Publications

A monolithic two-dimensional array of surface-emitting AlGaAs diode lasers with dry-etched vertical facets and parabolic deflecting mirrors was mounted junction-side up on a W / Cu microchannel heatsink and evaluated under continuous-wave (CW) operating conditions. Both the facets and parabolic deflecting mirrors were etched using chlorine ion-beam-assistd etching. Threshold current densities of different sections of the array were consistently around 240 A/cm (to the second power), and measured CW differential quantum efficiencies were in the 46-48% range. CW power densities as high as 148 W/cm (to the second power) were achieved with an average temperature rise of less than 25 degrees C in this junction-side-up configuration.

The Federal Aviation Administration's Terminal Doppler Weather Radar (TDWR) system was primarily designed to address the operational needs of pilots in the avoidance of low-altitude wind shears upon takeoff and landing at airports. One of the primary methods of wind-shear detection for the TDWR system is the gust-front detection algorithm. The algorithm is designed to detect gust fronts that produce a wind-shear hazard and/or sustained wind shifts. It serves the hazard warning function by providing an estimate of the wind-speed gain for aircraft penetrating the gust front. The gust-front detection and wind-shift algorithms together serve a planning function by providing forecasted gust-front locations and estimates of the horizontal wind vector behind the front, respectively. This information is used by air traffic managers to determine arrival and departure runway configurations and aircraft movements to minimize the impact of wind shifts on airport capacity. This paper describes the gust-front detection and wind-shift algorithms to be fielded in the initial TDWR systems. Results of a quantitative performance evaluation using Doppler radar data collected during TDWR operational demonstrations at the Denver, Kansas City, and Orlando airports are presented. The algorithms were found to be operationally useful by the FAA airport controllers and supervisors.

Oscillatory signals that have both an amplitude-modulation (AM) and a frequency-modulation (FM) structure are encountered in almost all communication systems. We have also used these structures recently for modeling speech resonances, being motivated by previous work on investigating fluid dynamics phenomena during speech production that provide evidence for the existence of modulations in speech signals. In this paper, we use a nonlinear differential operator that can detect modulations in AM-FM signals by estimating the product of their time-varying amplitude and frequency. This operator essentially tracks the energy needed by a source to produce the oscillatory signal. To solve the fundamental problem of estimating both the amplitude envelope and instantaneous frequency of an AM-FM signal we develop a novel approach that uses nonlinear combinations of instantaneous signal outputs from the energy operator to separate its output energy product into its amplitude modulation and frequency modulation components. The theoretical analysis is done first for continuous-time signals. Then several efficient algorithms are developed and compared for estimating the amplitude envelope and instantaneous frequency of discrete-time AM-FM signals. These energy separation algorithms are then applied to search for modulations in speech resonances, which we model using AM-FM signals to account for time-varying amplitude envelopes and instantaneous frequencies. Our experimental results provide evidence that bandpass filtered speech signals around speech formants contain amplitude and frequency modulations within a pitch period. Overall, the energy separation algorithms, due to their very low computational complexity and instantaneously-adapting nature, are very useful in detecting modulation patterns in speech and other time-varying signals.

The lift-generated wake vortices trailing behind an aircraft present a danger to aircraft following the same or nearby path. The degree of hazard to the following aircraft depends on the nature of the wake encountered in its flight path and on the ability of the aircraft to counter its effects. This report describes the current state of understanding of the factors that influence the motion and dissipation of wake vortices. The relationships of these factors to parameters that are measurable through meteorological sensors and from a priori knowledge of the vortex generating aircraft characteristics are discussed as an aid to structuring development plans for the creation of wake vortex advisory products by the Integrated Terminal Weather System (ITWS) and by special wake vortex sensors.

A new Automatic Dependent Surveillance (ADS) system concept combining GPS satellite navigation with Mode S data communications is described. Several potential applications of this concept are presented with emphasis on surface surveillance at airports. The navigation and data link performance are analyzed. Compact ADS position formats are included. The results of the first tests at Hanscom Field demonstrating the feasibility of the spontaneous broadcast of ADS positions using Mode S messages are presented. Test aircraft, vehicles, avionics equipment and the ground system configuration are described. Avionics standards and GPS interface requirements are discussed. Multipath and airport surface coverage issues are addressed. Plans for further testing in an operational environment at Logan Airport are outlined.

Hazardous weather in the terminal area is the major cause of aviation system delays as well as a principal cause of air carrier accidents. Several systems presently under development will provide significant increases in terminal safety. However, these systems will not make a major impact on weather-induced delays in the terminal area, meet a number of the safety needs (such as information to support ground deicing decisions), or reduce the workload of the terminal controller. The Integrated Terminal Weather System (ITWS) will provide improved aviation weather information in the allocated TRACON area (up to 50 nmi from the airport) by integrating data and products from various Federal Aviation Administration (FAA) and National Weather Service (NWS) sensors and weather information systems. The data from these sources will be combined to provide a unified set of safety and planning weather products for pilots, controllers, and terminal area traffic managers. by using data from multiple sensors, ITWS can generate important new products where no individual sensor alone could generate a single, reliable product. In other instances, use of data from several sources can compensate for erroneous data from one sensor and thus improve the overall integrity of existing products. Major objectives of the ITWS program are to increase the effective airport acceptance rate in adverse weather by rpoviding information to support terminal automation systems, better terminal route planning, and wake vortex advisory services, and to reduce the need for controllers to communicate weather information to pilots via VHF voice. This report summarizes the work acocmplished during fiscal year 1992 on the development of the ITWS initial operational capability products; functional prototype design; operation of testbeds to acquire data for product development and testing; operation evaluation of products by ATC users; investigation of approaches for effective transfer of the technology to the production contractor; transfer of products to pilots via digital data links; and technical support for the ITWS documents required by the General Accounting Office (GAO).

GPS and GLONASS employ different geocentric Cartesian coordinate frames to express the positions of their satellites and, therefore, of their users. GPS uses WGS84; GLONASS, SGS85. Interest in the civil aviation community in using signals from both systems requires that a transformation between the two coordinate frames be determined. We present an estimate of the SGS85--WGS84 transformation.

The Meteorological Data Collection and Reporting System (MDCRS) was designed for the Federal Aviation Administration (FAA) and the National Weather Service (NWS) to collect, decode, store and disseminate aircraft meteorological observations. The system, targeted primarily at improving upper air wind forecasts, was fielded in 1991.

Low level wind shear has been identified as a cause or contributing factor in a significant number of aviation accidents. Research has shown that the most dangerous type of wind shear is the microburst (Fujita, et al., 1977 and 1979). Briefly, a microburst is an intense local downdraft that results in a strong divergent outflow near the surface. The diameter of the outflow region may vary from 3 to 10 Km. Although many of these accidents were nonfatal, six of them resulted in a total of 550 lives lost. During the past 17 years, the mainstay of the effort by the Federal Aviation Administration (FAA) to provide wind shear warnings to pilots has been the Low Level Wind Shear Alert System (LLWAS). The system has been redesigned, based on extensive operational experience and new knowledge about the nature of the aviation wind shear hazard (Goff and Gramzow, 1989). In parallel development, the Terminal Doppler Weather Radar (TDWR) has provided a capable alternative for ground-based microburst detection (Turnbull, et al., 1989). Recent studies on the integration of LLWAS with TDWR have established the value of a combined TDWR/LLWAS wind shear detection system (Cole and Todd, 1993) The LLWAS system is being developed in four phases, I, II, III, and IV, which reflect the chronology of operational deployments. The original LLWAS, now called LLWAS I, was designed for the detection of frontal shears under the assumption that hazardous wind shear is associated with large-scale meteorological features (Goff and Gramzow, 1989). This system was deployed at 110 airports between 1977 and 1987. LLWAS I had no microburst detection capability and had excessive false alerts. LLWAS II was developed to reduce the false alert rate of LLWAS I and to provide a modest microburst detection capability. It is a direct response to recommendations by the National Research Council (NRS-NAS, 1983), following the 1982 microburst crash in New Orleans. This upgrade, deployed by modifying the software in LLWAS I, provided an improvement that would not suffer the delays and costs of the major construction that is required for off-airport LLWAS III sensors. These upgrades to LLWAS I were installed between 1988 and 1991. LLWAS II will be the operational wind shear detection system at many airports until the late '90s. LLWAS III was developed in response to the requirements that LLWAS have a microburst detection capability (NRS-NAS, 1983). This system was designed by a combination of computer simulation studies (Wilson and Flueck, 1986) and a successful field test of a prototype at Stapleton International Airport, Denver in Augist 1987 (Smythe, et al., 1989 and Wilson et al., 1991). LLWAS III combines a dense sensor network and a sophisticated Wind Shear/Microburst (WSMB) detection algoritohm to provide a substantial microburst detection capability. The prototype LLWAS III has continued to operate at Stapleton International Airport, Denver since 1987 and has been credited with the "save" of a commercial airliner on July 8, 1989. Nine LLWAS IIIs are being installed this year. LLWAS IV will be deployed at 83 airports in the late '90s. The LLWAS IV wind shear and microburst detection algorithms will be identical to LLWAS III. This system features a full hardware upgrade. Major imporvements include an ice-free sensor and hardware that is more reliable and maintainable. This report provides an evaluation of the effectiveness of LLWAS II and LLWAS III. The TDWR operational test bed at Orlando International Airport, Orlando (MCO) provides a unique data set for this evaluation. This test-bed features data from a 14-sensor LLWAS, the prototype TDWR, FL-2C, operated by MIT/LL, and the University of North Dakota meteorolgical radar (UND). Data from this test bed in the summers of 1991 and 1992 are used to provide an evaluation of LLWAS II and LLWAS III. Since LLWAS IV uses the same wind shear detection algorithm, it is expected that LLWAS III and LLWAS IV will have comparable wind shear detection capabilities.

In recent years a number of aircraft accidents have resulted from a small scale, low altitude wind shear phenomena known as a microburst. Microbursts are produced within thunderstorms and are characterized by intense downdrafts which spread out after impacting the earth's surface, displaying strong divergent outflows of wind. They are often associated with heavy rainfall, but can occur without surface rainfall (Wolfson, 1988). The Terminal Doppler Weather Radar (TWDR) program is the first system developed to detect microbursts from a ground-based radar in the airport terminal area. Improving safety is its primary goal, and test operations in Denver, Kansas City, and Orlando have shown it to be highly successful in identifying microbursts. In general, this identification has been performed with a > 90% probability of Detection (POD) and a < 10% Probability of False Alarm (PFA) (Merritt et. al., 1989). The Integrated Terminal Weather System (ITWS) will introduce several new low-level wind shear products. These products include the Microburst Prediction product, the Microburst Trend product, and an improved Microburst Detection Product. The Microburst prediction product will provide estimates of the future location, onset time, and peak intensity of microbursts before their surface effects are evident (Wolfson et. al., 1993). The Microburst Trend product is responsible for warning users about expected increases, over a two minute interval, in wind shear intensity along the approach and departure corridors of a runway. This two minute time period approximates the delay between pilot receipt of an alert and the time of actual encounter with the event. The trend product should serve to improve pilot information when making decisions involving a wind shear event. This is particularly important for currently weak, but rapidly intensifying, wind shears. The Improved Microburst Detection Algorithm being developed under the ITWS program attempts to build on the performance of the TDWR Microburst algorithm by improving POD and PFA and providing fiier localization capabilities. More importantly, enhancements to the TDWR algorithm are necessary in order to 1. provide a consistent input to the microburst trend algorithm. 2. closely relate the microburst alert to the energy loss that the aircraft will actually experience and to alerts from an on-board forward-looking Doppler radar. The TDWR algorithm does a good job detecting the microburst impacted airspace, but makes no attempt to deduce the number and centers of the events. Since the resultant alert shapes are uncorrelated over time, performing a more detailed meteorological analysis, such as location tracking, and size and intensity projections required by the microburst trend product, are compromised. This motivating factor for the improved Microburst Detection Algorithm is discussed in more detail in other works (Dasey. 1993a. Dasey, 1993b). The focus of this paper is on the second motivating factor listed above: relating the microburst alert more closely with actual aircraft performance. Much of this understanding has evolved from the analysis of data from instrumented aircraft penetrations of microbursts within the Orlando terminal area, coincident withTDWR testbed operation (Matthews and Berke, 1993.Campbell et. al., 1992). The microburst penetration flights were conducted by NASA Langley, the University of North Dakota (UND), and several manufacturers of forward-looking wind shear detection systems, including Bendix, Rockwell-Collins, and Westinghouse. Use of this data has allowed comparison of the alert representation from the TDWR Microburst algorithm with that of the initial ITWS algorithm in terms of its relationship with aircraft performance. Section 2. describes a wind shear hazard index, called the F Factor, and its estimation from a ground-based Doppler radar. The estimated F Factors from the TDWR alert shapes are described in section 3. Direct use of TDWR base data for computing shear is explored in section 4, as is the correlation of that data with aircraft F Factor measurements. Estimation of the F Factor from alert shapes output from the initial ITWS detection algorithm is explored in section 5. Section 6 examines the results and emphasizes future research.