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At least 703 records · Page 39

A method for classification of multisource data using interval-valued probabilities and its application to HIRIS data

A method of classifying multisource data in remote sensing is presented. The proposed method considers each data source as an information source providing a body of evidence, represents statistical evidence by interval-valued probabilities, and uses Dempster's rule to integrate information based on multiple data sources. The method is applied to the problems of ground-cover classification of multispectral data combined with digital terrain data such as elevation, slope, and aspect. Then this method is applied to simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data by dividing the dimensionally huge data source into smaller and more manageable pieces based on the global statistical correlation information. It produces higher classification accuracy than the Maximum Likelihood (ML) classification method when the Hughes phenomenon is apparent.

Kim, H.↗

Data space volumes and classification optimization of SPOT and Landsat TM data

In order to compare the data space volume of SPOT XS and Landsat TM images, three data sets, i.e., a wetlands/agricultural data set, an agricultural data set, and a forest data set, are examined. The comparisons are made for the same geographic area. The data space volumes for Landsat TM (2, 3, and 4) are found to be 70 to 100 percent larger than the volumes for the SPOT XS images. It is suggested that the additional midinfrared bands contribute to the difference in data space volumes between Landsat TM and SPOT XS. The data space volumes for Landsat TM bands 3, 4, and 5 are more than an order of magnitude greater than the volumes for the three band SPOT XS data sets. The volumes of the six-band Landsat TM images are four orders of magnitude greater than the SPOT XS. The analysis of the data space volumes is used to optimize the computation time and minimize the storage requirements of a maximum-likelihood classification based on a look-up table.

Ahearn, Sean C.↗

50 Mbps free space direct detection laser diode optical communication system with Q = 4 PPM signaling

A 50 Mbps direct detection optical communication system for use in an intersatellite link was constructed with an AlGaAs laser diode transmitter and a silicon avalanche photodiode photodetector. The system used a Q = 4 PPM format. The receiver consisted of a maximum likelihood PPM detector and a timing recovery subsystem. The PPM slot clock was recovered at the receiver by using a transition detector followed by a PLL. The PPM word clock was recovered by using a second PLL whose input was derived from the presence of back-to-back PPM pulses contained in the received random PPM pulse sequences. The system achieved a bit error rate of 0.000001 at less than 50 detected signal photons/information bit. The receiver was capable of acquiring and maintaining slot and word synchronization for received signal levels greater than 20 photons/information bit, at which the receiver bit error rate was about 0.01.

Sun, Xiaoli↗

Absolute magnitude calibration using trigonometric parallax - Incomplete, spectroscopic samples

A new numerical algorithm is used to calibrate the absolute magnitude of spectroscopically selected stars from their observed trigonometric parallax. This procedure, based on maximum-likelihood estimation, can retrieve unbiased estimates of the intrinsic absolute magnitude and its dispersion even from incomplete samples suffering from selection biases in apparent magnitude and color. It can also make full use of low accuracy and negative parallaxes and incorporate censorship on reported parallax values. Accurate error estimates are derived for each of the fitted parameters. The algorithm allows an a posteriori check of whether the fitted model gives a good representation of the observations. The procedure is described in general and applied to both real and simulated data.

Ratnatunga, Kavan U.↗

Experiments in on-orbit identification for control of space structures

Two extensions to an earlier work on system identification for large flexible structures are presented. The first extension applies an integrated frequency-domain ID approach to experiments utilizing rib root actuators for full system excitation of both 'boom-dish' and 'dish' modes of the structure; while the second extension employs a time-domain identification, utilizing frequency-domain results obtained for initialization of the parameter estimates. For the first extension, the results show that in the presence of closely packed modes, the curve-fit algorithm employed can distinguish modes with frequency separation as small as 0.04 Hz. The maximum likelihood estimation used in the second extension produce estimates close to the parametric modal values of frequencies and damping.

Yam, Y.↗

Application of a novel multi-stage signal parameter estimator to high dynamic GPS receivers

The performance of a novel multistage estimator when applied to the estimation of the position, velocity, and acceleration of high dynamic Global Positioning System (GPS) receivers is discussed. For the present application, a two-stage specialization of the more general estimation scheme is considered, wherein the first-stage algorithm is selected to be a modified least-squares algorithm operating upon the differential signal model and referred to as differential least-squares (DLS) and the second stage is simply an extended Kalman filter (EKF). In terms of the threshold on received carrier power-to-noise power spectral density ratio (CNR), when compared to the single-stage EKF algorithm, the DLS-EKF algorithm is about 1.5-2.0 dB better in terms of threshold and outperforms the crossproduct AFC (automatic frequency control) loop by 2-5 dB. For the case when data modulation is present, the proposed scheme provides an improvement of about 6 dB in terms of CNR compared to an earlier approximate MLE (maximum likelihood estimation) scheme. There are also very significant improvements in terms of other performance measures.

Kumar, R.↗

Using spatial coherence to retrieve cloud properties - ERBE experience and CERES applications

Cloud properties for individual ERBE scanner fields of view are obtained from simultaneous, collocated imagery data. The cloud properties are used to assess the performance of the ERBE maximum likelihood estimate of cloud conditions which ultimately influences estimates of the radiative fluxes at the top of the atmosphere. During EOS the analysis of MODIS observations within the fields of view of the CERES instruments promises to yield, in addition, estimates of surface radiative fluxes as well as the vertical distribution of radiative heating within the atmosphere.

Coakley, James A., Jr.↗

Power system state estimation for a spacecraft power system

An application of the maximum likelihood state estimator to a space-based power system is presented. The state estimator uses current and voltage measurements to generate estimates of node voltages for an electrical power distribution system for the Space Shuttle. Preliminary results on the effect of noisy measurements on estimated parameters are reported. The software used in generating these results is part of an overall package being developed at Louisiana Tech University. Intended applications of this package include the analysis of power systems and real-time parallel processing on the Space Shuttle.

Berry, F. C.↗

On multilevel block modulation codes

The multilevel (ML) technique for combining block coding and modulation is investigated. A general formulation is presented for ML modulation codes in terms of component codes with appropriate distance measures. A specific method for constructing ML block modulation codes (MLBMCs) with interdependency among component codes is proposed. Given an MLBMC C with no interdependency among the binary component codes, the proposed method gives an MLBC C-prime that has the same rate as C, a minimum squared Euclidean distance not less than that of C, a trellis diagram with the same number of states as that of C, and a smaller number of nearest-neighbor codewords than that of C. Finally, a technique is presented for analyzing the error performance of MLBMCs for an additive white Gaussian noise channel based on soft-decision maximum-likelihood decoding.

Kasami, Tadao↗

On the estimation of climatological Z-R relationships

A statistical framework for climatological Z-R parameter estimation is developed and simulation experiments are conducted to examine sampling properties of the estimators. Both parametric and nonparametric models are considered. For parametric models, it is shown that Z-R parameters can be estimated by maximum likelihood, a procedure with optimal large sample properties. A general nonparametric framework for climatological Z-R estimation is also developed. Nonparametric procedures are attractive because of their flexibility in dealing with certain types of measurement errors common to radar data. Simulation experiments show that even under favorable assumptions on error characteristics of radar and raingages, large datasets are required to obtain accurate Z-R parameter estimates. Another important conclusion is that estimation results are generally quite sensitive to radar and raingage measurement thresholds. For fixed sample size, the simulation results can be used to provide quantitative assessments of the accuracy of Z-R model parameter estimates. These results are particularly useful for error analysis of precipitation products that are derived using climatological Z-R relations. One example is the large-area rainfall estimates derived using the height-area rainfall threshold (HART) technique.

Krajewski, Witold F.↗

New quests for better attitudes

During the past few years considerable insight was gained into the QUEST algorithm both as a maximum likelihood estimator and as a Kalman filter/smoother for systems devoid of dynamical noise. The new algorithms and software are described and analytical comparisons are made with the more conventional attitude Kalman filter. It is also described how they may be accommodated to noisy dynamical systems.

Shuster, Malcolm D.↗

Multi-stage decoding of multi-level modulation codes

Various types of multi-stage decoding for multi-level modulation codes are investigated. It is shown that if the component codes of a multi-level modulation code and types of decoding at various stages are chosen properly, high spectral efficiency and large coding gain can be achieved with reduced decoding complexity. Particularly, it is shown that the difference in performance between the suboptimum multi-stage soft-decision maximum likelihood decoding of a modulation code and the single-stage optimum soft-decision decoding of the code is very small, only a fraction of dB loss in signal to noise ratio at a bit error rate (BER) of 10(exp -6).

Lin, Shu↗

Going through a quantum phase

Phase measurements on a single-mode radiation field are examined from a system-theoretic viewpoint. Quantum estimation theory is used to establish the primacy of the Susskind-Glogower (SG) phase operator; its phase eigenkets generate the probability operator measure (POM) for maximum likelihood phase estimation. A commuting observables description for the SG-POM on a signal x apparatus state space is derived. It is analogous to the signal-band x image-band formulation for optical heterodyne detection. Because heterodyning realizes the annihilation operator POM, this analogy may help realize the SG-POM. The wave function representation associated with the SG POM is then used to prove the duality between the phase measurement and the number operator measurement, from which a number-phase uncertainty principle is obtained, via Fourier theory, without recourse to linearization. Fourier theory is also employed to establish the principle of number-ket causality, leading to a Paley-Wiener condition that must be satisfied by the phase-measurement probability density function (PDF) for a single-mode field in an arbitrary quantum state. Finally, a two-mode phase measurement is shown to afford phase-conjugate quantum communication at zero error probability with finite average photon number. Application of this construct to interferometric precision measurements is briefly discussed.

Shapiro, Jeffrey H.↗

Design of neural networks for classification of remotely sensed imagery

Classification accuracies of a backpropagation neural network are discussed and compared with a maximum likelihood classifier (MLC) with multivariate normal class models. We have found that, because of its nonparametric nature, the neural network outperforms the MLC in this area. In addition, we discuss techniques for constructing optimal neural nets on parallel hardware like the MasPar MP-1 currently at GSFC. Other important discussions are centered around training and classification times of the two methods, and sensitivity to the training data. Finally, we discuss future work in the area of classification and neural nets.

Chettri, Samir R.↗

FEC decoder design optimization for mobile satellite communications

A new telecommunications service for location determination via satellite is being proposed for the continental USA and Europe, which provides users with the capability to find the location of, and communicate from, a moving vehicle to a central hub and vice versa. This communications system is expected to operate in an extremely noisy channel in the presence of fading. In order to achieve high levels of data integrity, it is essential to employ forward error correcting (FEC) encoding and decoding techniques in such mobile satellite systems. A constraint length k = 7 FEC decoder has been implemented in a single chip for such systems. The single chip implementation of the maximum likelihood decoder helps to minimize the cost, size, and power consumption, and improves the bit error rate (BER) performance of the mobile earth terminal (MET).

Roy, Ashim↗

Real-time antenna fault diagnosis experiments at DSS 13

Experimental results obtained when a previously described fault diagnosis system was run online in real time at the 34-m beam waveguide antenna at Deep Space Station (DSS) 13 are described. Experimental conditions and the quality of results are described. A neural network model and a maximum-likelihood Gaussian classifier are compared with and without a Markov component to model temporal context. At the rate of a state update every 6.4 seconds, over a period of roughly 1 hour, the neural-Markov system had zero errors (incorrect state estimates) while monitoring both faulty and normal operations. The overall results indicate that the neural-Markov combination is the most accurate model and has significant practical potential.

Mellstrom, J.↗

Theoretical computation of trace gases retrieval random error from measurements of high spectral resolution infrared sounder

The purpose of this paper is to demonstrate the trace gas profiling capabilities of future passive high spectral resolution (1 cm(exp -1) or better) infrared (600 to 2700 cm(exp -1)) satellite tropospheric sounders. These sounders, such as the grating spectrometer, Atmospheric InfRared Sounders (AIRS) (Chahine et al., 1990) and the interferometer, GOES High Resolution Interferometer Sounder (GHIS), (Smith et al., 1991) can provide these unique infrared spectra which enable us to conduct this analysis. In this calculation only the total random retrieval error component is presented. The systematic error components contributed by the forward and inverse model error are not considered (subject of further studies). The total random errors, which are composed of null space error (vertical resolution component error) and measurement error (instrument noise component error), are computed by assuming one wavenumber spectral resolution with wavenumber span from 1100 cm(exp -1) to 2300 cm(exp -1) (the band 600 cm(exp -1) to 1100 cm(exp -1) is not used since there is no major absorption of our three gases here) and measurement noise of 0.25 degree at reference temperature of 260 degree K. Temperature, water vapor, ozone and mixing ratio profiles of nitrous oxide, carbon monoxide and methane are taken from 1976 US Standard Atmosphere conditions (a FASCODE model). Covariance matrices of the gases are 'subjectively' generated by assuming 50 percent standard deviation of gaussian perturbation with respect to their US Standard model profiles. Minimum information and maximum likelihood retrieval solutions are used.

Huang, Hung-Lung↗

Improving classification of crop residues using digital land ownership data and Landsat TM imagery

Plant residue on the surface of cultivated soils in Miami County, Indiana is analyzed in terms of quantity and type with Landsat TM data to generate information for a conservation program for agricultural soil. The Landsat data are enhanced with land-ownership data in a geographic information system to facilitate classification with maximum-likelihood, minimum-distance, and neural-network classifiers. The most effective classifications resulted from the use of the neural network on the enhanced TM data.

Zhuang, Xin↗