Publications
Accuracy of motion-compensated NEXRAD precipitation
June 23, 2003
Project Report
Published in:
MIT Lincoln Laboratory Report ATC-312
Summary
A number of Federal Aviation Administration (FAA) aviation weather systems utilize Next Generation Weather Radar (NEXRAD) precipitation products including the Integrated Terminal Weather System (ITWS), Corridor Integrated Weather System (CIWS), Medium Intensity Airport Weather System (MIAWS), and the Weather and Radar Processor (WARP). The precipitation products from a NEXRAD [e.g., base reflectivity, composite reflectivity (CR), and vertical integrated liquid (VIL)] are generally only updated once with each NEXRAD volume scan, nominally at 5-6 minute intervals. Hence, the indicated position of storms may not correspond to the actual position due to movement of the storms since the last NEXRAD product update. This latency is particularly a concern in terminal applications such as MIAWS, which use the NEXRAD precipitation product to provide time critical information on moderate and heavy precipitation impacts on the final approach and departure corridors and runways. In order to provide a more accurate depiction, the MIAWS precipitation map is updated (advected) every 30 seconds based on the motion of the storms. The CIWS system performs a similar advection of NEXRAD data before mosaicing the precipitation products from individual NEXRADs. In both cases, motion vectors used for advection are generated by spatial cross-correlation of two consecutive precipitation maps (Chornoboy et al., 1994). This report addresses the accuracy of the advected precipitation map as compared to the current NEXRAD precipitation map using seven MIAWS cases from the Memphis, TN testbed and Jackson, MS prototype. We find that the advected precipitation product is significantly more accurate at providing a depiction of the current intensity of the storms as a fbnction of location. Without advection, the precipitation product from successive NEXRAD volume scans differs by at least one VIP level for over 47.5% of the one square kilometer pixels and has VIP level differences of two levels or more for 6.9% of the pixels in cases where both products had precipitation in a location. The advected precipitation product differs by one or more levels in only 17.2% of the pixels and a VIP level difference of two or more levels is observed in only 1.6% of the pixels. The percentage of cells in which there is precipitation in one map and no precipitation in the other is reduced from over 22% to less than 11% by use of advection. The analysis approach utilized did not quantitatively determine the relative importance of storm growth and decay over the period of the volume scan versus errors in storm motion estimation in causing the differences between the advected precipitation field and the current precipitation field.
Summary
A number of Federal Aviation Administration (FAA) aviation weather systems utilize Next Generation Weather Radar (NEXRAD) precipitation products including the Integrated Terminal Weather System (ITWS), Corridor Integrated Weather System (CIWS), Medium Intensity Airport Weather System (MIAWS), and the Weather and Radar Processor (WARP). The precipitation products from a NEXRAD [e.g...
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Overview of the Earth Observing One (EO-1) mission
June 1, 2003
Journal Article
Published in:
IEEE Trans. Geosci. Remote Sens., Vol. 41, No. 6, Pt. 1, June 2003, pp. 1149-1159.
Summary
The Earth Observing One (EO-1) satellite, a part of National Aeronautics and Space Administration's New Millennium Program, was developed to demonstrate new technologies and strategies for improved earth observations. It was launched from Vandenburg Air Force Base on November 21, 2000. The EO-1 satellite contains three observing instruments supported by a variety of newly developed space technologies. The Advanced Land Imager (ALI) is a prototype for a new generation of Landsat-7 Thematic Mapper. The Hyperion Imaging Spectrometer is the first high spatial resolution imaging spectrometer to orbit the earth. The Linear Etalon Imaging Spectral Array (LEISA) Atmospheric Corrector (LAC) is a high spectral resolution wedge imaging spectrometer designed to measure atmospheric water vapor content. Instrument performances are validated and carefully monitored through a combination of radiometric calibration approaches: solar, lunar, stellar, earth (vicarious), and atmospheric observations complemented by onboard calibration lamps and extensive prelaunch calibration. Techniques for spectral calibration of space-based sensors have been tested and validated with Hyperion. ALI and Hyperion instrument performance continue to meet or exceed predictions well beyond the planned one-year program. This paper reviews the EO-1 satellite system and provides details of the instruments and their performance as measured during the first year of operation. Calibration techniques and tradeoffs between alternative approaches are discussed. An overview of the science applications for instrument performance assessment is presented.
Summary
The Earth Observing One (EO-1) satellite, a part of National Aeronautics and Space Administration's New Millennium Program, was developed to demonstrate new technologies and strategies for improved earth observations. It was launched from Vandenburg Air Force Base on November 21, 2000. The EO-1 satellite contains three observing instruments supported by...
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Medium intensity airport weather system NEXRAD selection recommendations
April 29, 2003
Project Report
Published in:
MIT Lincoln Laboratory Report ATC-311
Summary
Under Federal Aviation Administration (FAA) sponsorship, Lincoln Laboratory has developed a Medium Intensity Airport Weather System (MIAWS). MIAWS provides air traffic controllers at medium- intensity airports a real time color display of weather impacting the terminal airspace. The weather data comes from nearby Doppler weather surveillance radars, called Next Generation Radar (NEXRAD). since May 2000 at field sites in Memphis (TN), Jackson (MS), Little Rock (AR), and Springfield (MO). With the success of the MIAWS prototypes and favorable response among air traffic controller users, the FAA is seeking to rapidly deploy MIAWS systems at forty airports within the National Airspace System Lincoln Lab has been operating prototypes of the Medium Intensity Airport Weather System (MIAWS) WAS). This report identifies suitable NEXRAD systems for each of the 40 MIAWS airports and three additional test and/or maintenance FAA facilities. Several other radar selection options are also provided to account for availability and cost-saving contingencies.
Summary
Under Federal Aviation Administration (FAA) sponsorship, Lincoln Laboratory has developed a Medium Intensity Airport Weather System (MIAWS). MIAWS provides air traffic controllers at medium- intensity airports a real time color display of weather impacting the terminal airspace. The weather data comes from nearby Doppler weather surveillance radars, called Next Generation...
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Combining cross-stream and time dimensions in phonetic speaker recognition
April 6, 2003
Conference Paper
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. 4, 6-10 April 2003, pp. IV-800 - IV-803.
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Summary
Recent studies show that phonetic sequences from multiple languages can provide effective features for speaker recognition. So far, only pronunciation dynamics in the time dimension, i.e., n-gram modeling on each of the phone sequences, have been examined. In the JHU 2002 Summer Workshop, we explored modeling the statistical pronunciation dynamics across streams in multiple languages (cross-stream dimensions) as an additional component to the time dimension. We found that bigram modeling in the cross-stream dimension achieves improved performance over that in the time dimension on the NIST 2001 Speaker Recognition Evaluation Extended Data Task. Moreover, a linear combination of information from both dimensions at the score level further improves the performance, showing that the two dimensions contain complementary information.
Summary
Recent studies show that phonetic sequences from multiple languages can provide effective features for speaker recognition. So far, only pronunciation dynamics in the time dimension, i.e., n-gram modeling on each of the phone sequences, have been examined. In the JHU 2002 Summer Workshop, we explored modeling the statistical pronunciation dynamics...
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Channel robust speaker verification via feature mapping
April 6, 2003
Conference Paper
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. II, 6-10 April 2003, pp. II-53 - II-56.
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Summary
In speaker recognition applications, channel variability is a major cause of errors. Techniques in the feature, model and score domains have been applied to mitigate channel effects. In this paper we present a new feature mapping technique that maps feature vectors into a channel independent space. The feature mapping learns mapping parameters from a set of channel-dependent models derived for a channel-dependent models derived from a channel-independent model via MAP adaptation. The technique is developed primarily for speaker verification, but can be applied for feature normalization in speech recognition applications. Results are presented on NIST landline and cellular telephone speech corpora where it is shown that feature mapping provides significant performance improvements over baseline systems and similar performance to Hnorm and Speaker-Model-Synthesis (SMS).
Summary
In speaker recognition applications, channel variability is a major cause of errors. Techniques in the feature, model and score domains have been applied to mitigate channel effects. In this paper we present a new feature mapping technique that maps feature vectors into a channel independent space. The feature mapping learns...
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Conditional pronunciation modeling in speaker detection
April 6, 2003
Conference Paper
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, 6-10 April 2003.
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R&D area:
Summary
In this paper, we present a conditional pronunciation modeling method for the speaker detection task that does not rely on acoustic vectors. Aiming at exploiting higher-level information carried by the speech signal, it uses time-aligned streams of phones and phonemes to model a speaker's specific Pronunciation. Our system uses phonemes drawn from a lexicon of pronunciations of words recognized by an automatic speech recognition system to generate the phoneme stream and an open-loop phone recognizer to generate a phone stream. The phoneme and phone streams are aligned at the frame level and conditional probabilities of a phone, given a phoneme, are estimated using co-occurrence counts. A likelihood detector is then applied to these probabilities. Performance is measured using the NIST Extended Data paradigm and the Switchboard-I corpus. Using 8 training conversations for enrollment, a 2.1% equal error rate was achieved. Extensions and alternatives, as well as fusion experiments, are presented and discussed.
Summary
In this paper, we present a conditional pronunciation modeling method for the speaker detection task that does not rely on acoustic vectors. Aiming at exploiting higher-level information carried by the speech signal, it uses time-aligned streams of phones and phonemes to model a speaker's specific Pronunciation. Our system uses phonemes...
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Phonetic speaker recognition using maximum-likelihood binary-decision tree models
April 6, 2003
Conference Paper
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP, Vol. 4, 6-10 April 2003.
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R&D area:
Summary
Recent work in phonetic speaker recognition has shown that modeling phone sequences using n-grams is a viable and effective approach to speaker recognition, primarily aiming at capturing speaker-dependent pronunciation and also word usage. This paper describes a method involving binary-tree-structured statistical models for extending the phonetic context beyond that of standard n-grams (particularly bigrams) by exploiting statistical dependencies within a longer sequence window without exponentially increasing the model complexity, as is the case with n-grams. Two ways of dealing with data sparsity are also studied, namely, model adaptation and a recursive bottom-up smoothing of symbol distributions. Results obtained under a variety of experimental conditions using the NIST 2001 Speaker Recognition Extended Data Task indicate consistent improvements in equal-error rate performance as compared to standard bigram models. The described approach confirms the relevance of long phonetic context in phonetic speaker recognition and represents an intermediate stage between short phone context and word-level modeling without the need for any lexical knowledge, which suggests its language independence.
Summary
Recent work in phonetic speaker recognition has shown that modeling phone sequences using n-grams is a viable and effective approach to speaker recognition, primarily aiming at capturing speaker-dependent pronunciation and also word usage. This paper describes a method involving binary-tree-structured statistical models for extending the phonetic context beyond that of...
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The SuperSID project : exploiting high-level information for high-accuracy speaker recognition
April 6, 2003
Conference Paper
Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. 4, 6-10 April 2003, pp. IV-784 - IV-787.
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Summary
The area of automatic speaker recognition has been dominated by systems using only short-term, low-level acoustic information, such as cepstral features. While these systems have indeed produced very low error rates, they ignore other levels of information beyond low-level acoustics that convey speaker information. Recently published work has shown examples that such high-level information can be used successfully in automatic speaker recognition systems and has the potential to improve accuracy and add robustness. For the 2002 JHU CLSP summer workshop, the SuperSID project was undertaken to exploit these high-level information sources and dramatically increase speaker recognition accuracy on a defined NIST evaluation corpus and task. This paper provides an overview of the structures, data, task, tools, and accomplishments of this project. Wide ranging approaches using pronunciation models, prosodic dynamics, pitch and duration features, phone streams, and conversational interactions were explored and developed. In this paper we show how these novel features and classifiers indeed provide complementary information and can be fused together to drive down the equal error rate on the 2001 NIS extended data task to 0.2% - a 71% relative reduction in error over the previous state of the art.
Summary
The area of automatic speaker recognition has been dominated by systems using only short-term, low-level acoustic information, such as cepstral features. While these systems have indeed produced very low error rates, they ignore other levels of information beyond low-level acoustics that convey speaker information. Recently published work has shown examples...
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Using prosodic and conversational features for high-performance speaker recognition : report from JHU WS'02
April 6, 2003
Conference Paper
Published in:
Proc. IEEE Int. Conf. on Acoustics, speech, and Signal Processing, ICASSP, Vol. IV, 6-10 April 2003, pp. IV-792 - IV-795.
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Summary
While there has been a long tradition of research seeking to use prosodic features, especially pitch, in speaker recognition systems, results have generally been disappointing when such features are used in isolation and only modest improvements have been set when used in conjunction with traditional cepstral GMM systems. In contrast, we report here on work from the JHU 2002 Summer Workshop exploring a range of prosodic features, using as testbed NIST's 2001 Extended Data task. We examined a variety of modeling techniques, such as n-gram models of turn-level prosodic features and simple vectors of summary statistics per conversation side scored by kth nearest-neighbor classifiers. We found that purely prosodic models were able to achieve equal error rates of under 10%, and yielded significant gains when combined with more traditional systems. We also report on exploratory work on "conversational" features, capturing properties of the interaction across conversion sides, such as turn-taking patterns.
Summary
While there has been a long tradition of research seeking to use prosodic features, especially pitch, in speaker recognition systems, results have generally been disappointing when such features are used in isolation and only modest improvements have been set when used in conjunction with traditional cepstral GMM systems. In contrast...
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Evaluation of TDWR range-velocity ambiguity mitigation techniques
April 4, 2003
Project Report
Published in:
MIT Lincoln Laboratory Report ATC-310
Summary
Range and velocity ambiguities pose significant data quality challenges for the Terminal Doppler Weather Radar (TDWR). For typical pulse repetition frequencies (PRFs) of 1-2 kHz, the radar is subject to both range-ambiguous precipitation returns and velocity aliasing. Experience shows that these are a major contributor to failures of the system's wind shear detection algorithms. Here we evaluate the degree of mitigation offered by existing phase diversity methods to these problems. Using optimized processing techniques, we analyze the performance of two particular phase codes that are best suited for application to TDWRs- random and SZ(8/64) [Sachidananda and Zrnic', [1999]- in the protection of weak-trip power, velocity, and spectral width estimates. Results from both simulated and real weather data indicate that the SZ(8/64) code generally outperforms the random code, except for protection of 1st trip from 5th trip interference. However, the SZ code estimates require a priori knowledge of out-of-trip spectral widths for censoring. This information cannot be provided adequately by a separate scan with a Pulse Repetition Frequency (PRF) low enough to unambiguously cover the entire range of detectable weather, because then the upper limit of measurable spectral width is only about 2 m/s . For this reason we conclude that SZ phase codes are not appropriate for TDWR use. For velocity ambiguity resolution, the random phase code could be transmitted at two PRFs on alternating dwells. Assuming the velocity changes little between two consecutive dwells, a Chinese remainder type of approach can be used to dealias the velocities. Strong ground clutter at close range, however, disables this scheme for gates at the beginning of the 2nd trip of the higher PRF. We offer an alternative scheme for range-velocity ambiguity mitigation: Multistaggered Pulse Processing (MSPP). Yielding excellent velocity dealiasing capabilities, the MSPP method should also provide protection from patchy, small-scale out-of-trip weather. To obtain maximum performance in both range and velocity dealiasing, we suggest that information from the initial low-PRF scan be used to decide the best waveform to transmit in the following scan-random phase code with alternating-dwell PRFs or MSPP. Such an adaptive approach presages future developments in weather radar, for example electronically scanned arrays allow selective probing of relevant weather events.
Summary
Range and velocity ambiguities pose significant data quality challenges for the Terminal Doppler Weather Radar (TDWR). For typical pulse repetition frequencies (PRFs) of 1-2 kHz, the radar is subject to both range-ambiguous precipitation returns and velocity aliasing. Experience shows that these are a major contributor to failures of the system's...
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