, in this paper, we demonstrate the existence of the SR phenomenon in decision fusion by examples. An explanation for its existence is provided.

Research paper thumbnail of Stochastic Resonance In Signal Detection And Human Perception

Research paper thumbnail of A parametric multichannel detection algorithm for correlated non-Gaussian random processes

Radar, IEEE National Conference, 1997

This paper addresses the problem of adaptive multichannel signal detection in additive correlated... more This paper addresses the problem of adaptive multichannel signal detection in additive correlated non-Gaussian noise using a parametric model-based approach. The adaptive signal detection problem has been addressed extensively for the case of additive Gaussian noise. However, the corresponding problem for the non-Gaussian case has received limited attention. The additive non-Gaussian noise is assumed to be modeled by a spherically invariant random process (SIRP). The innovations based detection algorithm for the case of constant signal with unknown complex amplitude is derived. The resulting receiver structure is shown to be equivalent to an adaptive matched filter compared to a data dependent threshold. Performance analysis of the derived receiver for the case of a K-distributed SIRV is presented

Research paper thumbnail of <title>Multichannel detection for correlated non-Gaussian random processes based on innovations</title>

Signal Processing, Sensor Fusion, and Target Recognition II, 1993

Abstmct--This paper addresses the problem of multichannel slgnal detection in additive correlnted... more Abstmct--This paper addresses the problem of multichannel slgnal detection in additive correlnted non-GapssiPn noise using the innovalions approach. Altbougb this problem has been addressed extedvely for the case d eddltrve Gaussian noise, the C O I T T S P Q~ problem for tbe non-Gaussinn case has d v e d limited attention. This is due to tbe fact that there is DO unique sped6cation for tbt joint pmbabUQ dedty function (PDF) of N correlpced non-Gaudm random variables. We overcome thjs problem by u h g the theory of sph&cally invariant mdom procemes (SIRF's) and derive the innOvatiOnS-based detector. It is found that tbe optimal estimators for obtaining the innovations proccrrses are linea and that the redting detector is canodd for the class of PDF's arising from SIRP's. We also present a performance analysis d the innovations-based detector for the case of a K-distributed SIRP. His research interests include radar signal processing, spectrum eStimantion, modeling non-Gaussian interference phenomena, and statistical communication theory. He has authored several journal and conference papers in the areas of his research interests. He presented an invited paper at the

Research paper thumbnail of Theory of the Stochastic Resonance Effect in Signal Detection: Part I-Fixed Detectors

IEEE Transactions on Signal Processing, 2007

This paper develops the mathematical framework to analyze the stochastic resonance (SR) effect in... more This paper develops the mathematical framework to analyze the stochastic resonance (SR) effect in binary hypothesis testing problems. The mechanism for SR noise enhanced signal detection is explored. The detection performance of a noise modified detector is derived in terms of the probability of detection PD and the probability of false alarm PFA. Furthermore, sufficient conditions are established to determine the improvability of a fixed detector using SR. The form of the optimal noise pdf is determined and the optimal stochastic resonance noise pdf which renders the maximum PD without increasing PFA is derived. Finally, an illustrative example is presented where performance comparisons are made between detectors where the optimal stochastic resonance noise, as well as Gaussian, uniform, and optimal symmetric noises are applied to enhance detection performance.

Research paper thumbnail of Robust STAP detection in a dense signal airborne radar environment

Signal Processing, 2004

This paper presents the performance of several space-time adaptive processing (STAP) detection me... more This paper presents the performance of several space-time adaptive processing (STAP) detection methods in a dense signal environment. These include the normalized parametric adaptive matched ÿlter (N-PAMF), the joint domain localized (JDL), and a variant of JDL referred to as the normalized JDL (NJDL). Issues considered here include robust detection with respect to signal contamination of training data and e cient estimation procedures with limited training data. The paper also introduces the innovations power sorting (IPS) pre-processing procedure for representative training data selection. Performance analyses are carried out with measured data from the Multichannel Airborne Radar Measurement (MCARM) program. ?

Research paper thumbnail of Model-based multichannel detection of cardiac conduction abnormalities

Journal of the American College of Cardiology, 1996

Research paper thumbnail of Theory of the Stochastic Resonance Effect in Signal Detection: Part I—Fixed Detectors

IEEE Transactions on Signal Processing, 2000

This paper develops the mathematical framework to analyze the stochastic resonance (SR) effect in... more This paper develops the mathematical framework to analyze the stochastic resonance (SR) effect in binary hypothesis testing problems. The mechanism for SR noise enhanced signal detection is explored. The detection performance of a noise modified detector is derived in terms of the probability of detection D and the probability of false alarm FA . Furthermore, sufficient conditions are established to determine the improvability of a fixed detector using SR. The form of the optimal noise pdf is determined and the optimal stochastic resonance noise pdf which renders the maximum D without increasing FA is derived. Finally, an illustrative example is presented where performance comparisons are made between detectors where the optimal stochastic resonance noise, as well as Gaussian, uniform, and optimal symmetric noises are applied to enhance detection performance.

Research paper thumbnail of Noise Enhanced Nonparametric Detection

IEEE Transactions on Information Theory, 2000

This paper investigates potential improvement of nonparametric detection performance via addition... more This paper investigates potential improvement of nonparametric detection performance via addition of noise and evaluates the performance of noise modified nonparametric detectors. Detection performance comparisons are made between the original detectors and noise modified detectors. Conditions for improvability as well as the optimum additive noise distributions of the widely used sign detector, the Wilcoxon detector, and the dead-zone limiter detector are derived. Finally, a simple and fast learning algorithm to find the optimal noise distribution solely based on received data is presented. A near-optimal solution can be found quickly based on a relatively small dataset.

Research paper thumbnail of Improving Sequential Detection Performance Via Stochastic Resonance

IEEE Signal Processing Letters, 2000

Research paper thumbnail of Performance of STAP Tests in Gaussian and Compound-Gaussian Clutter

Digital Signal Processing, 2000

Michels, James H., Himed, Braham, and Rangaswamy, Muralidhar, Performance of STAP Tests in Gaussi... more Michels, James H., Himed, Braham, and Rangaswamy, Muralidhar, Performance of STAP Tests in Gaussian and Compound-Gaussian Clutter, Digital Signal Processing, 10 (2000), 309–324.The performance of a recently proposed model-based space–time adaptive processing detection method is considered here and compared with several candidate algorithms. Specifically, we consider signal detection in additive disturbance consisting of compound-Gaussian clutter plus Gaussian thermal white noise.

Research paper thumbnail of Performance of Parametric and Covariance Based STAP Tests in Compound-Gaussian Clutter

Digital Signal Processing, 2002

Michels, J. H., Rangaswamy, M., and Himed, B., Performance of Parametric and Covariance Based STA... more Michels, J. H., Rangaswamy, M., and Himed, B., Performance of Parametric and Covariance Based STAP Tests in Compound-Gaussian Clutter, Digital Signal Processing12 (2002) 307–328The performance of a parametric space–time adaptive processing method is presented here. Specifically, we consider signal detection in additive disturbance containing compound-Gaussian clutter plus additive Gaussian thermal white noise. Performance is compared to the normalized adaptive matched

Research paper thumbnail of A GLRT for multichannel radar detection in the presence of both compound Gaussian clutter and additive white Gaussian noise

Digital Signal Processing, 2005

Motivated by multichannel radar detection applications in the presence of both white Gaussian noi... more Motivated by multichannel radar detection applications in the presence of both white Gaussian noise and Gaussian clutter with unknown power, we develop maximum likelihood parameter estimates for the disturbance process. Both cases with known and unknown white noise variance are treated. As the estimators do not admit closed-form solutions, numerical iterative procedures are developed that are guaranteed to at least converge to the local maximum. The developed estimates allow us to construct a generalized likelihood ratio test (GLRT) for the detection of a signal with constant but unknown amplitude. This GLRT has important applications in multichannel radar detection involving both white Gaussian noise and spherically invariant random process clutter and is shown to have better detection performance and CFAR property compared with existing statistics.  2005 Elsevier Inc. All rights reserved.

Research paper thumbnail of Statistical analysis of the non-homogeneity detector for STAP applications

Digital Signal Processing, 2004

The public reporting burden for this collection of information is estimated to average 1 hour per... more The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

Research paper thumbnail of Stochastic resonance: An approach for enhanced medical image processing

2007 IEEE/NIH Life Science Systems and Applications Workshop, 2007

Research paper thumbnail of Noise Enhanced Signal Detection and Estimation

2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers, 2007

ABSTRACT

Research paper thumbnail of Distributed Detection with Data Fusion for Aircraft Flight Control Computer Malfunction Monitoring

AIAA Guidance, Navigation, and Control Conference and Exhibit, 2005

Timely and reliable detection of controller malfunction is a crucial task in all control systems.... more Timely and reliable detection of controller malfunction is a crucial task in all control systems. In flight control, it is even more crucial, since the cost of controller malfunction is potentially very high. Aircraft Flight Control Computers (FCCs) are typically implemented with redundant processing elements in order to mask random independent component failures. However, redundancy alone does not mask the

Research paper thumbnail of Sensor fusion enhancement via optimized stochastic resonance at local sensors

2007 10th International Conference on Information Fusion, 2007

This paper considers the decentralized fusion problem involving local sensor detection as well as... more This paper considers the decentralized fusion problem involving local sensor detection as well as the fusion of decisions transmitted over non-ideal transmission channels in a wireless sensor network. Prime emphasis is given to the enhancement of several fusion rules using a recently developed stochastic resonance methodology applied at the local sensors. Further, it is shown that the optimal form of the stochastic resonance probability mass density for the decentralized sensor fusion problem retains the same form as that previously developed for the single sensor case.

Research paper thumbnail of Improving Nonparametric Detectors via Stochastic Resonance

2006 40th Annual Conference on Information Sciences and Systems, 2006

ABSTRACT

Research paper thumbnail of Can addition of noise improve distributed detection performance?

2006 9th International Conference on Information Fusion, 2006

Stochastic resonance (SR), a nonlinear physical phenomenon in which the performance of some nonli... more Stochastic resonance (SR), a nonlinear physical phenomenon in which the performance of some nonlinear systems can be enhanced by adding suitable noise, has been observed and applied in many areas. However, it has not been shown whether or not this phenomenon plays a role in distributed detection. It seems counterintuitive that adding additional noise to the received decisions at the fusion center can improve detection performance. However, in this paper, we demonstrate the existence of the SR phenomenon in decision fusion by examples. An explanation for its existence is provided.

Research paper thumbnail of Stochastic Resonance In Signal Detection And Human Perception

Research paper thumbnail of A parametric multichannel detection algorithm for correlated non-Gaussian random processes

Radar, IEEE National Conference, 1997

This paper addresses the problem of adaptive multichannel signal detection in additive correlated... more This paper addresses the problem of adaptive multichannel signal detection in additive correlated non-Gaussian noise using a parametric model-based approach. The adaptive signal detection problem has been addressed extensively for the case of additive Gaussian noise. However, the corresponding problem for the non-Gaussian case has received limited attention. The additive non-Gaussian noise is assumed to be modeled by a spherically invariant random process (SIRP). The innovations based detection algorithm for the case of constant signal with unknown complex amplitude is derived. The resulting receiver structure is shown to be equivalent to an adaptive matched filter compared to a data dependent threshold. Performance analysis of the derived receiver for the case of a K-distributed SIRV is presented

Research paper thumbnail of <title>Multichannel detection for correlated non-Gaussian random processes based on innovations</title>

Signal Processing, Sensor Fusion, and Target Recognition II, 1993

Abstmct--This paper addresses the problem of multichannel slgnal detection in additive correlnted... more Abstmct--This paper addresses the problem of multichannel slgnal detection in additive correlnted non-GapssiPn noise using the innovalions approach. Altbougb this problem has been addressed extedvely for the case d eddltrve Gaussian noise, the C O I T T S P Q~ problem for tbe non-Gaussinn case has d v e d limited attention. This is due to tbe fact that there is DO unique sped6cation for tbt joint pmbabUQ dedty function (PDF) of N correlpced non-Gaudm random variables. We overcome thjs problem by u h g the theory of sph&cally invariant mdom procemes (SIRF's) and derive the innOvatiOnS-based detector. It is found that tbe optimal estimators for obtaining the innovations proccrrses are linea and that the redting detector is canodd for the class of PDF's arising from SIRP's. We also present a performance analysis d the innovations-based detector for the case of a K-distributed SIRP. His research interests include radar signal processing, spectrum eStimantion, modeling non-Gaussian interference phenomena, and statistical communication theory. He has authored several journal and conference papers in the areas of his research interests. He presented an invited paper at the

Research paper thumbnail of Theory of the Stochastic Resonance Effect in Signal Detection: Part I-Fixed Detectors

IEEE Transactions on Signal Processing, 2007

This paper develops the mathematical framework to analyze the stochastic resonance (SR) effect in... more This paper develops the mathematical framework to analyze the stochastic resonance (SR) effect in binary hypothesis testing problems. The mechanism for SR noise enhanced signal detection is explored. The detection performance of a noise modified detector is derived in terms of the probability of detection PD and the probability of false alarm PFA. Furthermore, sufficient conditions are established to determine the improvability of a fixed detector using SR. The form of the optimal noise pdf is determined and the optimal stochastic resonance noise pdf which renders the maximum PD without increasing PFA is derived. Finally, an illustrative example is presented where performance comparisons are made between detectors where the optimal stochastic resonance noise, as well as Gaussian, uniform, and optimal symmetric noises are applied to enhance detection performance.

Research paper thumbnail of Robust STAP detection in a dense signal airborne radar environment

Signal Processing, 2004

This paper presents the performance of several space-time adaptive processing (STAP) detection me... more This paper presents the performance of several space-time adaptive processing (STAP) detection methods in a dense signal environment. These include the normalized parametric adaptive matched ÿlter (N-PAMF), the joint domain localized (JDL), and a variant of JDL referred to as the normalized JDL (NJDL). Issues considered here include robust detection with respect to signal contamination of training data and e cient estimation procedures with limited training data. The paper also introduces the innovations power sorting (IPS) pre-processing procedure for representative training data selection. Performance analyses are carried out with measured data from the Multichannel Airborne Radar Measurement (MCARM) program. ?

Research paper thumbnail of Model-based multichannel detection of cardiac conduction abnormalities

Journal of the American College of Cardiology, 1996

Research paper thumbnail of Theory of the Stochastic Resonance Effect in Signal Detection: Part I—Fixed Detectors

IEEE Transactions on Signal Processing, 2000

This paper develops the mathematical framework to analyze the stochastic resonance (SR) effect in... more This paper develops the mathematical framework to analyze the stochastic resonance (SR) effect in binary hypothesis testing problems. The mechanism for SR noise enhanced signal detection is explored. The detection performance of a noise modified detector is derived in terms of the probability of detection D and the probability of false alarm FA . Furthermore, sufficient conditions are established to determine the improvability of a fixed detector using SR. The form of the optimal noise pdf is determined and the optimal stochastic resonance noise pdf which renders the maximum D without increasing FA is derived. Finally, an illustrative example is presented where performance comparisons are made between detectors where the optimal stochastic resonance noise, as well as Gaussian, uniform, and optimal symmetric noises are applied to enhance detection performance.

Research paper thumbnail of Noise Enhanced Nonparametric Detection

IEEE Transactions on Information Theory, 2000

This paper investigates potential improvement of nonparametric detection performance via addition... more This paper investigates potential improvement of nonparametric detection performance via addition of noise and evaluates the performance of noise modified nonparametric detectors. Detection performance comparisons are made between the original detectors and noise modified detectors. Conditions for improvability as well as the optimum additive noise distributions of the widely used sign detector, the Wilcoxon detector, and the dead-zone limiter detector are derived. Finally, a simple and fast learning algorithm to find the optimal noise distribution solely based on received data is presented. A near-optimal solution can be found quickly based on a relatively small dataset.

Research paper thumbnail of Improving Sequential Detection Performance Via Stochastic Resonance

IEEE Signal Processing Letters, 2000

Research paper thumbnail of Performance of STAP Tests in Gaussian and Compound-Gaussian Clutter

Digital Signal Processing, 2000

Michels, James H., Himed, Braham, and Rangaswamy, Muralidhar, Performance of STAP Tests in Gaussi... more Michels, James H., Himed, Braham, and Rangaswamy, Muralidhar, Performance of STAP Tests in Gaussian and Compound-Gaussian Clutter, Digital Signal Processing, 10 (2000), 309–324.The performance of a recently proposed model-based space–time adaptive processing detection method is considered here and compared with several candidate algorithms. Specifically, we consider signal detection in additive disturbance consisting of compound-Gaussian clutter plus Gaussian thermal white noise.

Research paper thumbnail of Performance of Parametric and Covariance Based STAP Tests in Compound-Gaussian Clutter

Digital Signal Processing, 2002

Michels, J. H., Rangaswamy, M., and Himed, B., Performance of Parametric and Covariance Based STA... more Michels, J. H., Rangaswamy, M., and Himed, B., Performance of Parametric and Covariance Based STAP Tests in Compound-Gaussian Clutter, Digital Signal Processing12 (2002) 307–328The performance of a parametric space–time adaptive processing method is presented here. Specifically, we consider signal detection in additive disturbance containing compound-Gaussian clutter plus additive Gaussian thermal white noise. Performance is compared to the normalized adaptive matched

Research paper thumbnail of A GLRT for multichannel radar detection in the presence of both compound Gaussian clutter and additive white Gaussian noise

Digital Signal Processing, 2005

Motivated by multichannel radar detection applications in the presence of both white Gaussian noi... more Motivated by multichannel radar detection applications in the presence of both white Gaussian noise and Gaussian clutter with unknown power, we develop maximum likelihood parameter estimates for the disturbance process. Both cases with known and unknown white noise variance are treated. As the estimators do not admit closed-form solutions, numerical iterative procedures are developed that are guaranteed to at least converge to the local maximum. The developed estimates allow us to construct a generalized likelihood ratio test (GLRT) for the detection of a signal with constant but unknown amplitude. This GLRT has important applications in multichannel radar detection involving both white Gaussian noise and spherically invariant random process clutter and is shown to have better detection performance and CFAR property compared with existing statistics.  2005 Elsevier Inc. All rights reserved.

Research paper thumbnail of Statistical analysis of the non-homogeneity detector for STAP applications

Digital Signal Processing, 2004

The public reporting burden for this collection of information is estimated to average 1 hour per... more The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.