AFIT Faculty Directory
Maybeck, Peter , Professor Emeritus of Electrical Engineering
Dept of Electrical and Computer Engineering
Dept of Electrical and Computer Engineering
Email: Peter Maybeck
Education:
PhD, 1972 - Aeronautics and Astronautics - Massachusetts Institute of Technology BS, 1968 - Aeronautics and Astronautics - Massachusetts Institute of Technology
PhD, 1972 - Aeronautics and Astronautics - Massachusetts Institute of Technology BS, 1968 - Aeronautics and Astronautics - Massachusetts Institute of Technology
Research Interests:
Prof. Maybeck is a leading researcher, teacher and practitioner in modern control theory in general, and specifically in multiple model adaptive estimation and control theory and practice. His work includes original contributions of multiple model theory to the following applications: (1) high energy laser tracking, adapting to both appropriate assumed target dynamics model (benign vs. harshly maneuvering) and to choice of field-of-view more rapidly and accurately than any other adaptive algorithm (maintaining lock on target when all other algorithms broke lock), and discerning the hard body of a missile as separated from its exhaust plume; (2) adaptive control of large flexible spacecraft to allow for rapid pointing and efficient quelling of bending oscillations; (3) sensor/actuator failure detection and controller reconfiguration for survivable flight controller design (used as a front-end to the full VISTA F-16 flight control system to demonstrate realistic capabilities, and implemented and flown on the Lambda URV; correctly and rapidly detecting all failures in over six thousand simulations; new "hierarchical multiple model algorithm" allowing detection of multiple failures with no more computational load than for single failures); (4) detection and compensation for jamming, spoofing, and failures of a GPS-aided inertial navigation system; (5) adaptive prediction applied to virtual environment flight simulators to remove the effect of computational and scene-rendering delay time; and (6) tracking of maneuvering targets in clutter. Adaptive Filtering and Control by Multiple Model Methods Adaptive filter and control algorithms can be designed by Multiple Model Adaptive Estimation and Control (MMAE and MMAC) techniques. They have the structure of a bank of parallel Kalman filters or parallel LQG (Linear sytem, Quadratic cost criterion, Gaussian noise models) controllers that are used to form a probability-weighted average state estimate or control output. Each elemental filter or controller is based upon an assumed value of the uncertain parameter to which adaptation is to occur, and the characteristics of the residuals from each of these filters allow the evaluation of the probability that each elemental filter or controller is in fact based upon the best assumed value for the parameter. These algorithms are ideally suited to distributed computation and have some performance attributes that are far superior to those algorithms without the multiple model structure. One application of multiple model adaptive algorithms is the high energy laser pointing and tracking problem using forward looking infrared (FLIR) sensor measurements. Here, multiple-hot-spot targets can undergo substantial changes in trajectory characteristics. The estimation and control algorithm adapts to both appropriate assumed target dynamics model (benign vs. harshly maneuvering) and to choice of field-of-view size and shape. Similar techniques have also been applied to the problem of discerning the hard body of a missile, as separated from a dynamically changing exhaust plume. In 1991, Doppler information as well as speckle information in the return of a low-power laser pointed at the target were used to detect the hardbody/plume interface. [Herrera, MS in Dec. 1991; Ching, MS in Dec. 1992; Grondin, MS in Dec. 1993] Another application area is the adaptive control of large flexible spacecraft to allow for rapid pointing and efficient quelling of bending oscillations. For this case, the use of a "moving-bank" multiple model estimation and/or control algorithm has been investigated. Rather than implementing parallel filters or controllers based upon all viable discrete parameter values for all time, only a subset of these discrete values is used at any one time. There is a dynamic reallocation logic that decides which subset of values should currently be implemented, and ongoing research is portraying the performance capabilities of various decision logics for "moving" the bank of currently used values around parameter space. Optimizing the means of discretizing the parameter space upon which the multiple model adaptive estimators and controllers are based will enhance the performance of such algorithms as well. Current research has also addressed robustness issues: how well does the adaptation work in the face of unmodeled effects in the real world? Recently, this research focused on the control of the SPICE (SPace Integrated Control Experiment) space vehicle, with an original "truth model" of more than 200 states. [Gustafson, MS in Dec. 1991 - our department's nominee for the Commandant's Award for outstanding research; Fitch, MS in Dec. 1993; Schiller, MS in Dec. 1993; Griffin, MS in Dec 1994] Multiple model algorithms have also been applied to sensor/actuator failure detection and controller reconfiguration for survivable flight controller design. Each elemental filter/controller combination is designed for a specific assumed failure status of the aircraft: no failures, a particular sensor has failed, a particular actuator has failed, etc. Thus, the multiple model adaptive algorithm produces a blending of the elemental controllers, and it affords the detection of failures and the desired reconfiguration of the control system to provide excellent performance in the face of such failures. Current work is demonstrating performance that far exceeds capabilities of other reconfigurable flight control systems. Research concentrated on applying these methods to the VISTA F-16 aircraft, with both longitudinal and lateral-directional channels being considered. The multiple model algorithm was used as a front-end to the full VISTA F-16 flight control system to demonstrate realistic capabilities, rather than just generate "paper studies" that are typically deemed of academic interest but not realistic enough to be represesentative of actual implementable flight control systems. This research has been so successful, that the current research is aimed at implementation and actual flight test of such an algorithm on the Lambda URV and the SRF simulator at Wright Laboratory. [Menke, MS in June 1992; Hanlon, MS in Dec. 1992; Lane, MS in Dec. 1993; Eide, MS in Dec 1994; Stepaniak, MS in Dec 1995; Hanlon, PhD in Sep 1996; Lewis, MS in Dec 1996; Clark, MS in Dec 1997; Fisher, MS in Mar 1999] Recently, multiple model adaptive predictors have been applied to virtual environment flight simulators to remove the effect of computational and scene-rendering delay time. Current implementations of virtual environment flight simulators suffer in realism and training usefulness because of such delay, and the adaptive filters developed in this research specifically predict the angular orientation of the user's head a period of time in the future, so that the scene can be rendered appropriately by the time the user actually looks in that direction. [Russell, MS in Dec 1994; Kyger, MS in Dec 1995] Another application of multiple model adaptive estimation is the detection of jamming, spoofing, and failures of a GPS-aided inertial navigation system. Initial investigations have shown that multiple model algorithms have a greater capacity to detection such phenomena than other proposed algorithms, thereby allowing successful mission accomplishment despite aggressor attempts to incapacitate the guidance and navigation systems of our weapon systems. [Gray, MS in Dec 1994; Britton, MS in Dec 1995; White, MS in Dec 1995; Miller, PhD in Mar 1998; Vasquez, PhD in Jun 1998; Vanek, MS in Mar 1999; Erickson, PhD in Oct 2001; Larson, MS in Mar 2003; Gutierrez, MS in Mar 2004; Meidel, MS in Mar 2005] Mulitple model adaptive estimation and Gaussian mixture reduction techniques have been combined to yield significant performance improvement in the tracking of maneuvering targets in clutter The problem of tracking targets in clutter naturally leads to a Gaussian mixture representation of the probability density function of the target state vector. AFIT research has revealed that a tracker using an integral square error (ISE) based mixture reduction algorithm can provide tracking performance in clutter which is significantly better than any other known techniques using similar numbers of mixture components. One useful algorithm architecture for targets exhibiting very different trajectory characteristics over time, as due to maneuver initiation, would replace each Kalman filter within a conventional MMAE or IMM with an ISE-based algorithm that assumes the adequacy of the same particular dynamics model and discretized parameter choice ( mode ). Current research is also focusing on an ISE algorithm of MMAEs, rather than an MMAE of ISE algorithms, in order to address the important gating issue for such a tracker, and the performance improvement is substantial. [Williams, MS in Mar 2003; Smith, MS in Mar 2005; Petrucci, MS in Dec 2005; Kozak, MS in Mar 2006.]
Prof. Maybeck is a leading researcher, teacher and practitioner in modern control theory in general, and specifically in multiple model adaptive estimation and control theory and practice. His work includes original contributions of multiple model theory to the following applications: (1) high energy laser tracking, adapting to both appropriate assumed target dynamics model (benign vs. harshly maneuvering) and to choice of field-of-view more rapidly and accurately than any other adaptive algorithm (maintaining lock on target when all other algorithms broke lock), and discerning the hard body of a missile as separated from its exhaust plume; (2) adaptive control of large flexible spacecraft to allow for rapid pointing and efficient quelling of bending oscillations; (3) sensor/actuator failure detection and controller reconfiguration for survivable flight controller design (used as a front-end to the full VISTA F-16 flight control system to demonstrate realistic capabilities, and implemented and flown on the Lambda URV; correctly and rapidly detecting all failures in over six thousand simulations; new "hierarchical multiple model algorithm" allowing detection of multiple failures with no more computational load than for single failures); (4) detection and compensation for jamming, spoofing, and failures of a GPS-aided inertial navigation system; (5) adaptive prediction applied to virtual environment flight simulators to remove the effect of computational and scene-rendering delay time; and (6) tracking of maneuvering targets in clutter. Adaptive Filtering and Control by Multiple Model Methods Adaptive filter and control algorithms can be designed by Multiple Model Adaptive Estimation and Control (MMAE and MMAC) techniques. They have the structure of a bank of parallel Kalman filters or parallel LQG (Linear sytem, Quadratic cost criterion, Gaussian noise models) controllers that are used to form a probability-weighted average state estimate or control output. Each elemental filter or controller is based upon an assumed value of the uncertain parameter to which adaptation is to occur, and the characteristics of the residuals from each of these filters allow the evaluation of the probability that each elemental filter or controller is in fact based upon the best assumed value for the parameter. These algorithms are ideally suited to distributed computation and have some performance attributes that are far superior to those algorithms without the multiple model structure. One application of multiple model adaptive algorithms is the high energy laser pointing and tracking problem using forward looking infrared (FLIR) sensor measurements. Here, multiple-hot-spot targets can undergo substantial changes in trajectory characteristics. The estimation and control algorithm adapts to both appropriate assumed target dynamics model (benign vs. harshly maneuvering) and to choice of field-of-view size and shape. Similar techniques have also been applied to the problem of discerning the hard body of a missile, as separated from a dynamically changing exhaust plume. In 1991, Doppler information as well as speckle information in the return of a low-power laser pointed at the target were used to detect the hardbody/plume interface. [Herrera, MS in Dec. 1991; Ching, MS in Dec. 1992; Grondin, MS in Dec. 1993] Another application area is the adaptive control of large flexible spacecraft to allow for rapid pointing and efficient quelling of bending oscillations. For this case, the use of a "moving-bank" multiple model estimation and/or control algorithm has been investigated. Rather than implementing parallel filters or controllers based upon all viable discrete parameter values for all time, only a subset of these discrete values is used at any one time. There is a dynamic reallocation logic that decides which subset of values should currently be implemented, and ongoing research is portraying the performance capabilities of various decision logics for "moving" the bank of currently used values around parameter space. Optimizing the means of discretizing the parameter space upon which the multiple model adaptive estimators and controllers are based will enhance the performance of such algorithms as well. Current research has also addressed robustness issues: how well does the adaptation work in the face of unmodeled effects in the real world? Recently, this research focused on the control of the SPICE (SPace Integrated Control Experiment) space vehicle, with an original "truth model" of more than 200 states. [Gustafson, MS in Dec. 1991 - our department's nominee for the Commandant's Award for outstanding research; Fitch, MS in Dec. 1993; Schiller, MS in Dec. 1993; Griffin, MS in Dec 1994] Multiple model algorithms have also been applied to sensor/actuator failure detection and controller reconfiguration for survivable flight controller design. Each elemental filter/controller combination is designed for a specific assumed failure status of the aircraft: no failures, a particular sensor has failed, a particular actuator has failed, etc. Thus, the multiple model adaptive algorithm produces a blending of the elemental controllers, and it affords the detection of failures and the desired reconfiguration of the control system to provide excellent performance in the face of such failures. Current work is demonstrating performance that far exceeds capabilities of other reconfigurable flight control systems. Research concentrated on applying these methods to the VISTA F-16 aircraft, with both longitudinal and lateral-directional channels being considered. The multiple model algorithm was used as a front-end to the full VISTA F-16 flight control system to demonstrate realistic capabilities, rather than just generate "paper studies" that are typically deemed of academic interest but not realistic enough to be represesentative of actual implementable flight control systems. This research has been so successful, that the current research is aimed at implementation and actual flight test of such an algorithm on the Lambda URV and the SRF simulator at Wright Laboratory. [Menke, MS in June 1992; Hanlon, MS in Dec. 1992; Lane, MS in Dec. 1993; Eide, MS in Dec 1994; Stepaniak, MS in Dec 1995; Hanlon, PhD in Sep 1996; Lewis, MS in Dec 1996; Clark, MS in Dec 1997; Fisher, MS in Mar 1999] Recently, multiple model adaptive predictors have been applied to virtual environment flight simulators to remove the effect of computational and scene-rendering delay time. Current implementations of virtual environment flight simulators suffer in realism and training usefulness because of such delay, and the adaptive filters developed in this research specifically predict the angular orientation of the user's head a period of time in the future, so that the scene can be rendered appropriately by the time the user actually looks in that direction. [Russell, MS in Dec 1994; Kyger, MS in Dec 1995] Another application of multiple model adaptive estimation is the detection of jamming, spoofing, and failures of a GPS-aided inertial navigation system. Initial investigations have shown that multiple model algorithms have a greater capacity to detection such phenomena than other proposed algorithms, thereby allowing successful mission accomplishment despite aggressor attempts to incapacitate the guidance and navigation systems of our weapon systems. [Gray, MS in Dec 1994; Britton, MS in Dec 1995; White, MS in Dec 1995; Miller, PhD in Mar 1998; Vasquez, PhD in Jun 1998; Vanek, MS in Mar 1999; Erickson, PhD in Oct 2001; Larson, MS in Mar 2003; Gutierrez, MS in Mar 2004; Meidel, MS in Mar 2005] Mulitple model adaptive estimation and Gaussian mixture reduction techniques have been combined to yield significant performance improvement in the tracking of maneuvering targets in clutter The problem of tracking targets in clutter naturally leads to a Gaussian mixture representation of the probability density function of the target state vector. AFIT research has revealed that a tracker using an integral square error (ISE) based mixture reduction algorithm can provide tracking performance in clutter which is significantly better than any other known techniques using similar numbers of mixture components. One useful algorithm architecture for targets exhibiting very different trajectory characteristics over time, as due to maneuver initiation, would replace each Kalman filter within a conventional MMAE or IMM with an ISE-based algorithm that assumes the adequacy of the same particular dynamics model and discretized parameter choice ( mode ). Current research is also focusing on an ISE algorithm of MMAEs, rather than an MMAE of ISE algorithms, in order to address the important gating issue for such a tracker, and the performance improvement is substantial. [Williams, MS in Mar 2003; Smith, MS in Mar 2005; Petrucci, MS in Dec 2005; Kozak, MS in Mar 2006.]
Expertise:
GPS Signal Jamming Mitigation through Multiple Model Adaptive Estimation applied to Ultra-Tightly Coupled GPS/INS Architecture, distribution authorized to US Government agencies and their contractors (Critical Technology), by K. W. Meidel, P. S. Maybeck, and J. F. Raquet, Proceedings of the I.O.N. GNSS 2005 Conference, Long Beach, California, September 2005. Multiple Model Tracker Based on Gaussian Mixture Reduction for Maneuvering Targets in Clutter, by P. S. Maybeck and B. D. Smith, Proceedings of the 8th International Conference on Information Fusion, International Society of Information Fusion, Philadelphia, Pennsylvania, July 2005. Multipath-Adaptive GPS/INS Receiver, by J. W. Erickson, P. S. Maybeck, and J. F. Raquet, IEEE Transactions on Aerospace and Electronic Systems, Vol. 41, No. 4, pp. 645-657, April 2005. Cost-Function-Based Hypothesis Control Techniques for Multiple Hypothesis Tracking, byJ. L. Williams and P. S. Maybeck, Mathematical and Computer Modelling, Special Issue on Optimization and Control for Military Applications, 2005. A New Generalized Residual Multiple Model Adaptive Estimator of Parameters and States, byC. D. Ormsby, J. F. Raquet, and P. S. Maybeck, Mathematical and Computer Modelling, Special Issue on Optimization and Control for Military Applications, 2005. Enhanced Motion and Sizing of Bank in Moving-Bank MMAE, by J. R. Vasquez and P. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. 40, No. 3, pp. 770-779, July 2004. Multipath and GPS Signal Jamming Mitigation through Multiple Model Adaptive Estimation Applied to Ultra-Tightly Coupled GPS/INS Architecture, distribution authorized to US Government agencies and their contractors (Critical Technology), by J. R. Gutierrez, P. S. Maybeck, and J. F. Raquet, Proceedings of the 60th Annual Meeting of the I.O.N., Dayton, Ohio, June 2004. Cost-Function-Based Hypothesis Control Techniques for Multiple Hypothesis Tracking, byJ. L. Williams and P. S. Maybeck, Proceedings of the SPIE, Vol. 5428, pp. 167-179, SPIE Annual International Defense and Security Symposium, Orlando, Florida, April 2004. The Use of Optimal Smoothing and Nonlinear Filtering in Pseudolite-Based Flight Reference System, by T. J. Bouska, J. F. Raquet, and P. S. Maybeck, Proceedings of the 59th Annual Meeting of the I.O.N., Albuquerque, New Mexico, June 2003. A Generalized Multiple Model Adaptive Estimator for Parameter and State Estimation, byC. D. Ormsby, J. F. Raquet, and P. S. Maybeck, Proceedings of the 59th Annual Meeting of the I.O.N., Albuquerque, New Mexico, June 2003. A Jamming-Adaptive, Ultra-Tightly Coupled GPS/INS Receiver, distribution authorized to US Government agencies and their contractors (Critical Technology), by C. D. Larson, P. S. Maybeck, andJ. F. Raquet, accepted for publication in the Proceedings of the Classified Session at the 59th Annual Meeting of the I.O.N., Albuquerque, New Mexico, June 2003. Cost-Function-Based Gaussian Mixture Reduction for Target Tracking, by J. L. Williams and P. S. Maybeck, Proceedings of the 6th International Conference on Information Fusion, pp.1047-1054, International Society of Information Fusion, Cairns, Australia, July 2003. A Multiple Filter Approach for Precise Kinematic DGPS Positioning and Carrier-Phase Ambiguity Resolution, P. E. Henderson, J. F. Raquet, and P. S. Maybeck, NAVIGATION Journal of the Institute of Navigation, Vol. 49, No. 3, pp. 149-160, Fall 2002. A Novel Electrocardiogram Segmentation Algorithm Using a Multiple Model Adaptive Estimator, by G. Hoffman, M. M. Miller, M. Kabrisky, P. S. Maybeck, and J. F. Raquet, Proceedings of the IEEE Conference on Decision and Control, Las Vegas, Nevada, pp. 2524-2529, December 2002. Multiple Model Adaptive Estimation with Filter Spawning, by K. A. Fisher and P. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. 38, No. 3, pp. 755-768, July 2002. Target Recognition Using Late Time Scattering, by K. J. Pascoe, W. D. Wood, P. S. Maybeck, and A. W. Wood, Proceedings of the Progress In Electromagnetics Research Symposium (PIERS 2002), Cambridge, Massachusetts, July 2002. A Multipath-Adaptive, Ultra-Tightly Coupled GPS/INS Receiver, distribution authorized to US Government agencies and their contractors (Critical Technology), by J. W. Erickson, P. S. Maybeck, and J. F. Raquet, Proceedings of the Classified Session at the 58th Annual Meeting of the I.O.N., Albuquerque, New Mexico, June 2002. Target Recognition Via Late Time Scattering, by K. J. Pascoe, W. D. Wood, P. S. Maybeck, and A. W. Wood, Proceedings of the 6th Ultra-Wideband Short-Pulse Electromagnetics Conference of the AMEREM 2002 Symposium, Annapolis Maryland, June 2002; published by Kluwer Academic/Plenum, 2002. Stochastic Models, Estimation and Control, Vol. 3, by P. S. Maybeck, Navtech Press, Arlington, Virginia, 2001 (republished). Generalized LQG Design by Filter and Controller Model Selection, by T. E. Brehm and P. S. Maybeck, Proceedings of the 2000 IEEE Conference on Decision and Control, Sydney, Australia, pp. 2700-2705, Dec. 2000. Multiple Model Adaptive Estimation with Filter Spawning, by K. A. Fisher and P. S. Maybeck, Proceedings of the 2000 American Control Conference, Chicago, Illinois, pp. 2326-2331, June 2000. Multiple-Model Adaptive Estimation Using Residual Correlation Kalman Filter Bank, by P. D. Hanlon and P. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. 36, No. 2, pp. 393-406, April 2000. Characterization of Kalman Filter Residuals in the Presence of Mismodeling, by P. D. Hanlon and P. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. 36, No. 1, pp. 114-131, January 2000. Multiple Model Adaptive Algorithms for Detecting and Compensating Sensor/Actuator Failures in Aircraft Flight Control Systems, by P. S. Maybeck, International Journal of Robust and Nonlinear Control, Special Issue on Reconfigurable Flight Control, Vol. 9, pp. 1051-1070, 1999. Density Algorithm Based Moving-Bank MMAE, by J. R. Vasquez and P. S. Maybeck, Proceedings of the 1999 Conference on Decision and Control, Phoenix, Arizona, pp. 4117-4122, December 1999. GPS Signal Offset Detection and Noise Strength Estimation in a Parallel Kalman Filter Algorithm, by B. J. Vanek, P. S. Maybeck, and J. F. Raquet, Proceedings of the 1999 ION GPS-99 Conference, Nashville, Tennessee, pp. 2243-2252, September 1999. Enhanced Motion and Sizing of Bank in Moving-Bank MMAE, by J. R. Vasquez and P. S. Maybeck, Proceedings of the 1999 American Control Conference, San Diego, California, pp. 1555-1562, June 1999. Characterization of Kalman Filter Residuals in the Presence of Mismodeling, by P. D. Hanlon and P. S. Maybeck, Proceedings of the 1998 Conference on Decision and Control, Tampa, Florida, pp. 1254-1259, December 1998. Multiple-Model Adaptive Estimation Using Residual Correlation Kalman Filter Bank, by P. D. Hanlon and P. S. Maybeck, Proceedings of the 1998 Conference on Decision and Control, Tampa, Florida, pp. 4494-4495, December 1998. MMAE-Based Control Redistribution Applied to the VISTA F-16, by M. J. Stepaniak and P. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. 34, No. 4, pp. 1249-1260, October 1998. Detection of Interference/Jamming and Spoofing in a DGPS-Aided Inertial System, by N. A. White, P. S. Maybeck, and S. L. DeVilbiss, IEEE Transactions on Aerospace and Electronic Systems, Vol. 34, No. 4, pp. 1208-1217, October 1998. Reducing Lag in Virtual Displays Using Multiple Model Adaptive Estimation, by D. W. Kyger and P. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. 34, No. 4, pp. 1237-1248, October 1998. Modified Multiple Model Adaptive Estimation (M3AE) for Accurate Navigation and Detection of Electromagnetic Interference to the GPS Signal, by M. M. Miller and P. S. Maybeck, Proceedings of the 1998 ION GPS-98 Conference, Nashville, Tennessee, pp. 895-904, September 1998. MMAE Detection of Interference/Jamming and Spoofing in a DGPS-Aided Inertial System, by N. A. White, P. S. Maybeck, and S. L. DeVilbiss, Proceedings of the 1998 ION GPS-98 Conference, Nashville, Tennessee, pp. 905-914, September 1998. Interrelationship of Single-Filter and Multiple-Model Adaptive Algorithms, by P. D. Hanlon and P. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. 34, No. 3, pp. 934-946, July 1998. Equivalent Kalman Filter Structure for Multiple Model Adaptive Estimation (MMAE) and Generalized Likelihood Ratio (GLR) Failure Detection, by P. D. Hanlon and P. S. Maybeck, Proceedings of the 1997 Conference on Decision and Control, San Diego, California, pp. 4312-4317, December 1997. Control of a Large Space Structure Using MMAE/MMAC Techniques, by G. J. Schiller and P. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. 33, No. 4, pp. 1122-1131, October 1997. MMAE/MMAC Control for Bending with Multiple Uncertain Parameters, by G. C. Griffin, Jr. and P. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. 33, No. 3, pp. 903-912, July 1997. MMAE-Based Control Redistribution Applied to the VISTA F-16, by M. J. Stepaniak and P. S. Maybeck, Proceedings of the 1997 American Control Conference, Albuquerque, NM, pp. 133-139, June 1997. Reducing Lag in Virtual Displays Using Multiple Model Adaptive Estimation, by D. W. Kyger and P. S. Maybeck, Proceedings of the 1997 American Control Conference, Albuquerque, NM, pp. 2536-2541, June 1997. An MMAE Failure Detection System for the F-16, by P. K. Eide and P. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. 32, No. 3, pp. 1125-1136, July 1996. The Kalman Filter: An Introduction to Concepts, by P. S. Maybeck, Symposium on Autonomous Underwater Vehicle Technology, tutorial published by the IEEE Oceanic Engineering Society, Monterey, CA, 3 June 1996. Initial Data Truncation for Univariate Output of Discrete-Event Simulations Using the Kalman Filter, by M. A. Gallagher, K. W. Bauer, Jr., and P. S. Maybeck, Management Science, Vol. 42, No. 4, pp. 559-575, April 1996. Adaptive Tracking of Multiple Hot-Spot Target IR Images, by P. S. Maybeck and S. K. Rogers, Precision Stabilization and Tracking Systems for Acquisition, Pointing, and Control Applications, edited by M. K. Masten and L. A. Stockum, SPIE Optical Engineering Press, Bellingham, Washington, 1996. Sequence Comparison Techniques for Multisensor Fusion and Target Recognition, by E. W. Libby and P. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. 32, Number 1, pp. 52-65, January 1996. Implementation and Demonstration of a Multiple Model Adaptive Estimation Failure Detection System for the F-16, by P. K. Eide and P. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, New Orleans, Louisiana, pp. 1873-1878, December 1995. MMAE/MMAC Techniques Applied to Large Space Structure Bending with Multiple Uncertain Parameters, by G. C. Griffin, Jr. and P. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, New Orleans, Louisiana, pp. 1153-1158, December 1995. Performance Enhancements of a Multiple Model Adaptive Estimator, by P. S. Maybeck and P. D. Hanlon, IEEE Transactions on Aerospace and Electronic Systems, Vol. 31, Number 4, pp. 1240-1254, October 1995. Sensor/Actuator Failure Detection in the VISTA F-16 by Multiple Model Adaptive Estimation, by T. E. Menke and P. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. 31, Number 4, pp. 1218-1229, October 1995. Evaluation of a Multiple Model Failure Detection System for the F-16 in a Full-Scale Nonlinear Simulation, by P. K. Eide and P. S. Maybeck, Proceedings of the IEEE National Aerospace and Electronics Conference, Dayton, Ohio, pp. 531-536, May 1995. An Integrated GPS/INS/Baro and Radar Altimeter System for Aircraft Precision Approach Landings, by R. A. Gray and P. S. Maybeck, Proceedings of the IEEE National Aerospace and Electronics Conference, Dayton, Ohio, pp. 161-168, May 1995. Second-Order Acceleration Models for an MMAE Target Tracker, by B. J. Wheaton and P. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. 31, Number 1, pp. 151-167, January 1995. Initial Data Truncation for Multivariate Output of Discrete-Event Simulations Using the Kalman Filter, by M. A. Gallagher, K. W. Bauer, Jr., and P. S. Maybeck, Annals of Operations Research, Volume on Simulation and Modeling, Volume 53 (Editor: Osman Balci), pp. 419-442, December 1994. Multiple Model Adaptive Estimation Applied to the Lambda URV for Failure Detection and Identification, by D. W. Lane and P. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, Lake BuenaVista, Florida, pp. 678-683, December 1994. Multiple Model Adaptive Control of a Large Flexible Space Structure with Purposeful Dither for Enhanced Identifiability, by J. A. Fitch and P. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, Lake BuenaVista, Florida, pp. 2904-2909, December 1994. Stochastic Models, Estimation and Control, Vol. 1, by P. S. Maybeck, Navtech Press, Arlington, Virginia, 1994 (republished). Stochastic Models, Estimation and Control, Vol. 2, by P. S. Maybeck, Navtech Press, Arlington, Virginia, 1994 (republished). Space Structure Control Using Multiple Model Adaptive Estimation and Control, by G. J. Schiller and P. S. Maybeck, Proceedings of the 1994 IFAC Symposium on Automatic Control in Aerospace - Aerospace Control '94, Palo Alto, California, pp. 216-221, September 1994. Flexible Spacestructure Control via Moving-Bank Multiple Model Algorithms, by J. A. Gustafson and P. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. 30, No. 3, pp. 750-757, July 1994. Target Tracking Using Infrared Measurements and Laser Illumination, by P. S. Maybeck, T. D. Herrera and R. J. Evans, IEEE Transactions on Aerospace and Electronic Systems, Vol. 30, No. 3, pp. 758-768, July 1994. Application of Sequence Comparison Techniques to Multisensor Fusion and Target Recognition, by E. W. Libby and P. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, San Antonio, Texas, pp. 241-248, December 1993. Performance Enhancements of a Multiple Model Adaptive Estimator, by P. S. Maybeck and P. D. Hanlon, Proceedings of the IEEE Conference on Decision and Control, San Antonio, Texas, pp. 462-468, December 1993. An Alternate Algorithm for Discrete-Time Filtering, by F. H. Zeitz III and P. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. 29, No. 4, pp. 1123-1136, October 1993. Sensor/Actuator Failure Detection in the VISTA F-16 by Multiple Model Adaptive Estimation, by T. E. Menke and P. S. Maybeck, Proceedings of the 1993 American Control Conference (ACC), San Francisco, CA, pp. 3135-3140, June 1993. An Optimizing Design Strategy for Multiple Model Adaptive Estimation and Control, by S. N. Sheldon and P. S. Maybeck, IEEE Transactions on Automatic Control, Vol. 38, No. 4, pp. 651-654, April 1993. Tracking of a Reflective Target Using Infrared Measurements and Laser Illumination, by P. S. Maybeck, T. D. Herrera and R. J. Evans, Proceedings of the IEEE Conference on Decision and Control, Tucson, Arizona, pp. 3784-3789, December 1992. Control of a Large Flexible Space Structure with Moving-Bank Multiple Model Adaptive Algorithms, by J. A. Gustafson and P. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, Tucson, Arizona, pp. 1273-1278, December 1992. Confidence Intervals for Univariate Discrete-Event Simulation Output Using the Kalman Filter, by R. B. Howard, M. A. Gallagher, K. W. Bauer, Jr., and P. S. Maybeck, Proceedings of the 1992 Winter Simulation Conference, Arlington, VA, pp. 586-593, December 1992. Second-Order Acceleration Models for a Multiple Model Adaptive Estimator in a FLIR Tracker, by B. J. Niblett and P. S. Maybeck, Proceedings of the IEEE Conference on Control Applications, Dayton, Ohio, pp. 1044-1051, September 1992. Application of Multiple Model Adaptive Algorithms to Reconfigurable Flight Control , by P. S. Maybeck, in Control and Dynamic Systems, Vol. 52 (Integrated Technology Methods and Applications in Aerospace Systems Design), edited by C. T. Leondes; Academic Press, 1992. Reduced-Order Multiple Model Adaptive Controller for Flexible Spacestructure, by P. S. Maybeck and M. R. Schore, IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-28, No. 3, pp. 756-767, July 1992. Comparative Analysis of Backpropagation and the Extended Kalman Filter for Training Multilayer Perceptrons, by D. Ruck, S. K. Rogers, M. Kabrisky, P. S. Maybeck and M. E. Oxley, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 6, pp. 686-691, June 1992. Multiple Model Adaptive Estimation Applied to the VISTA F-16 Flight Control System with Actuator and Sensor Failures, by T. E. Menke and P. S. Maybeck, Proceedings of the IEEE National Aerospace and Electronics Conference, Dayton, Ohio, pp. 441-448, May 1992. Multiple Model Adaptive Controller for the STOL F-15 with Sensor/Actuator Failures, by P. S. Maybeck and D. L. Pogoda, Advances in Robust Adaptive Control, edited by Kumpati S. Narendra, Romeo Ortega, and Peter Dorato; IEEE Press, 1992. Reconfigurable Flight Control Via Multiple Model Adaptive Control Methods, by P. S. Maybeck and R. D. Stevens, IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-27, No.3, pp. 470-480, May 1991. On an Assumed Convergence Result in the LQG/LTR Technique, by C. L. Matson and P. S. Maybeck, IEEE Transactions on Automatic Control, Vol. AC-36, No. 1, pp. 123-125, Jan.. 1991. An Optimizing Design Strategy for Multiple Model Adaptive Estimation and Control, by S. N. Sheldon and P. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, Honolulu, Hawaii, pp. 3522-3527, December 1990. Reconfigurable Flight Control Via Multiple Model Adaptive Control Methods, by P. S. Maybeck and R. D. Stevens, Proceedings of the IEEE Conference on Decision and Control, Honolulu, Hawaii, pp. 3351-3356, December 1990. The Kalman Filter: An Introduction to Concepts, by P. S. Maybeck, in Autonomous Robot Vehicles, edited by I. Cox and G. Wilfong, Springer-Verlag, New York, pp. 194-204, 1990. Robustness of a Moving-Bank Multiple Model Algorithm for Control of a Flexible Spacestructure, by P. S. Maybeck and M. R. Schore, Proceedings of the IEEE National Aerospace and Electronics Conference, Dayton, Ohio, pp. 368-374, May 1990. Multiple Model Adaptive Controller for the STOL F-15 with Sensor/Actuator Failures, by P. S. Maybeck and D. L. Pogoda, Proceedings of the IEEE Conference on Decision and Control, Tampa, Florida, pp. 1566-1572, December 1989. A New Interpretation of the LQG/LTR Technique Using Optimal Projection Equations, by R. N. Paschall and P. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, Tampa, Florida, pp. 498-501, December 1989. Moving-Bank Multiple Model Adaptive Estimation and Control Algorithms: An Evaluation, by P. S. Maybeck, Control and Dynamic Systems: Advances in Theory and Applications, Vol. 31, edited by C. T. Leondes, Academic Press, pp. 1-31, San Diego, 1989. Adaptive Tracking of Maneuvering Targets Based on IR Image Data, by P. S. Maybeck, Kalman Filter Integration of Modern Guidance and Navigation Systems, NATO AGARD Lecture Series No. 166, edited by G. T. Schmidt, NATO Advisory Group for Aerospace Research and Development, London, England, pp. 7-1 - 7-18, 1989. Reconfigurable Flight Controller for the STOL F-15 with Sensor/Actuator Failures, by D. L. Pogoda and P. S. Maybeck, Proceedings of the IEEE National Aerospace and Electronics Conference, Dayton, Ohio, pp. 318-324, May 1989. An Optimal Parameter Discretization Strategy for Multiple Model Adaptive Estimators, byS. N. Sheldon and P. S. Maybeck, SIAM Conference on Control in the 90's: Achievements, Opportunities, and Challenges, San Francisco, California, May 1989. Spacestructure Control Using Moving Bank Multiple Model Estimation, by R. W. Lashlee, Jr. andP. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, Austin, Texas, pp. 712-717, December 1988. Enhancements to a Multiple Model Adaptive Estimator/Image-Tracker, by D. M. Tobin andP. S. Maybeck, IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-24, No. 4, pp. 417-426, July 1988. Stochastic Adaptive Controller Based on Noise-Corrupted Space-Time Measurement Process, by B. A. Johnson and P. S. Maybeck, Proceedings of the IEEE National Aerospace and Electronics Conference, Dayton, Ohio, pp. 352-359, May 1988. The Linear Quadratic Gaussian / Loop Transfer Recovery Technique for a Class of Distributed Parameter Systems, by C. L. Matson and P. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, Los Angeles, California, pp. 927-932, December 1987. On an Assumed Convergence Result in the LQG/LTR Technique, by C. L. Matson and P. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, Los Angeles, California, pp. 951-952, Dec. 1987. Substantial Enhancements to a Multiple Model Adaptive Estimator for Target Image Tracking, byD. M. Tobin and P. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, Los Angeles, California, pp. 2002-2011, December 1987. Moving Bank Multiple Model Adaptive Estimation Applied to Flexible Spacestructure Control, by D. A. Karnick and P. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, Los Angeles, California, pp. 1249-1257, December 1987. Investigation of Moving-Bank Multiple Model Adaptive Algorithms, by P. S. Maybeck and K. P. Hentz, AIAA Journal of Guidance, Control, and Dynamics, Vol. 10, No. 1, pp. 90-96, Jan/Feb 1987. LQG/LTR Design of a Flight Controller for a Short Take-Off and Landing Aircraft, by G. L. Gross, R. A. Houston and P. S. Maybeck, Proceedings of the IEEE National Aerospace and Electronics Conference, Dayton, Ohio, pp. 415-420, May 1986. Investigation of Moving-Bank Multiple Model Adaptive Algorithms, by P. S. Maybeck and K. P. Hentz, Proceedings of the IEEE Conference on Decision and Control, Ft. Lauderdale, Florida, pp. 1874-1881, Dec. 1985. Sufficient Conditions for Establishing Maximum Likelihood Estimates for Nonlinear Process Parameters, by R. E. Siferd and P. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, Ft. Lauderdale, Florida, pp. 212-213, Dec. 1985. Adaptive Tracker Field-of-View Variation Via Multiple Model Filtering, by P. S. Maybeck andR. I. Suizu, IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-21, No. 4, pp. 529-539, July 1985. Adaptive Tracking of Dynamic Airborne Vehicles Based on (FLIR) Image Plane Intensity Data, byP. S. Maybeck, Proceedings of the Third Bad Honnef Conference on Stochastic Differential Systems, Bad Honnef, West Germany, pp. 284-305, June 1985 (published by Springer-Verlag, Berlin, 1986). "MMAE-Based Control with Space-Time Point Process Observations," by P. S. Maybeck and W. L. Zicker, IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-21, No. 3, pp. 292-300, May 1985. "Adaptive Field of View Expansion Via Multiple Model Filtering for Tracking Dynamic Target Images," by P. S. Maybeck and R. I. Suizu, Proceedings of the IEEE National Aerospace and Electronics Conference, Dayton, Ohio, pp. 364-373, May 1985. "Adaptive Extended Kalman Filter Design and Performance Analysis for Target Image Tracking Systems and Other Applications," by P. S. Maybeck, The Techniques and Technology of the Application of Kalman Filters and Nonlinear Filters, C. T. Leondes, Coordinator, UCLA University Extension, May 1985. "Multiple Model Adaptive Estimation for Space-Time Point Process Observations," by D. E. Meer and P. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, Las Vegas, Nevada, pp. 811-818, Dec. 1984. "Robustness Enhancement for LQG Digital Flight Controller Design," by P. S. Maybeck, W. G. Miller and J. M. Howey, Proceedings of the IEEE National Aerospace and Electronics Conference, Dayton, Ohio, pp. 518-525, May 1984. "Adaptive Tracking of Multiple Hot-Spot Target IR Images," by P. S. Maybeck and S. K. Rogers, IEEE Transactions on Automatic Control, Vol. AC-28, No. 10, pp. 937-943, Oct. 1983. "Kalman Filtering: Concepts, Design and Extensions," by P. S. Maybeck, Proceedings of the 51st Military Operations Research Society Symposium, Dayton, Ohio, pp. 27-29, Sept. 1983. "An Optically Implemented Multiple-Stage Kalman Filter Algorithm," by W. A. Roemer and P. S. Maybeck, Proceedings of the SPIE: Real Time Signal Processing VI, San Diego, California, pp. 221-228, Aug. 1983. "Synthesis and Performance Evaluation Tools for CGT/PI Advanced Digital Flight Control Systems," by P. S. Maybeck, R. M. Floyd and A. Moseley, Proceedings of the IEEE National Aerospace and Electronics Conference, Dayton, Ohio, pp. 1259-1266, May 1983. "Stochastic Identifiability of Nonlinear Dynamical System Parameters," by R. E. Siferd and P. S. Maybeck, Proceedings of the 14th Annual Modeling and Simulation Conference, University of Pittsburgh, Pittsburgh, Pennsylvania, April 1983. "Advanced Applications of Kalman Filters and Nonlinear Estimators in Aerospace Systems," by P. S. Maybeck, Advances in Control and Dynamic Systems, Vol. XX, edited by C. T. Leondes, Academic Press, New York, New York, pp. 67-154, 1983. "Identifiability of Nonlinear Dynamical Systems," by R. E. Siferd and P. S. Maybeck, Proceedings of the IEEE Conference on Decision and Control, Orlando, Florida, pp. 1167-1171, Dec. 1982. "Adaptive Tracking of Dynamic Multiple Hot-Spot Target IR Images," by P. S. Maybeck and S. K. Rogers, Proceedings of the IEEE Conference on Decision and Control, Orlando, Florida, pp. 1145-1151, Dec. 1982. "Investigation of Constant Turn-Rate Dynamics Models in Filters for Airborne Vehicle Tracking," by P. S. Maybeck, W. H. Worsley and P. M. Flynn, Proceedings of the IEEE National Aerospace and Electronics Conference, Dayton, Ohio, pp. 896-903, May 1982. "Identifiability of Nonlinear Dynamical Systems with an Application to the Optimal Control Model for the Human Operator," by R. E. Siferd and P. S. Maybeck, Proceedings of the 13th Annual Modeling and Simulation Conference, University of Pittsburgh, Pittsburgh, Pennsylvania, April 1982. Stochastic Models, Estimation and Control, Vol. 3, by P. S. Maybeck, Academic Press, New York, N.Y., 1982. Stochastic Models, Estimation and Control, Vol. 2, by P. S. Maybeck, Academic Press, New York, N.Y., 1982. "Design and Performance Analysis of an Adaptive Extended Kalman Filter for Target Image Tracking," by P. S. Maybeck, Advances in the Theory and Technology of Applications of Nonlinear Filters and Kalman Filters, NATO AGARDograph No. 256, NATO Advisory Group for Aerospace Research and Development, London, England, pp. 10-1 - 10-15, 1982. "Reduced Order Kalman Filter Design and Performance Analysis," by P. S. Maybeck, Advances in the Theory and Technology of Applications of Nonlinear Filters and Kalman Filters, NATO AGARDograph No. 256, NATO Advisory Group for Aerospace Research and Development, London, England, pp. 9-1 - 9-16, 1982. "An Adaptive Extended Kalman Filter for Target Image Tracking," by P. S. Maybeck, R. L. Jensen and D. A. Harnly, IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-17, No. 2, pp. 172-180, March 1981. "Estimation of Aircraft Target Motion Using Orientation Measurements," by J. D. Kendrick, P. S. Maybeck and J. G. Reid, IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-17, No. 2, pp. 254-260, March 1981. "An Adaptive Distributed-Measurement Extended Kalman Filter Tracker," by P. S. Maybeck, R. L. Jensen and D. A. Harnly, Proceedings of the IEEE Conference on Decision and Control, Albuquerque, New Mexico, pp. 554-559, Dec. 1980. "Robustness of a New Infrared Target Tracker," by D. A. Harnly, R. L. Jensen and P. S. Maybeck, Proceedings of the IEEE National Aerospace and Electronics Conference, Dayton, Ohio, pp. 639-644, May 1980. "A Target Tracker Using Spatially Distributed Infrared Measurements," by P. S. Maybeck and D. E. Mercier, IEEE Transactions on Automatic Control, Vol. AC-25, No. 2, pp. 222-225, April 1980. "Performance Analysis of a Particularly Simple Kalman Filter," by P. S. Maybeck, Rocket Technology and Cosmonautics (Soviet technical journal; by agreement with AIAA), 1979. "A Target Tracker Using Spatially Distributed Infra-Red Measurements," by P. S. Maybeck and D. E. Mercier, Proceedings of the IEEE Conference on Decision and Control, Fort Lauderdale, Florida, pp. 285-289, Dec. 1979. "A New Tracker for Air-to-Air Missile Targets," by P. S. Maybeck, J. E. Negro