Improved multilevel quantization for detection of narrowbandsignals

Nearly optimum quantization levels for multileveled quantizers in radar receivers and distributed-detection are calculated for preassigned false-alarm probability Q₀ by maximizing the detection probability Q_d after replacing both Q ₀ and (1-Q_d) by the saddlepoint approximations. Narrowband signals of random phase and with both fixed and Rayleigh-fading amplitudes in Gaussian noise are treated, and the loss in signal detectability incurred by quantization is estimated     

Approximate Evaluation of Detection Probabilities in Radar and Optical Communications

Cumulative probability distributions such as occur in radar detection problems are approximated by a new version of the saddlepoint method of evaluating the inverse Laplace transform of the moment-generating function. When the number of radar pulses integrated is large, the approximation of lowest order yields good accuracy in the tails of the distributions, yet requires much less computation than standard recursive methods. Greater accuracy can be achieved upon summing the residual series by converting it to a continued fraction. The method is applied to evaluating the error-function integral and the Mth-order Q function, and to approximating the inverse of the chi-squared distribution. Cumulative distributions of discrete random variables, needed for determining error probabilities in optical communication receivers that involve counting photoelectrons, can be approximated by a simple modification of the method, which is here applied to the Laguerre distribution.     

Evaluating the Detectability of Gaussian Stochastic Signals by Steepest Descent Integration

It is shown how to compute the detection probability of certain signals by numerical integration of the Laplace inversion integral involving the characteristic function or the moment-generating function of the detection statistic. The contour of integration is taken as the path of steepest descent of the integrand and is determined numerically as the integration proceeds. The method is applied to calculating the performance of the optimum detector of a Gaussian stochastic signal in white noise when the signals actually present have a different average s.n.r. from that assumed in the design. Results are presented for narrowband signals with Lorentz and rectangular spectral densities. The detectability of the former is shown to be more sensitive than that of the latter to the value of the design s.n.r. The relative disadvantage of the threshold detector, also assessed by this method, is smaller for signals with a rectangular than for those with a Lorentz spectral density.     

Performance of receivers with linear detectors

The false-alarm and detection probabilities of a receiver summing M independent outputs of a linear detector are calculated by numerical saddlepoint integration. The saddlepoint approximation is also considered. Both constant-amplitude and Rayleigh-fading signals are treated, and the relative efficiency of the quadratic and the linear detectors for these is calculated for a broad range of values of M . The numerical integration method is the more efficient, the smaller the false-alarm probability or the false-dismissal probability, that is, under just those conditions for which the terms in the Gram-Charlier series oscillate most violently and the series becomes least reliable. The simpler saddlepoint approximation yields values that in those same regions have been found close enough to the exact probabilities to be adequate for most engineering purposes. The larger the number M of samples, the more efficient methods are     

Performance of an Ideal Quantum Receiver of a Coherent Signal of Random Phase

An ideal quantum receiver is to detect a coherent narrow-band optical signal in the presence of thermal background radiation. Curves are given both of the average probability of error in a binary communication system transmitting O's (blanks) and 1's (pulses) with equal probabilities, and of the probability of detection for various fixed values of the false-alarm probability.     

Detection probabilities for correlated Rayleigh fading signals

Both the method of saddlepoint integration and its associated saddlepoint approximation are applied to calculating the probability of detecting correlated Rayleigh-fading signals in Gaussian noise by means of a detector that integrates M samples of the output of a quadratic rectifier. The quadrature components of the signal samples are modeled as an autoregressive moving-average process, and specific results are exhibited for a first-order Markov process. By these methods the fluctuation loss can be computed for much larger values of M and for larger values of the detection probability than previously. Values calculated by the saddlepoint approximation prove to be close enough to the exact values to be useful over a broad range of signal parameters     

Evaluating Radar Detection Probabilities by Steepest Descent Integration

The probability of detection for radars employing noncoherent integration and a fixed threshold or cell-averaging constant false alarm rate (CA-CFAR) processor is computed by numerical contour integration in the complex plane. The technique is applied to both nonfluctuating and chi-squared fluctuating targets. A bound on the truncation error allows for a simple stopping rule for the numerical integration. The method has applicability to many problems in radar detection theory.     

Distribution of the envelope of a sum of random sine waves andGaussian noise

The cumulative distribution of the envelope of the sum of a sinusoidal signal, a number M of randomly phased interfering sine waves, and, possibly, Gaussian noise is expressed as the sum of Marcum's Q-function and an asymptotic series of Laguerre polynomials, much like the ordinary Edgeworth series for the distribution of the sum of a number of independent random variables. A test of the method with M=20 showed that its computation requires about 2% of the time needed for numerical inversion of the characteristic function of the distribution     

Optimum Detection of an Optical Image on a Photoelectric Surface

The detection of an optical image in the presence of uniform background light is based on a likelihood ratio formed of the numbers of photoelectrons emitted from small elements of a photoelectric surface onto which the image is focused. When diffraction is negligible and the surface has unit quantum efficiency, this detector is equipollent with the optimum detector of the image-forming light. Its performance is compared with that of the threshold detector and that of a detector basing its decisions on the total number of photoelectrons from a finite area of the image. The illuminance of the image is postulated to have a Gaussian spatial distribution. All three detectors exhibit nearly the same reliability.     

Resolution of Gaussian Images on a Photoelectrically Emissive Surface

Error probabilities are calculated for two detectors deciding which of two Gaussian images is present on the basis of the numbers and origins of photoelectrons ejected from the image plane. One detector is the optimum likelihood-ratio detector; the other compares the numbers of electrons from the two halves of the image plane. The results show that the performance of the former is better, although the latter is much easier to operate.