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Thomas Kerr III
Works at TeK Associates: has consulted at MITRE, XonTech, and Raytheon (2 contracts) for National Missile Defense (NMD); subcontracted to Arete for a Navy Airborne Remote Optical Spotlight System (AROSS) Littoral surveillance INS/GPS software development (in Matlab) and subsequent simulation-based trade-off study. Had 6+ month contract at Goodrich ISR supporting pointing accuracy of airborne SYERS camera equipped with IMU (& Matlab s/w).
Attends School of Hard Knocks! Earned M.S. & Ph.D. in Electrical Engineering in 1969 & 1971, respectively, major: Classical & Modern Control & Estimation Theory. Has taken 95+ Continuing Education courses to stay current.
Lives in Lexington, MA.


Thomas Kerr III

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Thomas Kerr III

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A good book by Bruce P. Gibbs (and contributed to by many of my TASC cohorts from the 1970's and other illustrious contemporaries):
Ph.D. in EE, DSP R&D Engineer: Estimation & Kalman Filter specialist, TeK Associates (CEO/Owner/Algorithm Engineer/Software Developer)
As a Ph.D. electrical engineer (by age 25), he specialized in Stochastic Control Theory in school by first studying classical frequency domain principles and procedures including: 
  • Proportional, Integral, Derivative (PID) compensation, 
  • Nyquist diagrams, Bode Plots, Nichols charts, M- and N-circles, Root Locus Plots (and associated use of spirule); 
(where the first bulleted item above is for achieving or improving system stability in Linear Time-Invariant systems, and the last bullet consists of alternative techniques for assessing it, as gauged by gain- and phase-margins revealing the system's lack of proximity to the minus one [i.e., -1] point in the complex plane). He then proceeded to gain a deep understanding of newer modern state variable-based estimation techniques (e.g., Kalman filters, Least Squares [LS] and Maximum Likelihood Estimation [MLE] and practical approximations) and of:
  • sampled-data discrete-time control,
  • bang-bang time-optimal control when there are no appreciable noise disturbances present,
  • nonlinear control for noise-free systems,
  • Lyapunov function methods for assessing stability,
  • Lebesgue integrals & measure theory-based probability theory for a rigorous understanding of conditional expectation & martingales,
  • Ito and Stratonovich stochastic calculus (and McShane Integration later), 
  • calculus-of-variations,
  • Pontryagin Maximum (or Minimum) Principle,
  • Luenberger Observers,
  • stochastic control for systems with process/plant & measurement noises that are fully statistically characterized: mean and variance (or matrix power spectrum),
  • the Separation Theorem, which stipulates when the estimation algorithm may be rigorously separated from the control law implementation (then conjoined afterwards), Some non-rigorous separations (even when the underlying systems are not strictly linear) may also perform adequately although they were designed separately before being integrated together., (Scroll down the last screen for details.)
  • Linear Quadratic Gaussian/Loop Transfer Recovery (LQG/LTR) feedback control (utilizing a Kalman filter).
and locally linearized optimal control approaches in the time domain. This path was pursued since realistic practical problems are usually nonlinear and time-varying and sometimes involve several (possibly nested) feedback loops: (in 1969) and Gelb, A. and Vander Velde, W. E., Multiple-Input Describing Functions and Nonlinear System Design, McGraw-Hill Book Company, NY, 1968, now found at: cf. Ching, S.-N., Eun, Y., Gokcek, C., Kabamba, P. T., Meerkov, S. M., Quasilinear Control: Performance analysis and design of feedback systems with nonlinear sensors and actuators, Cambridge University Press, Cambridge, UK, 2011 (it merely repeats pre-existing knowledge).
While teaching Optimal Control in the evenings at Northeastern University Graduate ECE Dept. from 1990-95, he also learned the pros & cons of more recent Robust Control. This new methodology is somewhat disappointing because it does not offer solutions to a wider class of problems than were already available. He wonders why engineers or other technologist should pursue this new approach merely because it takes an alternative viewpoint and employs different tools yet fails to avail any deeper insights? Even when applicable, this new approach results in a more sluggish system response (reminiscent of an over-damped system) that could only be tolerated within a relatively few process control applications. However, no warnings accompanied it. Moreover, older frequency domain-based compensation techniques in conjunction with the prior techniques of circuit-theory-based Network Synthesis can historically handle this so-called "new" Robust Control formulation without fanfare. 

While in school, he took extra courses in physics and in advanced mathematics (i.e., graduate courses) beyond what were required for engineers at the time. These have served him well while engaging in Research & Development for four decades. He is also a software developer with insight into a wide variety of algorithms and in how to best test them, stress them, or even break them (as a countermeasure a'la Old Crow).

He originally conceived of and developed Two Confidence Region (CR2) automated Failure Detection (a.k.a. Event Detection) testing approach (applied to augmented, aided navigation systems) for detecting component navigation sensor hardware failures or failures in its supporting subsystem processing, where either or both constitute situations warranting reconfiguration in the rigorous redundancy management of these navigation system complexes using underlying constituent decentralized Kalman filter concepts (which dovetails nicely with the associated reliability and availability analysis for handling m-out-of-n redundancy and warm standby subsystems, and which also routinely account for Mean-Time-To-Failure [MTTF], Mean-Repair-Time [MRT], and Failure Modes & Effects Analysis [FMEA]) for various architectures in order to provide sufficient total system availability. The two probabilities of false alarm and of correct detection associated with integrated use of the CR2 fault detection (a.k.a. Change Detection or Event Detection) approach is inherited by a non-ideal 3-state switch within the associated diagram to analytically account for this automated failure detection aspect within this standard linear structural diagram for analyzing system availability. For the entire sequence of original supporting documentation, please see: 
or (as seen by scrolling to the very bottom of the following screen):

From implementation experience, he is also cognizant of the advantages and drawbacks of various alternative approaches to event detection, including use of the Generalized Likelihood Ratio (GLR). He is cognizant of the fundamental underlying theory and alert to practical implementation considerations and the necessity to balance both. For further elaboration, please see:

He has become an expert in Kalman Filters and in other statistical estimation algorithms, as applied under contract to a wide variety of DoD applications:
  • Analyzing and simulating submarine Inertial Navigation Systems (INS) and other land-, sea-, and aircraft navigation applications (utilizing external navaids like GPS, VOR/ DME, and bathymetric map-matching as fix/resets for an INS through its associated NAV filter);
  • Developing Failure Detection, Identification, and Reconfiguration (FDIR) tests for navigation systems by seeking out and being aware of unique characteristics of known faults that can arise in each different application and by tailoring appropriate FDIR tests to match the resulting failure signal response options; 
  • Investigating particular Decentralized Kalman Filter (DKF) formulations for JTIDS RelNav and later continuing this line of R&D for combining FDIR and DKF within ICNIA;
  • Becoming cognizant of appropriate Kalman filter formulations for systems described by a Partial Differential Equation (PDE):;
  • Following developments associated with Interactive Multiple Model (IMM) bank-of-Kalman-Filters:
He posed and solved the problem of SSBN submarine external navaid fix utilization while evading enemy surveillance as a "cat-and-mouse" game of "sensor schedule optimization" within a natural Kalman NAV filter context. This approach balances the benefit of maintaining the requisite INS accuracy (by using alternative external Navaid fixes at a satisfactory rate and quality to correct for inherent gyro and accelerometer drift-rates that otherwise degrade INS accuracy over time if such fix/resets are not taken) versus the sweep rate exposure to enemy surveillance (quantification availed from JHU/APL of worse case detection threat for each navaid under consideration) incurred while taking those fixes. Official error models of SSBN Ships Inertial Navigation System (SINS) and new (at the time) Electrostatically Supported Gyro Monitor (ESGM) and their corresponding Kalman Navigation filters were utilized in the actual performance quantifications that he generated and reported to SP-24. Adequate owncraft INS location & velocity accuracy is needed to immediately transfer to Fire Control in case an SLBM launch is ordered. Upon transfer of SSBN INS information to the SLBM INS as a transfer alignment, the SLBM first essentially "flies in the tubes" briefly before being launched. It is impossible for an SLBM to accurately hit its designated target unless initial SSBN location data (upon launch) is sufficiently accurate. The following list has extensions to this approach as items 7 & 9: (He always carefully removes all classified quantifications from open literature discussions of only the underlying supporting methodology.)
He performed Independent Verification & Validation (IV&V) of passive and active sonar/sonobuoy target tracking for U.S. Navy's Light Airborne Multi-Platform System (LAMPS) for Anti-Submarine Warfare (ASW) signal processing: 
  • Lofar/Difar (Angle-Only), Post-Coherence Function (PCF) estimation
  • Time Delay Estimation (TDE)
  • Passive Tracking Algorithm (PTA)
and analyzed and simulated strategic Early Warning Radar for:
  • Strategic Defense Initiative (SDI) [a.k.a. Star Wars
  • National Missile Defense/Updated Early Warning Radar (NMD/UEWR
  • Angle-Only (a.k.a. Bearings-Only) for range-denied jammed radar & Difar sonar
for quantifying the corresponding target tracking filter performance for each of the above items and their interaction with multi-target tracking algorithms. Among the more mundane tasks he has performed:
  • Developed test plans, procedures, checklists, guidelines, and rationale for evaluating ship-borne performance of 4 competing commercial SATNAV receivers; 
  • Surveyed and summarized GPS Phase II Specifications and options/variations in cross-checking both contractors compliance (and in tagging violations) during Phase II competition for follow-on Phase III. Performed operational test & evaluation in monitoring GPS integration on attack submarine SSN-701 La Jolla, both dockside (in San Diego, CA) and at sea as part of DT&E(OR), and investigated the susceptibility of its AN/BRA-34 antenna (by obtaining quantification of its radar cross-section) to detection by enemy radar surveillance while taking GPS fixes (by considering duration and approximate frequency of fixes);
  • Participated on a team representing U.S. government interests by critiquing Kalman filter design and performance of early Magnavox Phase 1 minesweeper Precise Integrated Navigation System (PINS) that relied on an unsavory ship's motion model instead of being unified about the necessary INS (initially absent on this platform but introduced for Phase 2);
  • Worked on IV&V and documented several security issues for WIS (WWMCCS Improved System, where WWMCCS is World Wide Military Command and Control System): relating to use of coaxial vs. triaxial cable/grounding; some potential vulnerabilities of fiber optics links; addressed WASSO (i.e., system administrator) issues and concerns; wrote white paper on use of encryption vs. check sums; scoped initial version of overall security plan. Gained familiarity with Orange-, Green-, Yellow-, and Bluebooks, being the DoD standards adhered to by the National Security Administration (NSA) in order for them to sanction anything for properly handling classified data processing. (Obviously, this assignment had nothing to do with Kalman filters but, never-the-less, was extremely useful to Tom years later in considering how to properly handle passwords, perform classified processing, perform classified deletes, and the "how’s" and "why’s" of enabling encryption within TeK Associates’ current TK-MIP software product);
  • Applied his Kalman filter/navigation theory background to offer pre-planned grooming, such as intermediate waypoint insertion and preparation of real-time turn-around cues to initiate 3 minute, 180 degree turns, and selection of candidate navaid fix types and fix-rate to support sufficiently accurate airborne multi-sensor data collection from a Grumman G-1 aircraft (with a Honeywell LASERNAV II INS) over an Electronic Terrain Board (with known physical targets inserted) by avoiding blatant gaps yet yielding reduced redundancy in overlap incurred during row-wise backsweeps after each end-of-row turn. Purpose of collecting this multi-sensor data is for later tuning of Neural Networks (by others), as used for Automatic Target Recognition (ATR) algorithms.

He conveyed results for each of the above in separate company technical reports. He also investigated their associated signal processing CPU loading aspects, for example:

He has applied and extended Search & Screening theory: Kerr, T. H., "Impact of Navigation Accuracy in Optimized Straight-Line Surveillance/ Detection of Undersea Buried Pipe Valves," Proceedings of National Maritine Meeting of the Institute of Navigation (ION). Cambridge, MA, 27-29 October 1982, as used for detecting targets and, conversely, for eluding detection. He also has experience with 2D image processing and restoration algorithms (for blur compensation and for noise amelioration). Examples:

In performing design and analysis for the aforementioned applications, he routinely implements requisite Monte-Carlo simulations for trade-off studies to set parameters and to substantiate conclusions. He has 40+ years at these activities, including 19 years developing user-centric Windows-based software (s/w) utilizing multiple tools and many 3rd party add-ons: He is Web Master of his company web site:

While his earlier programming experience was in Assembly Language, PL/1, and Fortran, he has become an expert in using Visual Basic  for developing his company's Windows-based product, TK-MIP, and he has also utilized MatLab and Simulink (currently possessing 15 specialty Toolboxes & Blocksets, respectively, including a MatLab-to-C cross-compiler for other contract work since 1992. He appreciates the ease of using these modern high level computer languages all the more since his having implemented an Assembly Language version of a real-time algorithm on GEPAC-30 minicomputers for ultrasonically detecting flaws in large turbine rotors so that General Electric would be aware of potential problems beforehand (and subsequently avoid the wasted step of attaching blades to a faulty rotor during the manufacturing process). While at GE in their Industrial Simulations Group, he also improved General Electric's Automated Dynamic Analyzer (ADA), internally available, proprietary software for modeling and simulation.
He served as Chairman and Vice-Chairman of the Boston Section of the IEEE Control System Society for over eight years, where he was nominated to become an IEEE Fellow for his contributions to Kalman Filter theory and applications and for correcting and clarifying Cramer-Rao Lower Bound (CRLB) calculations for nonlinear estimation of Ballistic targets.

He has 130+ open literature peer-reviewed publications and company reports: and he continues to actively publish in these associated R&D areas: His publications were recently cited 122+ times by other independent authors and researchers worldwide:, thus endorsing their significance. He also received the 1987 M. Barry Carlton 
Award for best paper to appear in the IEEE Transactions on Aerospace and Electronic Systems:

IEEE (Gold life senior member), AIAA Guidance, Navigation, & Control (since 1984, elected Associate Fellow in 2012):, ION (since 1981), ISA, NDIA (life member), MSDN (level 2). Experienced user of Microsoft Office products, MathType, and LaTeX for documenting his analysis results and for writing technical proposals. Detailed resume:
Bragging rights
Received BSEE Magna Cum Laude. Was student president of his undergraduate chapter of Pi Mu Epsilon. Scored in 97th percentile of Graduate Record Mathematics Specialty Exam upon entering graduate school at University of Iowa. Has received two awards for technical writing: (1) in 1967, from the Federal Power Commission in a competition between engineering schools in the metropolitan Washington D.C. area; (2) in 1988, the M. Barry Carlton Award for Best Paper to appear in IEEE Transactions on Aerospace and Electronic Systems in 1987. Served as Co-chairman of “Sensors, Components, and Algorithms for Navigation” session at the Institute of Navigation (ION) Annual Summer Conference (1999) in Cambridge, MA. Vice-Chairman of Stochastic Control Session at Conference on Decision and Control (CDC) in 1975. Principal developer of TK-MIP software for the PC. By knowing when to duck, has remained married to Aniece Ragland Kerr since 1975, and they have two adult sons. Had 9 technical conference presentations/publications in 2001. Chairman of local Boston section of Control System Society twice (1990-92; 2000-04); Vice-Chairman (1995-96); Chairman of its Steering Committee (1992-94); At-large member of Steering Committee (1997-98). Was always on good speaking terms with the late Dr. Gerald J. Bierman but less so with Dr. Neal A. Carlson. (Both of the aforementioned known for their work on revolutionizing squareroot Kalman filter mechanization.) Personal best half-mile time: 2:03; mile time: 4:40; 10 mile: 1:03; 20 mile: 2:06. Has 12 marathons under his belt in the 1970's. Finished 3rd in a 10 mile race behind Tom Osler in 1972. Member of Charles River Wheelmen (CRW) long distance bicycling club since 1977. Was CRW Ride Leader (1990-97). He and his wife took American Combat Karate in 1975. He is an enthusiastic stilt walker, roller skater, skier (downhill and cross-country), occasional swimmer, and is pursuing the unicycle. Received Rensselaer Medal for mathematics and science proficiency upon graduating from Jr. High School. He lettered in Track in High School, was a member of the Honor Society; and, upon graduating, received Science Award & also the Music Educators' Award (and a one year's membership in the Musician's Union). He played cello, flute, recorder, piccolo, oboe, and viola; but he has now, evidently, learned to "toot his own horn" about himself, in stark contrast to his usual unassuming nature. At Howard Univ., he received the Western Electric, McDonnell Douglas, and Engineers' Wives' Awards, each having an associated modest financial stipend. At Univ. of Iowa, he had an NSF Traineeship later. His academic society affiliations: Pi Mu Epsilon, Tau Beta Pi, Sigma Pi Sigma, Eta Kappa Nu, & Sigma Xi (later). He still averages 100+ miles/week by bicycle, especially when writing something technical. He became an AIAA GNC Associate Fellow in Jan. 2012.
  • School of Hard Knocks! Earned M.S. & Ph.D. in Electrical Engineering in 1969 & 1971, respectively, major: Classical & Modern Control & Estimation Theory. Has taken 95+ Continuing Education courses to stay current.
    Stochastic/Modern Control & Estimation Theory, 1969 - present
  • Howard University, Washington, D.C. ('63-'67)
    B.S.E.E.- magna cum laude (Electronics)
  • George Washington University, Washington, D.C. (summer '67)
    Adv. Calculus, Complex Variables (evenings only)
  • University of Iowa, Iowa City, IA (Sep. '67-Feb. '71)
    Elec.Eng-Classical/Modern Control & Estimation Theory
  • UCLA Continuing Education (summer '69)
    Adv. KF (2 wk, 8 hr/day)
  • MIT Continuing Education ('74, summer '94)
  • University of Maryland Continuing Ed. ('89).
Basic Information
Other names
T. H. Kerr III, Thomas H. Kerr III, T. H. Kerr
R&D Algorithm Engineer, Systems Engineer, Software Developer
Engineering & Mathematical Analysis; Systems Engineer in Kalman filter theory & applications: INS/GPS Navigation; radar target tracking filters; Software Developer: Matlab & Simulink; Visual Basic; Fortran; Visio; knowledge of VBX/OCX 3rd party tools; Technical Writing: M/S Office & LaTeX
  • TeK Associates: has consulted at MITRE, XonTech, and Raytheon (2 contracts) for National Missile Defense (NMD); subcontracted to Arete for a Navy Airborne Remote Optical Spotlight System (AROSS) Littoral surveillance INS/GPS software development (in Matlab) and subsequent simulation-based trade-off study. Had 6+ month contract at Goodrich ISR supporting pointing accuracy of airborne SYERS camera equipped with IMU (& Matlab s/w).
    CEO/Principal Investigator, 1992 - present
  • TeK Associates
    R&D Algorithm Engineer, Systems Engineer, Software Developer, Web Master (CEO/Owner), 1992 - present
  • Howard University, Washington, D.C. (summer, '67)
    Research Assistant
  • University of Iowa, Iowa City, IA ('68-'71)
    Teaching Assistant/Research Assistant
  • General Electric Corporate R&D Center, Schenectady, NY ('71-'73)
    Control Engineer in Information Studies Branch
  • The Analytic Sciences Corporation (TASC), Reading, MA ('73-'79)
    Member of the Technical Staff
  • Intermetrics, Inc., Cambridge, MA ('79-'86) now L3
    Senior Analyst/Systems Engineer
  • MIT Lincoln Laboratory, Lexington, MA ('86-'92)
    Member of the Technical Staff
  • Northeastern University, Burlington, MA Campus (evenings only, '90-'94)
    Instructor in Graduate ECE Dept. teaching Optimal Control & Estimation
  • Kelley Sevices, as contractor at Google Books, Lexington, MA ('07 - '09)
    Scanner and QA Operator (2nd Shift)
  • Adecco, as contractor at Goodrich, ISR in Westford, MA (Oct. '11-May '12)
    Systems Engineer
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Lexington, MA.
Washington, D. C. - Iowa City, IA - Schenectady, NY - Winchester, MA - Mobile, AL