Measurement of the Wiener kernels of a non- linear system by cross-correlation.
Wiener kernels of nonlinear system based on cross- correlation techniques
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Wiener kernels of nonlinear system based on cross- correlation techniques
Message estimation of sample function from continuous Gaussian random process, establishing and evaluating optimal bounds for procedure
Analog computer method for approximating stationary Gaussian random process depending only on position parameter
Capacity of discrete time Gaussian channels
Asymptotic relative efficiency of mixed statistical threshold tests with Gaussian samples
Phase modulated laser light beam statistical analysis, using Michelson interferometer with piezoelectric crystal driven mirror connected to Gaussian random noise generator
Rate distortion over band limited feedback channels, considering capacity of additive Gaussian white noise channel
Extreme value theory is examined to explain the cause of the bias and spread in performance of communications systems characterized by low bit rates and high data reliability requirements, for cases in which underlying noise is Gaussian or perturbed Gaussian. Experimental verification is presented and procedures that minimize these effects are suggested. Even under these conditions, however, extreme value theory test results are not particularly more significant than bit error rate tests.
Performance tests (error probability measurements) of communications systems characterized by low bit rates and high reliability requirements frequently utilize classical extreme value theory (EVT) to avoid the excessive test times encountered with bit error rate (BER) tests. If the underlying noise is Gaussian or perturbed Gaussian, the EVT error estimates have either excessive bias or excessive variance if an insufficient number of test samples is used. EVT is examined to explain the cause of this bias and spread. Experimental verification is made by testing a known Gaussian source, and procedures that minimize these effects are described. It seems apparent that even under the best of conditions the EVT test results are not particularly better than those of BER tests.
It is shown that for satellites in circular Earth orbits with altitudes of 500 kilometers to 1500 kilometers, and for satellites in elliptical orbits with an approximate 4000 kilometer height of perigee, a high degree least squares polynomial (e.g., a 9th or 10th degree in some cases) is required to smooth both range and range rate data for purposes of input to orbit determination programs. In order to circumvent this problem, functions of range and range rate are smoothed with lower degree least squares polynomials (e.g., 3rd and 4th degree) and it is shown that under the above geometric constraints the standard deviation of fit can be reduced to levels commensurate with typical S-band tracking system resolution which is 1 to 2 meters in range and 0.005 meters/second in range rate for a 1 per second data rate. Also shown are the effects of Gaussian random noise, biases, and periodic noise. This analysis includes numerous examples applied to the 44 point data smoothing interval currently used in much of the operational preprocessing at the Goddard Space Flight Center.
This paper presents the best obtainable random coding and expurgated upper bounds on the probabilities of undetectable error, of t-order failure (advance to depth t into an incorrect subset), and of likelihood rise in the incorrect subset, applicable to sequential decoding when the metric bias G is arbitrary. Upper bounds on the Pareto exponent are also presented. The G-values optimizing each of the parameters of interest are determined, and are shown to lie in intervals that in general have nonzero widths. The G-optimal expurgated bound on undetectable error is shown to agree with that for maximum likelihood decoding of convolutional codes, and that on failure agrees with the block code expurgated bound. Included are curves evaluating the bounds for interesting choices of G and SNR for a binary-input quantized-output Gaussian additive noise channel.
This paper considers the control of a continuous linear plant disturbed by white plant noise when the control is constrained to be a piecewise constant function of time; i.e. a stochastic sampled-data system. The cost function is the integral of quadratic error terms in the state and control, thus penalizing errors at every instant of time while the plant noise disturbs the system continuously. The problem is solved by reducing the constrained continuous problem to an unconstrained discrete one. It is shown that the separation principle for estimation and control still holds for this problem when the plant disturbance and measurement noise are Gaussian.
The development is examined of a new edge detector which is shown to perform adequately in the non-Gaussian multiplicative noise environment which characterizes radar images. This edge detector operates over larger local neighborhood and is less susceptible to noise than previous edge detectors and is therefore more suitable for radar. In addition, a radar image noise model is employed for the design of this new operator. This edge detector is unique in that it is assumed that every local area belongs to either the class of local areas not containing edges or to the class of local areas containing edges. Each pixel's local neighborhood is then assigned to one of these two classes using a statistical hypothesis (a likelihood ratio) test. It is demonstrated that this algorithm is useful for detecting edges in radar images.
The present investigation is concerned with the error control strategy used in connection with digital data networks, taking into account the capacity of the end-to-end channel. It is assumed that the only source of noise is Gaussian and that bandwidth limitation on capacity can be ignored. Attention is given to L links in tandem with binary repeaters, the asymptotic capacity with a large number of links, some alternative repeater strategies, and approaches for lowering the bit error probability. Numerical results are presented, taking into consideration capacity versus link error probability for various numbers of links.
A finite element formulation is presented for the analysis of beams and rectangular plates undergoing large deflections subjected to Gaussian white noise excitations. Single-mode response is assumed in the present formulation. Root-mean-square (RMS) maximum deflections for simply supported and clamped beams and plates at various sound spectrum levels are obtained and compared with solutions using the Fokker-Planck-Kolmogorov equation and the equivalent linearization methods. RMS maximum stains and equivalent linear frequencies are compared with the equivalent linearization results for assessment of the accuracy of the finite element method.
Tifft (1980, 1982) claimed that observations of double galaxies reveal a 72 km/s periodicity. Sharp showed that the 'periodicities' in the observations are completely consistent with the statistics of small numbers. Here it is shown that Tifft's statistical procedure would ascribe a periodicity to small sets of Gaussian random noise. Satisfying the null hypothesis that the observations are not samples drawn from a normal population would require the acquisition of at least an order of magnitude more data.
The authors describe an error-correction coding approach that allows operation in either burst or continuous modes at data rates of multiple hundreds of megabits per second. Bandspreading is low since the code rate is 7/8 or greater, which is consistent with high-rate link operation. The encoder, along with a hard-decision decoder, fits on a single application-specific integrated circuit (ASIC) chip. Soft-decision decoding is possible utilizing applique hardware in conjunction with the hard-decision decoder. Expected coding gain is a function of the application and is approximately 2.5 dB for hard-decision decoding at 10-5 bit-error rate with phase-shift-keying modulation and additive Gaussian white noise interference. The principal use envisioned for this technique is to achieve a modest amount of coding gain on high-data-rate, bandwidth-constrained channels. Data rates of up to 300 Mb/s can be accommodated by the codec chip. The major objective is burst-mode communications, where code words are composed of 32 n data bits followed by 32 overhead bits.
A probability density function for the variability of ensemble averaged spectral estimates from helicopter acoustic signals in Gaussian background noise was evaluated. Numerical methods for calculating the density function and for determining confidence limits were explored. Density functions were predicted for both synthesized and experimental data and compared with observed spectral estimate variability.