Publications

This paper is a concept study for possible future utilization of active electronically scanned radars to provide weather and aircraft surveillance functions in U.S. airspace. If critical technology costs decrease sufficiently, multi-function phased array radars might prove to be a cost effective alternative to current surveillance radars, since the number of required radars would be reduced, and maintenance and logistics infrastructure would be consolidated. A radar configuration that provides terminal-area and long-range aircraft surveillance and weather measurement capability is described and a radar network design that replicates or exceeds current airspace coverage is presented. Key technology issues are examined, including transmit-receive elements, overlapped sub-arrays, the digital beamformer, and weather and aircraft post-processing algorithms. We conclude by discussing implications relative to future national weather and non-cooperative aircraft target surveillance needs. The U.S. Government currently operates four separate ground based surveillance radar networks supporting public and aviation-specific weather warnings and advisories, and primary or "skin paint" aircraft surveillance. The separate networks are: (i) The 10-cm wavelength NEXRAD or WSR88-D (Serafin and Wilson, 2000) national-scale weather radar network. This is managed jointly by the National Weather Service (NWS), the Federal Aviation Administration (FAA), and the Department of Defense (DoD). (ii) The 5-cm wavelength Terminal Doppler Weather Radars (TDWR) (Evans and Turnbull, 1989) deployed at large airports to detect low-altitude wind-shear phenomena. (iii) The 10-cm wavelength Airport Surveillance Radars (ASR-9 and ASR-11) (Taylor and Brunins, 1985) providing terminal area primary aircraft surveillance and vertically averaged precipitation reflectivity measurements. (iv) The 30-cm wavelength Air Route Surveillance Radars (ARSR-1, 2, 3 and 4) (Weber, 2005) that provide national-scale primary aircraft surveillance. The latter three networks are managed primarily by the FAA, although the DoD operates a limited number of ASRs and has partial responsibility for maintenance of the ARSR network. In total there are 513 of these radars in the contiguous United States (CONUS), Alaska, and Hawaii. The agencies that maintain these radars conduct various "life extension" activities that are projected to extend their operational life to approximately 2020. At this time, there are no defined programs to acquire replacement radars. The NWS and FAA have recently begun exploratory research on the capabilities and technology issues related to the use of multi-function phased array radar (MPAR) as a possible replacement approach. A key concept is that the MPAR network could provide both weather and primary aircraft surveillance, thereby reducing the total number of ground-based radars. In addition, MPAR surveillance capabilities would likely exceed those of current operational radars, for example, by providing more frequent weather volume scans and by providing vertical resolution and height estimates for primary aircraft targets. Table 1 summarizes the capabilities of current U.S. surveillance radars. These are approximations and do not fully capture variations in capability as a function, for example, of range or operating mode. A key observation is that significant variation in update rates between the aircraft and weather surveillance functions are currently achieved by using fundamentally different antenna patterns--low-gain vertical "fan beams" for aircraft surveillance that are scanned in azimuth only, versus high-gain weather radar "pencil beams" that are scanned volumetrically at much lower update rates. Note also that, if expressed in consistent units, the power-aperture products of the weather radars significantly exceed those of the ASRs and ARSRs. In the next section, we present a concept design for MPAR and demonstrate that it can simultaneously provide the measurement capabilities summarized in Table 1. In Section 3 we present an MPAR network concept that duplicates the airspace coverage provided by the current multiple radar networks. Section 4 discusses technology issues and associated cost considerations. We conclude in Section 5 by discussing implications relative to future national weather and non-cooperative aircraft target surveillance needs.

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.

A radar data quality control (QC) system is being developed for the real-time, continuously updateable NOWCAST system at the Naval Research Laboratory (NRL-NOWCAST) in Monterey, California. NRL has developed its own new radar QC algorithms, and is also working with the MIT Lincoln Laboratory (MIT LL), the National Center for Atmospheric Research (NCAR), the National Severe Storms Laboratory and the Cooperative Institute for Mesoscale Meteorological Studies at the University of Oklahoma (NSSL-OU) to obtain, adapt, integrate, test and install various types of recently-developed radar QC algorithms for use with NRL-NOWCAST. These algorithms work with volume scans of full-resolution Doppler radar data. Radar data QC can be divided into two categories: echo classification (EC) and calibration. New EC algorithms have recently demonstrated substantial success at separating the radar echoes of precipitation from other echo types, such as noise, normal propagation (NP) and anomalous propagation (AP) ground clutter, sea clutter, insects/clear-air, birds, second-trip echoes, and constant power function (CPF) artifacts. Radar data calibration methods assess the accuracy of both the data values and data coordinates. One calibration issue is aliased radial velocity data from precipitation and insect/clear-air returns, which if correctly de-aliased, afford the opportunity to estimate winds. Another calibration issue of concern to NRL is the processing of radar data from mobile platforms, such as US Navy ships. This processing requires corrections to the radial velocity data and the data-coordinates for the motion of the platform, as well as corrections for the altitude of the data coordinates due to the AP of the radar beam that frequently occurs within surface and evaporation ducts of the marine atmosphere. The goal of this work is to test the performance of the most current and promising radar data QC algorithms on archived data sets, both from ground- and sea-based radars, in order to determine the optimal combination for future real-time use within NRL-NOWCAST. NRL-NOWCAST currently ingests full-resolution Doppler radar data from both the Weather Surveillance Radar-1988 Doppler (WSR-88D) network and the US Department of Defense (DoD) Supplemental Weather Radar (SWR) at the Naval Air Station (NAS) in Fallon, NV. Various products are then created from these data for NRL-NOWCAST display. The radar data are also ingested into the COAMPS-0S (R) (Geiszler et al. 2004) data assimilation system at NRL. Figure 1 shows a flow chart that summarizes the processing stages and uses of radar data at NRL. Figure 2 shows an example of the NRL-NOWCAST demonstration site currently set up at Fallon, where the specific products displayed are only a few from a large list that may be chosen by the forecasters at the NAS. This paper presents a brief overview of the concepts behind the various EC and radial velocity de-aliasing algorithms under consideration. Test results from an NRL algorithm-testing platform will also be presented along with some previously published test results from the authors. Additional test results from the platform will be presented at the conference. Methods to address data-value and data coordinate calibration problems associated with Doppler radars onboard US Navy ships are currently being studied; a discussion on future work in this area will be outlined.

The MITLL/AFRL MT system is a statistical phrase-based translation system that implements many modern SMT training and decoding techniques. Our system was designed with the long term goal of dealing with corrupted ASR input for Speech-to-Speech MT applications. This paper will discuss the architecture of the MITLL/AFRL MT system, and experiments with manual and ASR transcription data that were run as part of the IWSLT-2005 Chinese-to-English evaluation campaign.

Breathiness is an aspect of voice quality that is difficult to analyze and synthesize, especially since its periodic and noise components are typically overlapping in frequency. The decomposition and manipulation of these two components is of importance in a variety of speech application areas such as text-to-speech synthesis, speech encoding, and clinical assessment of disordered voices. This paper first investigates the perceptual relevance of a speech production model that assumes the speech noise component is modulated by the glottal airflow waveform. After verifying the importance of noise modulation in breathy vowels, we use the modulation model to address the particular problem of pitch modification of this signal class. Using a decomposition method referred to as pitch-scaled harmonic filtering to extract the additive noise component, we introduce a pitch modification algorithm that explicitly modifies the modulation characteristic of this noise component. The approach applies envelope shaping to the noise source that is derived from the inverse-filtered noise component. Modification examples using synthetic and real breathy vowels indicate promising performance with spectrally-overlapping periodic and noise components.

Assessing the security of large enterprise networks is complex and labor intensive. Current security analysis tools typically examine only individual firewalls, routers, or hosts separately and do not comprehensively analyze overall network security. We present a new approach that uses configuration information on firewalls and vulnerability information on all network devices to build attack graphs that show how far inside and outside attackers can progress through a network by successively compromising exposed and vulnerable hosts. In addition, attack graphs are automatically analyzed to produce a small set of prioritized recommendations to enhance network security. Field trials on networks with up to 3,400 hosts demonstrate the ability to accurately identify a small number of critical stepping-stone hosts that need to be patched to protect against external attackers. Simulation studies on complex networks with more than 40,000 hosts demonstrate good scaling. This analysis can be used for many purposes, including identifying critical stepping-stone hosts to patch or protect with a firewall, comparing the security of alternating network designs, determining the security risk caused by proposed changes in firewall rules or new vulnerabilities, and identifying the most critical hosts to patch when a new vulnerability is announced. Unique aspects of this work are new attack graph generation algorithms that scale to enterprise networks with thousands of hosts, efficient approaches to determine what other hosts and ports in large networks are reachable from each individual host, automatic data importation from network vulnerability scanners and firewalls, and automatic attack graph analyses to generate recommendations.

Multiple pulse-repetition interval (multi-PRI) transmission is part of an adaptive signal transmission and processing algorithm being developed to combat range-velocity (RV) ambiguity for the Terminal Doppler Weather Radar (TDWR). In Part I of this two-part paper, an adaptive clutter filtering procedure that yields low biases in the moments estimates was presented. In this part, algorithms for simultaneously providing range-overlay protection and velocity dealiasing using multi-PRI signal transmission and processing are presented. The effectiveness of the multi-PRI RV ambiguity mitigation scheme is demonstrated using simulated and real weather radar data, with excellent results. Combined with the adaptive clutter filter, this technique will be used within the larger context of an adaptive signal transmission and processing scheme in which phase-code processing will be a complementary alternative.

Algorithm implementation efficiency is key to delivering high-performance computing capabilities to demanding, high throughput signal and image processing applications and simulations. Significant progress has been made in optimization of serial programs, but many applications require parallel processing, which brings with it the difficult task of determining efficient mappings of algorithms. The pMapper infrastructure addresses the problem of performance optimization of multistage MATLAB applications on parallel architectures. pMapper is an automatic performance tuning library written as a layer on top of pMatlab: Parallel Matlab toolbox. While pMatlab abstracts the message-passing interface, the responsibility of mapping numerical arrays falls on the user. Choosing the best mapping for a set of numerical arrays is a nontrivial task that requires significant knowledge of programming languages, parallel computing, and processor architecture. pMapper automates the task of map generation. This abstract addresses the design details of pMapper and presents preliminary results.

Large data sets that cannot fit in memory can be addressed with out-of-core methods, which use memory as a "window" to view a section of the data stored on disk at a time. The Parallel Matlab for eXtreme Virtual Memory (pMatlab XVM) library adds out-of-core extensions to the Parallel Matlab (pMatlab) library. We have applied pMatlab XVM to the DARPA High Productivity Computing Systems? HPCchallenge FFT benchmark. The benchmark was run using several different implementations: C+MPI, pMatlab, pMatlab hand coded for out-of-core and pMatlab XVM. These experiments found 1) the performance of the C+MPI and pMatlab versions were comparable; 2) the out-of-core versions deliver 80% of the performance of the in-core versions; 3) the out-of-core versions were able to perform a 1 terabyte (64 billion point) FFT and 4) the pMatlab XVM program was smaller, easier to implement and verify, and more efficient than its hand coded equivalent. We are transitioning this technology to several DoD signal processing applications and plan to apply pMatlab XVM to the full HPCchallenge benchmark suite. Using next generation hardware, problems sizes a factor of 100 to 1000 times larger should be feasible.

The computational demands of software continue to outpace the capacities of processor and memory technologies, especially in scientific and engineering programs. One option to improve performance is parallel processing. However, despite decades of research and development, writing parallel programs continues to be difficult. This is especially the case for scientists and engineers who have limited backgrounds in computer science. MATLAB®, due to its ease of use compared to other programming languages like C and Fortran, is one of the most popular languages for implementing numerical computations, thus making it an excellent platform for developing an accessible parallel computing framework. The MIT Lincoln Laboratory has developed two libraries, pMatlab and MatlabMPI, that not only enables parallel programming with MATLAB in a simple fashion, accessible to non-computer scientists. This document will overview basic concepts in parallel programming and introduce pMatlab.