Engineering topics
Publications and source records attributed to Wheeler, Kevin.
We are proposing to develop a miniaturized polarimetric L-band synthetic aperture radar (SAR) for repeatpass differential interferometric measurements of deformation for rapidly deforming surfaces of geophysical interest such as volcanoes or earthquakes that is to be flown on a unmanned aerial vehicle (UAV or minimally piloted vehicle (MPV).
We demonstrate experimentally that inference in a complex hybrid Dynamic Bayesian Network (DBN) is possible using the 2 - T i e Slice DBN (2T-DBN) from [Koller & Lerner, 20001 to model fault detection in a watertank system. In [Koller & Lerner, 20001 a generic Particle Filter (PF) is used for inference. We extend the experiment and perform approximate inference using The Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). Furthermore, we combine these techniques in a 'non-strict' Rao-Blackwellisation framework and apply it to the watertank system. We show that UKF and UKF in a PF framework outperfom the generic PF, EKF and EKF in a PF framework with respect to accuracy and robustness in terms of estimation RMSE. Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. We also show that the choice of network structure is very important for the performance of the generic PF and the EKF algorithms, but not for the UKF algorithms. Furthermore, we investigate the influence of data noise in the water[ank simulation. Theory and implementation is based on the theory presented.
We are proposing to develop a miniaturized polarimetric L-band synthetic aperture radar (SAR) for repeat-pass differential interferometric measurements of deformation for rapidly deforming surfaces of geophysical interest such as volcanoes or earthquakes that is to be flown on a unmanned aerial vehicle (UAV) or minimally piloted vehicle (MPV). Upon surveying the capabilities and availabilities of such aircraft, the Proteus aircraft and the ALTAIR UAV appear to meet our criteria in terms of payload capabilities, flying altitude, and endurance. To support the repeat pass deformation capability it is necessary to control flight track capability of the aircraft to be within a specified 10 m tube with a goal of 1 m. This requires real-time GPS control of the autopilot to achieve these objectives that has not been demonstrated on these aircraft. Based on the Proteus and ALTAIR's altitude of 13.7 km (45,000 ft), we are designing a fully polarimetric L-band radar with 80 MHz bandwidth and a 16 km range swath. The radar will have an active electronic beam steering antenna to achieve a Doppler centroid stability that is necessary for repeat-pass interferometry. This paper presents some of the trade studies for the platform, instrument and the expected science.
The goal of visual inference programming is to develop a software framework data analysis and to provide machine learning algorithms for inter-active data exploration and visualization. The topics include: 1) Intelligent Data Understanding (IDU) framework; 2) Challenge problems; 3) What's new here; 4) Framework features; 5) Wiring diagram; 6) Generated script; 7) Results of script; 8) Initial algorithms; 9) Independent Component Analysis for instrument diagnosis; 10) Output sensory mapping virtual joystick; 11) Output sensory mapping typing; 12) Closed-loop feedback mu-rhythm control; 13) Closed-loop training; 14) Data sources; and 15) Algorithms. This paper is in viewgraph form.
Scientists are conducting research into electroencephalograms (EEGs) of brainwave activity, and electromyography (EMG) of muscle activity, in order to develop systems which can control an aircraft with only a pilot's thoughts. This article describes some EEG and EMG signals, and how they might be analyzed and interpreted to operate an aircraft. The development of a system to detect and interpret fine muscle movements is also profiled in the article.
We present a novel smoothing approach to non-parametric regression curve fitting. This is based on kernel partial least squares (PLS) regression in reproducing kernel Hilbert space. It is our concern to apply the methodology for smoothing experimental data where some level of knowledge about the approximate shape, local inhomogeneities or points where the desired function changes its curvature is known a priori or can be derived based on the observed noisy data. We propose locally-based kernel PLS regression that extends the previous kernel PLS methodology by incorporating this knowledge. We compare our approach with existing smoothing splines, hybrid adaptive splines and wavelet shrinkage techniques on two generated data sets.
The close relation of signal de-noising and regression problems dealing with the estimation of functions reflecting dependency between a set of inputs and dependent outputs corrupted with some level of noise have been employed in our approach.
We implement a 2-time slice dynamic Bayesian network (2T-DBN) framework and make a 1-D state estimation simulation, an extension of the experiment in (v.d. Merwe et al., 2000) and compare different filtering techniques. Furthermore, we demonstrate experimentally that inference in a complex hybrid DBN is possible by simulating fault detection in a watertank system, an extension of the experiment in (Koller & Lerner, 2000) using a hybrid 2T-DBN. In both experiments, we perform approximate inference using standard filtering techniques, Monte Carlo methods and combinations of these. In the watertank simulation, we also demonstrate the use of 'non-strict' Rao-Blackwellisation. We show that the unscented Kalman filter (UKF) and UKF in a particle filtering framework outperform the generic particle filter, the extended Kalman filter (EKF) and EKF in a particle filtering framework with respect to accuracy in terms of estimation RMSE and sensitivity with respect to choice of network structure. Especially we demonstrate the superiority of UKF in a PF framework when our beliefs of how data was generated are wrong. Furthermore, we investigate the influence of data noise in the watertank simulation using UKF and PFUKD and show that the algorithms are more sensitive to changes in the measurement noise level that the process noise level. Theory and implementation is based on (v.d. Merwe et al., 2000).
This paper presents results of a recent experiment in fine grain Electromyographic (EMG) signal recognition, We demonstrate bioelectric flight control of 757 class simulation aircraft landing at San Francisco International Airport. The physical instrumentality of a pilot control stick is not used. A pilot closes a fist in empty air and performs control movements which are captured by a dry electrode array on the arm, analyzed and routed through a flight director permitting full pilot outer loop control of the simulation. A Vision Dome immersive display is used to create a VR world for the aircraft body mechanics and flight changes to pilot movements. Inner loop surfaces and differential aircraft thrust is controlled using a hybrid neural network architecture that combines a damage adaptive controller (Jorgensen 1998, Totah 1998) with a propulsion only based control system (Bull & Kaneshige 1997). Thus the 757 aircraft is not only being flown bioelectrically at the pilot level but also demonstrates damage adaptive neural network control permitting adaptation to severe changes in the physical flight characteristics of the aircraft at the inner loop level. To compensate for accident scenarios, the aircraft uses remaining control surface authority and differential thrust from the engines. To the best of our knowledge this is the first time real time bioelectric fine-grained control, differential thrust based control, and neural network damage adaptive control have been integrated into a single flight demonstration. The paper describes the EMG pattern recognition system and the bioelectric pattern recognition methodology.
This paper presents recent results in neuroelectric pattern recognition of electromyographic (EMG) signals used to control virtual computer input devices. The devices are designed to substitute for the functions of both a traditional joystick and keyboard entry method. We demonstrate recognition accuracy through neuroelectric control of a 757 class simulation aircraft landing at San Francisco International Airport using a virtual joystick as shown. This is accomplished by a pilot closing his fist in empty air and performing control movements that are captured by a dry electrode array on the arm which are then analyzed and routed through a flight director permitting full pilot outer loop control of the simulation. We then demonstrate finer grain motor pattern recognition through a virtual keyboard by having a typist tap his traders on a typical desk in a touch typist position. The EMG signals are then translated to keyboard presses and displayed. The paper describes the bioelectric pattern recognition methodology common to both examples. Figure 2 depicts raw EMG data from typing, the numeral '8' and the numeral '9'. These two gestures are very close in appearance and statistical properties yet are distinguishable by our hidden Kharkov model algorithms. Extensions of this work to NASA emissions and robotic control are considered.
Geosynchronous Synthetic Aperture Radar (GeoSAR) is a consortium project consisting of The Jet Propulsion Laboratory (JPL), Calgis (a small GIS company based in Fresno, CA) and the California Department of Conservation with funding provided by Defense Research Projects Agency (DARPA) started in November 1996. The two main objectives of the GeoSAR Program are: 1) To develop a state of the art dual frequency interferometric radar mapping instrument capable of mapping the true ground surface height beneath the vegetation canopy; and 2) To transition this mapping technology to a commercial company, Calgis. JPL, the technical lead, has the following program deliverables at program completion in November 1999 include radar design and radar hardware for X-band (3 cm) and P-band (83 cm) radars, processor software, hardware and documentation, and calibrated X-band radar.
Cassini Radar is a multimode rada instrument designed to probe the optically inaccessible surface of Titan, Saturn's largest moon. The individual modes will allow surface imaging, surface emissivity measurements. Recently, the breadboard model of this instrument was built and has undergone a series of functional and perfomance tests. The results obtained from these tests indicate that the instrument design is satisfactory and that the various required performance parameters are suffieciently met.