Showing papers presented at "IEEE Aerospace Conference in 2007"

Global Path Planning on Board the Mars Exploration Rovers

Abstract: In January 2004, NASA's twin Mars exploration rovers (MERs), spirit and opportunity, began searching the surface of Mars for evidence of past water activity. In order to localize and approach scientifically interesting targets, the rovers employ an on-board navigation system. Given the latency in sending commands from Earth to the Martian rovers (and in receiving return data), a high level of navigational autonomy is desirable. Autonomous navigation with hazard avoidance (AutoNav) is currently performed using a local path planner called GESTALT (grid-based estimation of surface traversability applied to local terrain). GESTALT uses stereo cameras to evaluate terrain safety and avoid obstacles. GESTALT works well to guide the rovers around narrow and isolated hazards, however, it is susceptible to failure when clusters of closely spaced, non-traversable rocks form extended obstacles. In May 2005, a new technology task was initiated at the Jet Propulsion Laboratory to address this limitation. A version of the Carnegie Mellon University Field D* global path planner has been integrated into MER flight software, enabling simultaneous local and global planning during AutoNav. A revised version of AutoNav was uploaded to the rovers during the summer of 2006. This paper describes how global planning was integrated into the MER flight software, and presents results of testing the improved AutoNav system using the MER Surface System TestBed rover.

The Silent Force Multiplier: The History and Role of UAVs in Warfare

Abstract: Many don't realize that the history of UAVs started nearly a century ago, and that the modern era of UAVs goes back nearly four decades. It often goes unnoticed that UAV development and employment is being pursued by more than 50 countries world-wide. This paper examines the history of unmanned aerial vehicles including the initial concepts and employment and the role of UAVs in warfare, providing examples from several conflicts throughout the world. An analysis of the reasons for the underutilization of UAV capabilities, the reasons why UAV missions have been successful, and basis for continuing to fund UAV development and employment is given. Finally, current UAV trends are discussed.

Autonomous Precision Landing and Hazard Detection and Avoidance Technology (ALHAT)

Abstract: As NASA plans to send humans back to the Moon and develop a lunar outpost, technologies must be developed to place humans and cargo safely, precisely, repeatedly, on the lunar surface with the capability to avoid surface hazards. Exploration Space Architecture Study requirements include the need for global lunar surface access with safe, precise landing without lighting constraints on terrain that may have landing hazards for human scale landing vehicles. Landing accuracies of perhaps 100's of meters for sortie crew missions to 10's of meters for Outpost class missions are required. The Autonomous precision Landing Hazard Avoidance Technology (ALHAT) project will develop the new and unique descent and landing Guidance, Navigation and Control (GNC) hardware and software technologies necessary for these capabilities. The ALHAT project will qualify a lunar descent and landing GNC system to a Technology Readiness Level (TRL) of 6 capable of supporting lunar crewed, cargo, and robotic missions. The (ALHAT) development project was chartered by NASA Headquarters in October 2005. This paper describes the project development, research, analysis and concept evolution that has occurred since the assignment of the project. This includes the areas of systems engineering, GNC, sensors, sensor algorithms, simulations, fielding testing, laboratory testing, Hardware-In-The-Loop testing, system avionics and system certification concepts. This paper provides a high level overview of the background, the program management plan, and the current status of the ALHAT project.

NASA World Wind: Opensource GIS for Mission Operations

Abstract: This paper describes NASA World Wind, its technical architecture and performance, and its emerging use for mission operations. World Wind is a geographic information system that provides graphical access to terabytes of imagery and elevation models for planets and other celestial objects including satellite and other data of the Earth, Moon, Mars, Venus, and Jupiter; as well as astronomical data made available through the Sloan Digital Sky Survey. World Wind is also a customizable system that can be integrated as part of other applications. World Wind is not only an application in which add-ons can be integrated, but is also being developed as a plugin that can be integrated with other applications. This paper also describes the significant contributions of the international opensource community in making World Wind what it is today. Contributions have involved the following: 1) lead development of add-ons, several of which have been integrated as part of the core system available for direct download via sourceforge, 2) lead provider of high-resolution data sets, 3) lead help desk support through Internet relay chat for end-users and developers, and 4) significant technical contributions to the core system including bug identification, tracking and resolution as well as ideas for new features and source code modifications.

Mars Science Laboratory: Entry, Descent, and Landing System Performance

Abstract: In 2010, the Mars Science Laboratory (MSL) mission will pioneer the next generation of robotic entry, descent, and landing (EDL) systems, by delivering the largest and most capable rover to date to the surface of Mars. To do so, MSL will fly a guided lifting entry at a lift-to-drag ratio in excess of that ever flown at Mars, deploy the largest parachute ever at Mars, and perform a novel Sky Crane maneuver. Through improved altitude capability, increased latitude coverage, and more accurate payload delivery, MSL is allowing the science community to consider the exploration of previously inaccessible regions of the planet.

Communications for Integrated Modular Avionics

Abstract: The aerospace industry has been adopting avionics architectures to take advantage of advances in computer engineering. Integrated Modular Avionics (IMA), as described in ARINC 653, distributes functional modules into a robust configuration interconnected with a "virtual backplane" data communications network. Each avionics module's function is defined in software compliant with the APEX Application Program Interface. The Avionics Full-Duplex Ethernet (AFDX) network replaces the point-to-point connections used in previous distributed systems with "virtual links". This network creates a command and data path between avionics modules with the software and network defining the active virtual links over an integrated physical network. In the event of failures, the software and network can perform complex reconfigurations very quickly, resulting in a very robust system. In this paper, suitable architectures, standards and conceptual designs for IMA computational modules and the virtual backplane are defined and analyzed for applicability to spacecraft. The AFDX network standard is examined in detail and compared with IEEE 802.3 Ethernet. A reference design for the "Ancillary Sensor Network" (ASN) is outlined based on the IEEE 1451 "Standard for a Smart Transducer Interface for Sensors and Actuators" using realtime operating systems, time deterministic AFDX and wireless LAN technology. Strategies for flight test and operational data collection related to Systems Health Management are developed, facilitating vehicle ground processing. Finally, a laboratory evaluation defines performance metrics and test protocols and summarizes the results of AFDX network tests, allowing identification of design issues and determination of ASN subsystem scalability, from a few to potentially thousands of smart and legacy sensors.

Performance Comparison of Rock Detection Algorithms for Autonomous Planetary Geology

Abstract: Detecting rocks in images is a valuable capability for autonomous planetary science. Rock detection facilitates selective data collection and return. It also assists with image analysis on Earth. This work reviews seven rock detection algorithms from the autonomous science literature. We evaluate each algorithm with respect to several autonomous geology applications. Tests show the algorithms' performance on Mars Exploration Rover imagery, terrestrial images from analog environments, and synthetic images from a Mars terrain simulator. This provides insight into the detectors' performance under different imaging conditions.

Self-Supervised Classification for Planetary Rover Terrain Sensing

Abstract: Autonomous mobility in rough terrain is key to enabling increased science data return from planetary rover missions. Current terrain sensing and path planning approaches can be used to avoid geometric hazards, such as rocks and steep slopes, but are unable to remotely identify and avoid non-geometric hazards, such as loose sand in which a rover may become entrenched. This paper proposes a self-supervised classification approach to learning the visual appearance of terrain classes which relies on vibration-based sensing of wheel-terrain interaction to identify these terrain classes. Experimental results from a four-wheeled rover in Mars analog terrain demonstrate the potential for this approach.

Vibrating antennas and compensation techniques Research in NATO/RTO/SET 087/RTG 50

Abstract: Array antennas which are integrated onto structures of aircraft and unmanned aerial vehicles (UAVs) are subject to unsteady aerodynamic loads. Mechanical forces and these aerodynamic loads will cause deformation of the antenna supporting structure. As a consequence, the positions and orientations of the elements of the phased array antenna change. The relative phases of the respective signals feeding the antennas will vary, and as a consequence the antenna radiation pattern is affected: the main beam direction can change and the beam width and/or side lobe levels can increase. The influence of deformations and vibrations will be most significant on array antennas, which are large in terms of wavelength (high gain antennas). The objective of the present paper is to present some applications of such array antennas, and to discuss the effects of deformations and vibrations on the performance of array antennas, and to describe technology to counteract these effects by means of adaptive or synthetic beam forming.

Analysis and Emulation of FM Radio Signals for Passive Radar

Abstract: Due to its high power levels provided, and its wide coverage, FM radio could be a good opportunity transmitter for passive coherent location (PCL) radar systems. In this paper we study the effectiveness of FM signals as radar waveforms by means of simulated and real data analysis. To this purpose, an emulation of FM radio transmitter is presented, whose outputs are compared with real FM data collected by an experimental digital receiver. In this way, we also achieve a reliable instrument to optimize target detection performance by a successive adaptive signal processing. To complete the analysis of opportunity waveforms, the signals' self-ambiguity functions and spectra are evaluated, so it is possible to improve the knowledge of how to select the most appropriate FM channel. Since emulated data differ from real data in means of the transmission channel, a statistical analysis of the real channel is presented.

Terrain Classification and Classifier Fusion for Planetary Exploration Rovers

Abstract: Knowledge of the physical properties of terrain surrounding a planetary exploration rover can be used to allow a rover system to fully exploit its mobility capabilities. Here a study of multi-sensor terrain classification for planetary rovers in Mars and Mars-like environments is presented. Two classification algorithms for color, texture, and range features are presented based on maximum likelihood estimation and support vector machines. In addition, a classification method based on vibration features derived from rover wheel-terrain interaction is briefly described. Two techniques for merging the results of these "low-level" classifiers are presented that rely on Bayesian fusion and meta-classifier fusion. The performance of these algorithms is studied using images from NASA's mars exploration rover mission and through experiments on a four-wheeled test-bed rover operating in Mars-analog terrain. It is shown that accurate terrain classification can be achieved via classifier fusion from visual and tactile features.

Application of Prognostic Health Management in Digital Electronic Systems

Abstract: Development of robust prognostics for digital electronic system health management will improve device reliability and maintainability for many industries with products ranging from enterprise network servers to military aircraft. Techniques from a variety of disciplines is required to develop an effective, robust, and technically sound health management system for digital electronics. The presented technical approach integrates collaborative diagnostic and prognostic techniques from engineering disciplines including statistical reliability, damage accumulation modeling, physics of failure modeling, signal processing and feature extraction, and automated reasoning algorithms. These advanced prognostic/diagnostic algorithms utilize intelligent data fusion architectures to optimally combine sensor data with probabilistic component models to achieve the best decisions on the overall health of digital components and systems. A comprehensive component prognostic capability can be achieved by utilizing a combination of health monitoring data and model-based estimates used when no diagnostic indicators are present. Both board and component level minimally-invasive and purely internal data acquisition methods will be paired with model-based assessments to demonstrate this approach to digital component health state awareness.

Prognostics and Health Management A Data-Driven Approach to Supporting the F-35 Lightning II

Abstract: The Joint Strike Fighter (JSF) Program has developed an aggressive vision for and has specified a very stringent set of requirements for a comprehensive Prognostics and Health Management (PHM) system. This vision and the associated specified requirements has resulted in the development of perhaps the most advanced and comprehensive set of diagnostic, prognostic, and health management capabilities yet to be applied to an aviation platform. These PHM capabilities are currently being developed and integrated into the JSF Air System design. This paper will provide a top level overview summary of the current PHM concept, design, and architecture; provide a general capabilities description; and discuss overall program status. Some specific examples of system benefits, design trade-off decisions, and system integration challenges will be discussed. Particular examples of some subsystem prognostic capabilities will also be discussed.

Target Tracking by Multiple Particle Filtering

Abstract: In this paper we address the problem of tracking of multiple targets in a wireless sensor network using particle filtering. This methodology approximates the probability distributions of the objects of interest by using random measures composed of particles and associated weights. An important challenge of the resulting algorithms is the need for very large number of particles when the dimensions of the states are even moderately large. We propose to combat this problem by alternative particle filtering implementations where we partition the state space of the system into different subspaces and run a separate particle filter for each subspace. The performance of the considered algorithm is illustrated through computer simulations that show considerable advantage of the proposed method over the standard particle filter.

Evolution of a UAV Autonomy Classification Taxonomy

Abstract: At PerMIS 2002, the Air Force Research Lab presented a paper entitled "Metrics Schmetrics", which presented the results of a research study on how to determine the autonomy level of a unmanned aerial vehicle (UAV). The striking point made by the author, given the obvious importance of the need to effectively evaluate and classify autonomous algorithms, was the general dearth of existing effective metrics and/or taxonomies to determine a UAVs autonomy level. To fill the void, the author presented a framework, referred to as the AFRL Autonomy Framework, which identified 10 autonomy control levels (ACLs) and presented the characteristics that differentiate the various levels. The purpose of this paper is to examine how the metrics presented have been applied and how they have evolved and expanded, in theory and practice, as a result of lessons learned during those applications. Specifically, the evolution in metrics development has culminated with the advent of an evaluation technique that blends emerging simulation technologies to create new unmanned aerial vehicle (UAV) autonomy assessment methods which take full advantage of visual virtual environments and statistical constructive simulations to examine the autonomy algorithm along the four dimensions of the Observe, Orient, Decide, Act (OODA) loop commonly applied by military aviators during the decision making process. This approach has been used over the last three years to evaluate emerging autonomy technologies from the Army's Unmanned Autonomous Collaborative Operations (UACO) Science and Technology program. This unique technique for testing UAV autonomy effectiveness is a significant advance in the UAV community. As autonomy algorithms proliferate to the point where multiple candidate algorithms are available for each platform, the ability to characterize the effectiveness of each autonomy algorithm will be critical to further for successful implementation of autonomous capability.

Gaussian Particle Implementations of Probability Hypothesis Density Filters

Abstract: The probability hypothesis density (PHD) filter is a multiple-target filter for recursively estimating the number of targets and their state vectors from sets of observations. The filter is able to operate in environments with false alarms and missed detections. Two distinct algorithmic implementations of this technique have been developed. The first of which, called the Particle PHD filter, requires clustering techniques to provide target state estimates which can lead to inaccurate estimates and is computationally expensive. The second algorithm, called the Gaussian Mixture PHD (GM-PHD) filter does not require clustering algorithms but is restricted to linear-Gaussian target dynamics, since it uses the Kalman filter to estimate the means and covariances of the Gaussians. Extensions for the GM-PHD filter allow for mildly non-linear dynamics using extended and Unscented Kalman filters. A new particle implementation of the PHD filter which does not require clustering to determine target states is presented here. The PHD is approximated by a mixture of Gaussians, as in the GM-PHD filter but the transition density and likelihood function can be non-linear. The resulting filter no longer has a closed form solution so Monte Carlo integration is applied for approximating the prediction and update distributions. This is calculated using a bank of Gaussian particle filters, similar to the procedure used with the Gaussian sum particle filter. The new algorithm is derived here and presented with simulated results.

Tier-Scalable Reconnaissance Missions For The Autonomous Exploration Of Planetary Bodies

Abstract: A fundamentally new (scientific) reconnaissance mission concept, termed tier-scalable reconnaissance, for remote planetary (including Earth) atmospheric, surface and subsurface exploration recently has been devised that soon will replace the engineering and safety constrained mission designs of the past, allowing for optimal acquisition of geologic, paleohydrologic, paleoclimatic, and possible astrobiologic information of Venus, Mars, Europa, Ganymede, Titan, Enceladus, Triton, and other extraterrestrial targets. This paradigm is equally applicable to potentially hazardous or inaccessible operational areas on Earth such as those related to military or terrorist activities, or areas that have been exposed to biochemical agents, radiation, or natural disasters. Traditional missions have performed local, ground-level reconnaissance through rovers and immobile landers, or global mapping performed by an orbiter. The former is safety and engineering constrained, affording limited detailed reconnaissance of a single site at the expense of a regional understanding, while the latter returns immense datasets, often overlooking detailed information of local and regional significance.

Seeded Failure Testing and Analysis of an Electro-Mechanical Actuator

Abstract: An accelerated wear test program for an electromechanical actuator (EMA) was funded and conducted by Parker Aerospace and Lockheed Martin Aeronautics Company. Testing was performed at Dynamic Controls, Inc. The objective of the program was to identify failure pre-cursors that exhibited repeatable trends, and could be used to construct a remaining useful life algorithm with an identifiable confidence level. Selected mechanical components of the actuator were seeded with an abrasive contaminant to achieve accelerated wear. The desired goal was to achieve component wearout in approximately 24 hours, providing acceptable test times with pre-cursor signatures representative of normal wearout. The test facility and approach is described, along with failure criteria and test results. Although the scope of this effort was relatively limited, it revealed some potentially useful wear indicators, along with many areas of difficulty for which further effort is recommended.

Finding Hyperspectral Anomalies Using Multivariate Outlier Detection

Abstract: This research demonstrates the adverse implications of using non-robust statistical methods for detecting anomalies in hyperspectral image data, and proposes the use of multivariate outlier detection methods as an alternative detection strategy. Existing outlier detection methods are adapted for use in a hyperspectral image context, and their performance is compared to the benchmark RX detector and a cluster-based anomaly detector. Tests conducted using both simulated data and actual hyperspectral imagery indicate that multivariate outlier detection methods can achieve superior detection performance relative to current non-robust detection methods.

An Enhanced Prognostic Model for Intermittent Failures in Digital Electronics

Abstract: This paper presents an enhanced prognostic model to predict remaining useful life. The model utilizes environmental loads and in-situ performance measurements in conjunction with two baseline prediction algorithms: life consumption monitoring (LCM) and uncertainty adjusted prognostics (UAP). Fusion techniques are then utilized to integrate the two prognostic algorithms. A key and unique value of this combined prognostic model is its ability to assess intermittent as well as "hard" failures. In the paper we show how it has been validated for intermittent and "hard" solder joint interconnect failures under temperature cycling loads.

Classification of Damage Signatures in Composite Plates using One-Class SVMs

Abstract: Damage characterization through wave propagation and scattering is of considerable interest to many non-destructive evaluation techniques. For fiber-reinforced composites, complex waves can be generated during the tests due to the non-homogeneous and anisotropic nature of the material when compared to isotropic materials. Additional complexities are introduced due to the presence of the damage and thus results in difficulty to characterize these defects. The inability to detect damage in composite structures limits their use in practice. A major task of structural health monitoring is to identify and characterize the existing defects or defect evolution through the interactions between structural features and multidisciplinary physical phenomena. In a wave-based approach to addressing this problem, the presence of damage is characterized by the changes in the signature of the resultant wave that propagates through the structure. In order to measure and characterize the wave propagation, we use the response of the surface-mounted piezoelectric transducers as input to an advanced machine-learning based classifier known as a support vector machine.

Co-operative Localisation and Mapping for Multiple UAVs in Unknown Environments

Abstract: In this paper we demonstrate a co-operative path-planning algorithm for multi-vehicle simultaneous localisation and mapping (SLAM) that uses information-based measures to maximize the accuracy of a feature map which is constructed from terrain observation made by each vehicle. The SLAM algorithm is distributed amongst the vehicles where each vehicle shares locally built map information via a central communications node. This information is used to assist in localisation which in turn increases the accuracy of the map information each vehicle provides. Each vehicle communicates to the central node potential trajectories it can take and the associated map information it will provide. The central communications node then co-ordinates the actions of each platform such as to maximise the accuracy of the globally constructed map. The vehicles each perform SLAM using a combination of on-board inertial sensors and an on-board terrain sensor. Results are presented using a 6-DoF simulation of several UAVs over an initially unexplored terrain.

Guidance and Control Design for Powered Descent and Landing on Mars

Abstract: Future Mars landing missions must be capable of autonomously delivering highly capable and mobile rovers safely and gently in an upright orientation. The airbag landing system used to deliver earlier rovers (Mars Pathfinder and the two Mars Exploration vehicles) is incapable of landing the Mars Science Laboratory (MSL)-class rover. The design of a novel sky-crane landing concept to land the proposed Mars Science Laboratory rover is presented here. The descent is guided and actively controlled in six degrees of freedom. Terminal guidance is robust to terrain variations-induced altimeter noise. A terminal descent sensor provides surface relative velocity and altitude measurements, the inertial measurement unit measurements help propagate the vehicle attitude and positions. Guidance and control system commands eight throttle-able Mars lander engines to actively control the vehicle attitude and translations. Computer simulations demonstrate the viability of this concept in the presence of various environmental, configuration, and hardware imperfections.

An Open System Architecture for Condition Based Maintenance Overview

Abstract: The Boeing Company is developing an open integrated vehicle health management (IVHM) architecture; Smiths aerospace is extending and applying it. The charter of the Boeing phantom works is to provide tools and assets that enable next generation vehicles to be more reliable, efficient, capable, and autonomous. In support of those goals Integrated vehicle health management (IVHM) is receiving increased attention. The open system architecture for condition based maintenance (OSACBM) is a standard for building IVHM applications that meet those goals. Boeing has created a software framework for developing generic tools based on OSACBM that support scaleable, efficient modules which simplify IVHM integration in two ways: First, the integration improves the IVHM software models, software algorithms, data, communications, and embedded processors. Second, integration facilitates the use of IVHM with command, control, communication, mission, flight, maintenance, and other vehicle major systems.

Regional Mineral Mapping By Extending Hyperspectral Signatures Using Multispectral Data

Abstract: Hyperspectral imaging (HSI) data in the 0.4 -2.5 micrometer (VNIR/SWIR) spectral range allow direct identification of minerals using their fully resolved spectral signatures, however, spatial coverage is limited. Multispectral Imaging data (MSI) (e.g. data from the Advanced Spaceborne Emission and Reflection Radiometer, ASTER) are spectrally undersampled and may not allow unique identification, but they do provide synoptic spatial coverage. Combining the two data types by modeling hyperspectral signatures to ASTER band passes allows extending HSI mapping results to regional scales and leads to improved mineral mapping over larger areas.

A Generalized Process for Optimal Threshold Setting in HUMS

Abstract: Monitoring the health of a helicopter drive train enhances flight safety and reduces operating costs. Health and usage management systems (HUMS) monitor the drive train by using accelerometers to measure component vibration. Algorithms process the time domain vibration data into various condition indicators (CI), which are used to determine component health via thresholding. For the rotating machinery, a standard set of CI are shaft order one, two and three (i.e. 1, 2 or 3 times the shaft RPM). Shaft order one (SOI) is indicative of an unbalance, where as higher shaft order can be used to detect a bent shaft or misalignment condition. In the case of bearings, CIs are envelope spectrum or cepstrum analysis of the ball, cage, inner race and outer race frequencies. There are a number of standard CI used for gear analysis, such as line elimination and resynthesis, side band modulation, gear misalignment, etc. In general, some method is used to set thresholds for these CIs: when the threshold is exceeded, maintenance is recommended. The HUMS system must balance the risk of setting the threshold too high such that a component may fail in flight versus the risk of setting the threshold too low, which results in additional maintenance cost. This paper covers a generalized process of optimally setting threshold for CI and fusing the information into an Health Indicator. It can be shown that the distributions of CI for shaft magnitude and bearing envelop energy are Rayleigh distribution. The normalized distance functions for these CIs are a Nakagami distribution with mu (shape parameter) of n (number of CI) and Omega (scale parameter) of 2 x 1/(2-pi/2) x mu. For gear CIs, which are considered as Gaussian, the normalized distance function is again Nakagami, but with a mu of nil and Omega of n. Given the theoretical mu and Omega, a threshold for any set of CI can be generated resulting in system probability of false alarm (PFA). This is an optimal decision rule for detecting a component which is no longer nominal. The normalized distance distribution is a function of the component CI sample statistic. Procedures are developed to calculate the unbiased statistic: covariance for Rayleigh based CIs and mean value/covariance for Gaussian based CIs. In the cases where the population of components is not nominal (e.g. mass imbalances which violate the Rayleigh assumption) tools are presented to control this. For gear, normalizing transforms can be used to ensure the CIs are more Gaussian. Example data from utility helicopters are given.

ALHAT System Architecture and Operational Concept

Abstract: An autonomous lunar landing system applicable to a wide variety of crewed and robotic lunar descent vehicles is under development as part of the ALHAT (autonomous precision landing and hazard detection and avoidance technology) project. This system, referred to as the ALHAT system module (ASM) is a highly advanced integrated sensor suite that enables landing a lunar descent vehicle within tens of meters of a certified and designated landing location anywhere on the Moon, under any lighting condition. This paper describes the basic ASM architecture and its novel concept of operations, and matures this architecture through description of top level lunar landing requirements. Working closely with NASA primary stakeholders, a fully developed ASM design will enable global lunar access for exploration of unique and challenging areas on the lunar surface never before visited.

Validating Prognostic Algorithms: A Case Study Using Comprehensive Bearing Fault Data

Abstract: The ultimate goal of prognostics is to accurately predict remaining useful life (RUL) based on sensor data, system usage, and prior knowledge of fault-to-failure progression rates (i.e. a model). One of the key components necessary for developing a prognostic algorithm is a diagnostic severity metric. This paper presents an evaluation of a number of standard vibration-based diagnostic metrics, utilizing a large set of experimental fault-to-failure progression data for bearings. These experiments included over 40 complete bearing failure progressions with 10 to 30 ground truth data points per bearing. Additional data supporting the potential of using oil debris monitoring in conjunction with vibration monitoring is also presented. Once a prognostic algorithm has been developed, the next critical step is to validate how well the algorithm performs. Conceptually, this seems like a simple task. However, there are many criteria to be considered, including convergence rate, accuracy, and stability of the RUL prediction. The paper includes an evaluation of prognostic algorithms based on vibration-based diagnostics that feed into a model-based prediction of future spall propagation and thus remaining life. Methods for objectively measuring the quality of the predictions are proposed. The results presented herein help demonstrate the capabilities and limitations of predictive prognostics at the current state-of-the-art.

Radiation Hardened FPGA Technology for Space Applications

Abstract: High performance, high density, radiation hardened Field Programmable Gate Arrays (FPGAs) are in great demand for military and space applications to reduce design cost and cycle time. BAE Systems has implemented radiation hardened 150nm bulk CMOS process technology in its foundry located in Manassas, VA to support such advanced product needs. BAE Systems and Actel Corporation are collaborating to bring the next-generation radiation hardened FPGA product for space applications to market. This paper will describe the rad hard AX-250 FPGA and the electrical and radiation test data on rad hard 150nm product hardware, FPGA device structures and anti-fuse arrays, as part of the overall FPGA product installation and qualification effort.

Multiple Target Tracking Using Maximum Likelihood Probabilistic Data Association

Abstract: The maximum likelihood-probabilistic data association (MLPDA) target tracking algorithm is effective in tracking very low observable targets. A key limitation of MLPDA is that it is restricted to tracking a single target. We derive and implement a multiple target version of MLPDA called Joint MLPDA (JMLPDA). While the JMLPDA implementation presented in this paper is focused on a two-target case, this algorithm is extensible to any number of targets. The MLPDA and JMLPDA algorithms are combined to form a multi-target MLPDA tracking algorithm. Performance of the JMLPDA and the multi-target MLPDA algorithms are compared to a probabilistic multi-hypothesis tracker (PMHT) for two crossing targets, focusing on track management/update. Simulation results show that under conditions of heavy clutter, the multi-target MLPDA outperforms PMHT in terms of reduced track errors and longer track life.