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Summary of triple Doppler data, Orlando 1991

April 7, 1992
  |
Project Report
Author:
Christopher F. Keohan
…
Published in:
MIT Lincoln Laboratory Report ATC-186
Topic:
doppler radar
R&D area:
R&D group:

Summary

Under Federal Aviation Administration (FAA) sponsorship, Lincoln Laboratory conducted an aviation weather hazard measurement and operational demonstration program during the summer of 1991 near the Orlando International Airport. Three Doppler radars were sited in a triangle around the airport, allowing triple Doppler coverage of thunderstorms and microbursts occurring there. This report contains a summary of all of the microburst producing thunderstorms that occurred within the triple Doppler region that were scanned in a coordinated fashion, during the months of June, July, August, and September, 1991. Statistics on the microburst events are presented to give an overall picture of the available data for use in analysis. The bulk of the report consists of detailed information about each triple Doppler day, including the time, location, and strength of microbursts within the triple Doppler period as well as the availability of data from supporting sensors including the ASR-9-WSR Doppler radar, radiosondes, LLWAS, Mesonet, AWOS, instrumented aircraft, ACARS, interferometer, and corona points.
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Summary

Under Federal Aviation Administration (FAA) sponsorship, Lincoln Laboratory conducted an aviation weather hazard measurement and operational demonstration program during the summer of 1991 near the Orlando International Airport. Three Doppler radars were sited in a triangle around the airport, allowing triple Doppler coverage of thunderstorms and microbursts occurring there. This...

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Summary of triple Doppler data, Orlando 1991

Doppler mean velocity estimation - small sample analysis and a new estimator

March 25, 1992
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Technical Report
Author:
Edward S. Chornoboy
Published in:
MIT Lincoln Laboratory Report TR-942
Topic:
doppler radar
R&D area:
R&D group:

Summary

Optimal Doppler velocity estimation, under the constraint of small sample size, is explored for a standard Gaussian signal measurement model and thematic maximum likelihood (ML) and Bayes estimation. Because the model considered depends on a vector parameter [velocity, spectrum width, and signal-to-noise ratio (SNR)], the exact formulation of an ML or Bayes solution involves a system of equations that is neither uncoupled nor explicit in form. Historically, iterative methods have been the most suggested approach to solving the required equations. In addition to being computationally intensive, it is unclear whether iterative methods can be constructed to perform well given a small-sample size and low signal strength. This report takes a different approach and seeks to construct approximate (ML and Bayes) estimators based on the notion of using constrained adaptive models to deal with nuisance parameter removal. A Monte Carlo simulation is used to determine small-sample estimator statistics and to demonstrate true performance bounds in the case of known nuisance values. Performance comparisons between these optional forms and other standard estimators [pulse pairs (PP) and a frequency domain (WP) method] are presented. Performance sensitivity of the optimal algorithms, with respect to uncertainity in the values of model nuisance parameters, is explored.
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Summary

Optimal Doppler velocity estimation, under the constraint of small sample size, is explored for a standard Gaussian signal measurement model and thematic maximum likelihood (ML) and Bayes estimation. Because the model considered depends on a vector parameter [velocity, spectrum width, and signal-to-noise ratio (SNR)], the exact formulation of an ML...

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Doppler mean velocity estimation - small sample analysis and a new estimator

An integrated speech-background model for robust speaker identification

March 23, 1992
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Conference Paper
Author:
Douglas A. Reynolds
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Published in:
Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, ICASSP, Vol. 2, 23-26 March 1992, pp. 185-188.
Topic:
speaker recognition
R&D area:
R&D group:

Summary

This paper examines a procedure for text independent speaker identification in noisy environments where the interfering background signals cannot be characterized using traditional broadband or impulsive noise models. In the procedure, both the speaker and the background processes are modeled using mixtures of Gaussians. Speaker and background models are integrated into a unified statistical framework allowing the decoupling of the underlying speech process from the noise corrupted observations via the expectation-maximization algorithm. Using this formalism, speaker model parameters are estimated in the presence of the background process, and a scoring procedure is implemented for computing the speaker likelihood in the noise corrupted environment. Performance is evaluated using a 16 speaker conversational speech database with both "speech babble" and white noise background processes.
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Summary

This paper examines a procedure for text independent speaker identification in noisy environments where the interfering background signals cannot be characterized using traditional broadband or impulsive noise models. In the procedure, both the speaker and the background processes are modeled using mixtures of Gaussians. Speaker and background models are integrated...

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An integrated speech-background model for robust speaker identification

A speech recognizer using radial basis function neural networks in an HMM framework

March 23, 1992
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Conference Paper
Author:
Elliot Singer
…
Published in:
ICASSP'92, Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Vol. 1, Speech Processing 1, 23-26 March 1992, pp. 629-632.
Topic:
speech recognition
R&D area:

Summary

A high performance speaker-independent isolated-word speech recognizer was developed which combines hidden Markov models (HMMs) and radial basis function (RBF) neural networks. RBF networks in this recognizer use discriminant training techniques to estimate Bayesian probabilities for each speech frame while HMM decoders estimate overall word likelihood scores for network outputs. RBF training is performed after the HMM recognizer has automatically segmented training tokens using forced Viterbi alignment. In recognition experiments using a speaker-independent E-set database, the hybrid recognizer had an error rate of 11.5% compared to 15.7% for the robust unimodal Gaussian HMM recognizer upon which the hybrid system was based. The error rate was also lower than that of a tied-mixture HMM recognizer with the same number of centers. These results demonstrate that RBF networks can be successfully incorporated in hybrid recognizers and suggest that they may be capable of good performance with fewer parameters than required by Gaussian mixture classifiers.
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Summary

A high performance speaker-independent isolated-word speech recognizer was developed which combines hidden Markov models (HMMs) and radial basis function (RBF) neural networks. RBF networks in this recognizer use discriminant training techniques to estimate Bayesian probabilities for each speech frame while HMM decoders estimate overall word likelihood scores for network outputs...

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A speech recognizer using radial basis function neural networks in an HMM framework

Initialization for improved IIR filter performance

March 1, 1992
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Journal Article
Author:
Edward S. Chornoboy
Published in:
IEEE Trans. Signal Process., Vol. 40, No. 3, March 1992, pp. 543-550.
Topic:
signal processing
R&D area:
R&D group:

Summary

A new method for initializing the memory registers of IIR filters is introduced. In addition to providing improved performance as compared to other methods of initialization, this method is unique in that it makes no a priori assumptions regarding the input-signal content. Therefore, this method applies equally well to a variety of IIR filter designs and applications. The method is best suited for signal-processing applications in which "batch" processing of the data is used. However, sequential processing can be accommodated when delays at the beginning of a processing segment can be tolerated.
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Summary

A new method for initializing the memory registers of IIR filters is introduced. In addition to providing improved performance as compared to other methods of initialization, this method is unique in that it makes no a priori assumptions regarding the input-signal content. Therefore, this method applies equally well to a...

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Initialization for improved IIR filter performance

Shape invariant time-scale and pitch modification of speech

March 1, 1992
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Journal Article
Author:
Thomas F. Quatieri
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Published in:
IEEE Trans. Signal Process., Vol. 40, No. 3, March 1992, pp. 497-510.
Topic:
speech enhancement
R&D area:
R&D group:

Summary

The simplified linear model of speech production predicts that when the rate of articulation is changed, the resulting waveform takes on the appearance of the original, except for a change in the time scale. The goal of this paper is to develop a time-scale modification system that preserves this shape-invariance property during voicing. This is done using a version of the sinusoidal analysis-synthesis system that models and independently modifies the phase contributions of the vocal tract and vocal cord excitation. An important property of the system is its capability of performing time-varying rates of change. Extensions of the method are applied to fixed and time-varying pitch modification of speech. The sine-wave analysis-synthesis system also allows for shape-invariant joint time-scale and pitch modification, and allows for the adjustment of the time scale and pitch according to speech characteristics such as the degree of voicing.
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Summary

The simplified linear model of speech production predicts that when the rate of articulation is changed, the resulting waveform takes on the appearance of the original, except for a change in the time scale. The goal of this paper is to develop a time-scale modification system that preserves this shape-invariance...

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Shape invariant time-scale and pitch modification of speech

Terminal Doppler weather radar/low-level wind shear alert system integration algorithm specification, version 1.1

February 24, 1992
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Project Report
Author:
Rodney E. Cole
Published in:
MIT Lincoln Laboratory Report ATC-187
Topic:
algorithms
R&D area:
R&D group:

Summary

There will be a number of airports that receive both a Terminal Doppler Weather Radar (TDWR) windshear detection system and a phase III Low-Level Wind Shear Alert System (LLWAS). At those airports, the two systems will need to he combined into a single windshear detection system. This report specifies the algorithm to be used to integrate the two subsystems. The algorithm takes in the alphanumeric runway alert messages generated by each subsystem and joins them into integrated alert messages. The design goals of this windshear detection system are (1) to maintain the probability of detection for hazardous events while reducing the number of false alerts and microburst overwarnings and 2) to increase the accuracy of the loss/gain estimates. The first design goal is accomplished by issuing an integrated alert for an operational runway whenever either subsystem issues a 'strong' alert for that runway; by canceling a 'weak' windshear alert on an operational runway if only one subsystem is making the declaration; and by reducing a 'weak' microburst alert on an operational runway to a 'strong' windshear alert if only one subsystem is making the declaration. The second design goal is accomplished by using the average of the two loss/gain values, when appropriate. TDWR, windshear, LLWAS, algorithm specification.
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Summary

There will be a number of airports that receive both a Terminal Doppler Weather Radar (TDWR) windshear detection system and a phase III Low-Level Wind Shear Alert System (LLWAS). At those airports, the two systems will need to he combined into a single windshear detection system. This report specifies the...

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Terminal Doppler weather radar/low-level wind shear alert system integration algorithm specification, version 1.1

Improved hidden Markov model speech recognition using radial basis function networks

December 13, 1991
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Conference Paper
Author:
Elliot Singer
…
Published in:
Advances in Neural Information Processing Systems, Denver, CO, 2-5 December 1991.
Topic:
speech recognition
R&D area:

Summary

A high performance speaker-independent isolated-word hybrid speech recognizer was developed which combines Hidden Markov Models (HMMs) and Radial Basis Function (RBF) neural networks. In recognition experiments using a speaker-independent E-set database, the hybrid recognizer had an error rate of 11.5% compared to 15.7% for the robust unimodal Gaussian HMM recognizer upon which the hybrid system was based. These results and additional experiments demonstrate that RBF networks can be successfully incorporated in hybrid recognizers and suggest that they may be capable of good performance with fewer parameters than required by Gaussian mixture classifiers. A global parameter optimization method designed to minimize the overall word error rather than the frame recognition error failed to reduce the error rate.
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Summary

A high performance speaker-independent isolated-word hybrid speech recognizer was developed which combines Hidden Markov Models (HMMs) and Radial Basis Function (RBF) neural networks. In recognition experiments using a speaker-independent E-set database, the hybrid recognizer had an error rate of 11.5% compared to 15.7% for the robust unimodal Gaussian HMM recognizer...

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Improved hidden Markov model speech recognition using radial basis function networks

Neural network classifiers estimate Bayesian a posteriori probabilities

December 1, 1991
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Conference Paper
Author:
Michael D. Richard
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Published in:
Neural Comput., Vol. 3, No. 4, Winter 1991, pp. 461-483.
Topic:
machine learning
R&D area:
R&D group:

Summary

Many neural network classifiers provide outputs which estimate Bayesian a posteriori probabilities. When the estimation is accurate, network outputs can be treated as probabilities and sum to one. Simple proofs show that Bayesian probabilities are estimated when desired network outputs are 1 of M (one output unity, all others zero) and a squared-error or mss-entropy cost function is used. Results of Monte Carlo simulations performed using multilayer perceptron (MLP) networks trained with backpropagation, radial basis function (RBD networks, and high-order polynomial networks graphically demonstrate that network outputs provide good estimates of Bayesian probabilities. Estimation accuracy depends on network complexity, the amount of training data, and the degree to which training data reflect true likelihood distributions and a priori class probabilities. Interpretation of network outputs as Bayesian probabilities allows outputs from multiple networks to be combined for higher level decision making, simplifies creation of rejection thresholds, makes it possible to compensate for differences between pattern class probabilities in training and test data, allows outputs to be used to minimize alternative risk functions, and suggests alternative measures of network performance.
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Summary

Many neural network classifiers provide outputs which estimate Bayesian a posteriori probabilities. When the estimation is accurate, network outputs can be treated as probabilities and sum to one. Simple proofs show that Bayesian probabilities are estimated when desired network outputs are 1 of M (one output unity, all others zero)...

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Neural network classifiers estimate Bayesian a posteriori probabilities

Air-to-air visual acquisition handbook

November 27, 1991
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Project Report
Author:
John W. Andrews
Published in:
MIT Lincoln Laboratory Report ATC-151
Topic:
collision avoidance
R&D area:
R&D group:

Summary

The document describes a set of computer programs that provide a practical means for predicting air-to-air visual acquisition performance for aircraft on collision courses. The programs are based upon a mathematical model of pilot visual acquisition performance. Guidelines are provided for selecting model parameters based upon previously collected flight test data. Selected results of computer analysis are provided.
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Summary

The document describes a set of computer programs that provide a practical means for predicting air-to-air visual acquisition performance for aircraft on collision courses. The programs are based upon a mathematical model of pilot visual acquisition performance. Guidelines are provided for selecting model parameters based upon previously collected flight test...

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Air-to-air visual acquisition handbook
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