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Signal processing algorithms for the Terminal Doppler Weather Radar: Build 2
April 30, 2010
Project Report
Published in:
MIT Lincoln Laboratory Report ATC-363
Summary
As a new radar data acquisition system (RDA) was developed for the Terminal Doppler Weather Radar (TDWR), enhanced signal processing algorithms taking advantage of its increased capabilities were also developed. The primary goals of protecting the base data estimates from range-aliased signals and providing reliable velocity dealiasing were achieved through multiple pulse repetition interval (PRI) and phase coding methods. An innovative radial-by-radial adaptive selection process was used to take full advantage of the different techniques, the first time such an approach has been implemented for weather radars. Improvement in clutter filtering was also achieved. This report discusses in detail these new RDA signal processing algorithms.
Summary
As a new radar data acquisition system (RDA) was developed for the Terminal Doppler Weather Radar (TDWR), enhanced signal processing algorithms taking advantage of its increased capabilities were also developed. The primary goals of protecting the base data estimates from range-aliased signals and providing reliable velocity dealiasing were achieved through...
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Evaluation of enroute Convective Weather Avoidance Models based on planned and observed flight
January 18, 2010
Conference Paper
Published in:
14th Conf. on Aviation, Range, and Aerospace Meteorology, ARAM, 16-21 January 2010.
Summary
The effective management of convective weather in congested air space requires decision support tools that can translate weather information available to air traffic managers into anticipated impact on air traffic operations. The Convective Weather Avoidance Model (CWAM) has been under development at Lincoln Lab under sponsorship of NASA to develop a correlation between pilot behavior and observable weather parameters. To date, the observable weather parameters have been the Corridor Integrated Weather System (CIWS) high resolution Vertically Integrated Liquid (VIL) precipitation map and the CIWS Echo Top product. The CWAM was dependent upon a crude model to define pilot deviations based upon finding weather encounters and then comparing the distance between the planned and actual flight trajectories. Due to a large number of false deviations from this crude model, a significant amount of hand editing was required to use the database. This paper will focus on two areas of work to improve the performance of the enroute convective weather avoidance models. First, an improved automated algorithm to detect weather-related deviations that significantly reduces the percentage of false deviation detections will be presented. This new model includes additional information on each deviation, including the location the decision was made to deviate. The additional information extracted from this algorithm can be used to evaluate the conditions at the decision time which may impact the severity of weather pilots are willing to penetrate. The new deviation detection algorithm has also reduced the amount of hand editing required by removing short cuts taken to reduce the flight time, deviations that occur well past the decision time, and non-weather related reroutes. The second focus of this paper will be the comparison of three different convective weather avoidance models that have been proposed, based upon the analysis of an expanded database of flight deviations. Six weather impact days from 2007 and 2008 have been added to the existing case set from 2006, tripling the number of flight trajectories that can be used in validating the models. In addition to validating the existing CWAM, we will look at additional parameters that may improve the performance of the CWAM.
Summary
The effective management of convective weather in congested air space requires decision support tools that can translate weather information available to air traffic managers into anticipated impact on air traffic operations. The Convective Weather Avoidance Model (CWAM) has been under development at Lincoln Lab under sponsorship of NASA to develop...
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CompositeMatch: detecting n-ary matches in ontology alignment
October 25, 2009
Conference Paper
Published in:
OM 2009: Proc. 4th Int. Workshop on Ontology Matching, 25 October 2009, pp. 250-251.
Summary
The field of ontology alignment still contains numerous unresolved problems, one of which is the accurate identification of composite matches. In this work, we present a context-sensitive ontology alignment algorithm, CompositeMatch, that identifies these matches, along with the typical one-to-one matches, by looking more broadly at the information that a concept's relationships confer. We show that our algorithm can identify composite matches with greater confidence than current tools.
Summary
The field of ontology alignment still contains numerous unresolved problems, one of which is the accurate identification of composite matches. In this work, we present a context-sensitive ontology alignment algorithm, CompositeMatch, that identifies these matches, along with the typical one-to-one matches, by looking more broadly at the information that a...
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Moving clutter spectral filter for Terminal Doppler Weather Radar
October 6, 2009
Conference Paper
Published in:
34th Conf. on Radar Meteorology, 5-9 October 2009.
Summary
Detecting low-altitude wind shear in support of aviation safety and efficiency is the primary mission of the Terminal Doppler Weather Radar (TDWR). The wind-shear detection performance depends directly on the quality of the data produced by the TDWR. At times the data quality suffers from the presence of clutter. Al-though stationary ground clutter signals can be removed by a high-pass filter, moving clutter such as birds and roadway traffic cannot be attenuated using the same technique because their signal power can exist any-where in the Doppler velocity spectrum. Furthermore, because the TDWR is a single-polarization radar, polarimetry cannot be used to discriminate these types of clutter from atmospheric signals. The moving clutter problem is exacerbated at Western sites with dry microbursts, because their low signal-to-noise ratios (SNRs) are more easily masked by un-wanted moving clutter. For Las Vegas (LAS), Nevada, the offending clutter is traffic on roads that are oriented along the radar line of sight near the airport. The radar is located at a significantly higher altitude than the town, improving the visibility to the roads, and giving LAS the worst road clutter problem of all TDWR sites. The Salt Lake City (SLC), Utah, airport is located near the Great Salt Lake, which is the biggest inland staging area for migrating seabirds in the country. It, therefore, suffers from bird clutter, which not only can obscure wind shear signatures but can also mimic them to trigger false alarms. The TDWR "dry" site issues are discussed in more detail by Cho (2008). In order to mitigate these problems, we developed a moving clutter spectral filter (MCSF). In this paper we describe the algorithm and present preliminary test results.
Summary
Detecting low-altitude wind shear in support of aviation safety and efficiency is the primary mission of the Terminal Doppler Weather Radar (TDWR). The wind-shear detection performance depends directly on the quality of the data produced by the TDWR. At times the data quality suffers from the presence of clutter. Al-though...
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Automatic registration of LIDAR and optical images of urban scenes
June 20, 2009
Conference Paper
Published in:
CVPR 2009, IEEE Conf. on Computer Vision and Pattern Recognition, 20-25 June 2009, pp. 2639-2646.
Topic:
R&D group:
Summary
Fusion of 3D laser radar (LIDAR) imagery and aerial optical imagery is an efficient method for constructing 3D virtual reality models. One difficult aspect of creating such models is registering the optical image with the LIDAR point cloud, which is characterized as a camera pose estimation problem. We propose a novel application of mutual information registration methods, which exploits the statistical dependency in urban scenes of optical apperance with measured LIDAR elevation. We utilize the well known downhill simplex optimization to infer camera pose parameters. We discuss three methods for measuring mutual information between LIDAR imagery and optical imagery. Utilization of OpenGL and graphics hardware in the optimization process yields registration times dramatically lower than previous methods. Using an initial registration comparable to GPS/INS accuracy, we demonstrate the utility of our algorithm with a collection of urban images and present 3D models created with the fused imagery.
Summary
Fusion of 3D laser radar (LIDAR) imagery and aerial optical imagery is an efficient method for constructing 3D virtual reality models. One difficult aspect of creating such models is registering the optical image with the LIDAR point cloud, which is characterized as a camera pose estimation problem. We propose a...
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A wind forecast algorithm to support Wake Turbulence Mitigation for Departures (WTMD)
January 20, 2008
Conference Paper
Published in:
13th Conf. on Aviation, Range and Aerospace Meteorology, ARAM, 20-24 January 2008.
Summary
Turbulence associated with wake vortices generated by arriving and departing aircraft poses a potential safety risk to other nearby aircraft, and as such this potential risk may apply to aircraft operating on Closely Spaced Parallel Runways (CSPRs). Aircraft separation standards are imposed to mitigate this potential risk. The FAA and NASA are investigating application of wind-dependent procedures for improved departure operations that would safely reduce spacing restrictions to allow increased airport operating capacity. These procedures are referred to collectively as Wake Turbulence Mitigation for Departures (WTMD). An important component of WTMD is a Wind Forecast Algorithm (WFA) developed by MIT Lincoln Laboratory. The algorithm is designed to predict when runway crosswind conditions will remain persistently favorable to preclude transport of aircraft departure wakes into the path of aircraft on parallel runways (Figure 1). The algorithm has two distinct components for predicting the winds at the surface (33 ft) and aloft up to 1000 ft (the altitude by which an alternate form of separation would be applied by Air Traffic Control to aircraft departing the parallel runways, typically 15 degree or greater divergence in aircraft paths). The surface component forecast applies a statistical approach using recent observations of winds from 1-minute ASOS observations. The winds-aloft component relies on the 2 to 4 hour wind forecasts from NCEP's Rapid Update Cycle (RUC) model. The baseline version of the algorithm was developed and tested using data from St. Louis Lambert International Airport (STL). Algorithm performance was evaluated using 1-minute ASOS observations and crosswind component measurements taken from a dedicated Light Detection and Ranging (LIDAR) system. The algorithm was also demonstrated and evaluated at Houston George Bush International Airport (IAH). Use of the WFA is planned for 8 other airports deemed likely to derive significant benefit from WTMD procedures. The operational concept of WTMD for use by Air Traffic Control (ATC) includes additional decision levels beyond the WFA forecast. These include a check for VFR ceiling and visibility conditions, and final enablement by a human controller. More details concerning WTMD can be found in Lang et al. (2005) and Lang et al. (2007). A more complete description of the WFA is given in Robasky and Clark (2008). The early history of WFA development is detailed in Cole and Winkler (2004).
Summary
Turbulence associated with wake vortices generated by arriving and departing aircraft poses a potential safety risk to other nearby aircraft, and as such this potential risk may apply to aircraft operating on Closely Spaced Parallel Runways (CSPRs). Aircraft separation standards are imposed to mitigate this potential risk. The FAA and...
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Evaluation of weather impact models in departure management decision support: operational performance of the Route Availability Planning Tool (RAPT) prototype
January 20, 2008
Conference Paper
Published in:
13th Conf. on Aviation, Range and Aerospace Meteorology, ARAM, 20-24 January 2008.
Summary
In this paper, the revised RAPT algorithm and display are described and evaluated. The fidelity of the RAPT operational model is assessed by comparing RAPT departure status with observed departure flows (i.e., trajectories, weather avoidance maneuvers and storm penetrations) on several days when convective weather SWAPs were in effect in New York. Real-time in-situ observations at RAPT facilities (described in a companion paper at this conference; Robinson, 2008), user feedback from RAPT playbacks and the REPEAT web site are used to support this post-event evaluation. For example, real time observations provide the time and operational rationale for a specific departure route closure identified in the traffic flow analysis. This information is necessary to identify closures or flow restrictions that are the result of factors outside of the current RAPT algorithm domain (e.g., traffic restrictions due to volume, downstream congestion, etc.). Real time observations are also used to identify specific times when critical, weather-related operational decisions were made. The RAPT guidance at these critical decision points is analyzed to determine if RAPT provided information that enabled (or could have enabled, had it been used) more timely or effective decisions. The effect of forecast uncertainty on RAPT performance is also examined, particularly in convective weather situations where the location, severity and operational impact were difficult to predict. Strategies that mitigated risks associated with forecast uncertainty are presented. These include the use of additional information provided in the RAPT display, such as echo top heights encountered along the departure route, to confirm or modify RAPT guidance and the consideration of the departure status of two or more adjacent routes to 'average out' variations in the departure status timelines.
Summary
In this paper, the revised RAPT algorithm and display are described and evaluated. The fidelity of the RAPT operational model is assessed by comparing RAPT departure status with observed departure flows (i.e., trajectories, weather avoidance maneuvers and storm penetrations) on several days when convective weather SWAPs were in effect in...
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An automated visibility detection algorithm utilizing camera imagery
January 14, 2007
Conference Paper
Published in:
87Th AMS Annual Meeting, 14-18 January 2007.
Summary
The Federal Highway Administration (FHWA) has had a focused program to improve the integration of weather decision support systems into surface transportation operations since 1999. Clarus (Latin for clear) is the FHWA's most recent surface transportation weather initiative. The Clarus concept is to develop and demonstrate an integrated surface transportation weather observing, forecasting and data management system (Pisano, 2006a). As part of this effort, the FHWA is also promoting research into methods for applying new and existing sensor or probe data. These efforts include utilizing new in-vehicle sensor data that will be part of the vehicle infrastructure initiative (VII) (Pisano, 2006b), and finding innovative ways to use existing camera imagery. MIT Lincoln Laboratory (MIT/LL) was tasked to evaluate the usefulness of camera imagery for sensing ambient and road weather conditions and the feasibility for creating a portable visibility estimation algorithm. This paper gives a general background on the current utilization of camera imagery, including past and ongoing research of automated weather/condition algorithms. This is followed by a description of the MIT/LL camera test site, the analyses performed and the resultant prototype visibility estimation algorithm. In addition, the paper details application of the prototype algorithm to existing state DOT cameras in Utah. The final section discusses the future possibilities of camera-based weather and road condition algorithms.
Summary
The Federal Highway Administration (FHWA) has had a focused program to improve the integration of weather decision support systems into surface transportation operations since 1999. Clarus (Latin for clear) is the FHWA's most recent surface transportation weather initiative. The Clarus concept is to develop and demonstrate an integrated surface transportation...
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Enhanced radar data acquisition system and signal processing algorithms for the Terminal Doppler Weather Radar
October 24, 2005
Conference Paper
Published in:
32nd Conf. on Radar Meteorology, 24-29 October 2005.
Summary
As part of a broader FAA program to improve supportability, the Terminal Doppler Weather Radar (TDWR) radar data acquisition (RDA) subsystem is being replaced. For this purpose we developed an engineering prototype RDA with a scalable, open-systems hardware platform. This paper describes the design and characteristics of this new system. The dramatically increased computing power and more flexible transmitter control also enables modern signal processing algorithms to be implemented to improve the quality of the base data. Results highlighting the improved range-overlay protection provided by the new algorithms are presented.
Summary
As part of a broader FAA program to improve supportability, the Terminal Doppler Weather Radar (TDWR) radar data acquisition (RDA) subsystem is being replaced. For this purpose we developed an engineering prototype RDA with a scalable, open-systems hardware platform. This paper describes the design and characteristics of this new system...
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MIGFA: the Machine Intelligent Gust Front Algorithm for NEXRAD
October 24, 2005
Conference Paper
Published in:
32nd Conf. on Radar Meteorology, 24-29 October 2005.
Summary
Over a decade ago the FAA identified a need to detect and forecast movement of wind shear hazards such as gust fronts that impact the terminal air space. The Machine Intelligent Gust Front Algorithm (MIGFA) was developed to address this need (Delanoy and Troxel, 1993). The MIGFA product provides the position, the forecasted positions, and the strength of each wind shear detection to support air traffic control safety and planning functions. MIGFA will realize a new capability for NEXRAD but was originated for use with the FAA's Airport Surveillance Radar Model 9 (ASR-9) Weather Systems Processor (WSP) as described in Troxel and Pughe (2002). Subsequently, a second version was developed for the FAA's Terminal Doppler Weather Radar (TDWR) and is a component of the FAA's Integrated Terminal Weather System (ITWS). Most of the larger U.S. airports have ITWS installations. The ASR-9s are associated with medium-sized airports. MIGFA in NEXRAD is intended to further expand MIGFA support of air traffic control functions. There are significant algorithmic differences between the ASR-9 WSP and TDWR versions of MIGFA, primarily because of the different beam types of the two radars. Physically, the TDWR's pencil beam allows for good vertical resolution in a spatial volume of data. The ASR-9's vertical fan beam results in poor vertical resolution. Nonetheless, a key tenet in developing these two versions of MIGFA was to use the same core image processing analysis techniques (Morgan and Troxel, 2002) central to the MIGFA functionality. This same core is also central to MIGFA in NEXRAD. The Massachusetts Institute of Technology's Lincoln Laboratory (LL) has been tasked by the FAA to transfer MIGFA technology to NEXRAD. The goal is to enable a NEXRAD MIGFA capability at airports within about 70 km of any NEXRAD. LL has been developing NEXRAD algorithms to address the FAA's weather systems' needs since the Open Radar Product Generator (ORPG) was fielded in 2001. FAA sponsored, LL-developed NEXRAD algorithms generate the following products: the Data Quality Assurance (DQA), the High Resolution VIL (HRVIL), and the High Resolution Enhanced Echo Tops (HREET) (Smalley et al., 2003). These algorithms have proven useful to non-FAA users of NEXRAD products such as the National Weather Service (NWS) and the Department of Defense (DoD). Similarly, the NWS and DoD are developing plans to use MIGFA. MIGFA is slated to be included in the ORPG Build 9 baseline that is scheduled to be released in the Spring of 2007. In the following sections, we will discuss the salient features of MIGFA; the tuning of MIGFA to NEXRAD data; a comparison of detection performance of the TDWR and NEXRAD MIGFA versions; and some examples of MIGFA in operation.
Summary
Over a decade ago the FAA identified a need to detect and forecast movement of wind shear hazards such as gust fronts that impact the terminal air space. The Machine Intelligent Gust Front Algorithm (MIGFA) was developed to address this need (Delanoy and Troxel, 1993). The MIGFA product provides the...
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