This paper presents experimental results for a parallel pipeline STAP system with I/O task implementation using the parallel file systems on the Intel Paragon and the IBM SP. In our previous work, a parallel pipeline model was designed for radar signal processing applications on parallel computers. Based on this model, we implemented a real STAP application which demonstrated the performance scalability of this model in terms of throughput and latency. In this paper we study the effect on system performance when the I/O task is incorporated in the parallel pipeline model. There are two alternative for I/O implementation: embedding I/O in the pipeline or having a separate I/O task. From these two I/O implementations, we discovered that the latency may be improved when the structure of the pipeline is reorganized by merging multiple tasks into a single task. All the performance results shown in this paper demonstrated the scalability of parallel I/O implementation on the parallel pipeline STAP system.

The problem of detecting radar signals embedded in clutter is an area of great interest. In many radar applications, it is important to set thresholds to achieve a false alarm probability (P/sub F/) of 10/sup -5/ or lower. Using conventional Monte Carlo techniques, where thresholds are set based on raw percentiles, an extremely large number of samples is required. We use the generalized Pareto distribution to approximate the extreme tail of the distributions and propose the ordered sample least squares (OSLS) method for estimating its parameters.

The Gaussian multichannel binary detection problem is considered. A multichannel generalized likelihood ratio is implemented using a model-based approach where the signal is assumed to be characterized by an autoregressive vector process. Detection performance is obtained for the special case where the underlying processes are assumed to have known autoregressive process parameters. Specifically, results for two-channel signal vectors containing various temporal and cross-channel correlation are obtained using a Monte Carlo procedure. These results are plotted versus signal-to-noise ratio and are shown to be bounded by available optimal detection curves. The two-channel detection results are shown to decrease as (S/N)/sub 2/ decreases and approach the superior angle channel performance asymptotically. A likelihood ratio for a more general class of processes with correlated Gaussian quadrature components is noted.>

Mutual coupling between array sensors is studied, and schemes for compensating for these effects are proposed. Particular attention is given to nonsearch algorithms such as ESPRIT and the moving window. Computer simulations are included to demonstrate the effectiveness of the proposed compensation schemes.>

Detection of a memoryless nonlinear functional of a Gaussian process in additive Gaussian white noise is considered. The Volterra functional expansion for the likelihood ratio, and two examples of calculating the kernels are presented. It is shown that kernels up to third order can be obtained for a hard-limited Gaussian process and for the absolute value of a Gaussian process. For the case of hard limiting, the kernels are nonlinear functions of the autocorrelation of the Gaussian process. For the absolute value case, the kernels are nonlinear functions of the kernel derived for the linear problem. A Monte Carlo simulation of receiver performance is presented for the case of detection of the absolute value of a first-order Butterworth process in additive Gaussian white noise. The suboptimal detector is obtained by truncating the log likelihood ratio to second order.

Resolution of two sinusoidal signals of frequencies f1and f2requires a time record of length at least1/|f_{1}-f_{2}|. Resolution of two decaying exponentials of complex frequencies s1= σ1+jω1and s2= σ2+jω2, with\sigma_{2} \leq \sigma_{1}, by correlation coefficient is independent of the ratios σ2/σ1, and (ω1- ω2)/σ1for a time record of length greater than3/|\sigma_{1}|. This is also the condition for near-orthogonalization of a set of complex exponentials, with small error.

The effect of a Gaussian-distributed phase modulation on the envelope detection of a sinusoid is investigated. Asymptotic receiver operating characteristics are obtained for Gaussian-distributed phase modulation processes that satisfy a central limit theorem due to Sun. Numerical results are presented for a first-order Butterworth phase modulation spectrum.

The paper describes very recent and promising results on a new approach to noise correction for pole-zero modeling of signals by the pencil-of-functions method. In contradistinction to spectral subtraction techniques where only amplitude information is emphasized (and phase is ignored), we propose a method that (a) estimates the noise spectral density for the frame, and then (b) performs the subtraction of the noise correlation matrix from the correlation matrix of the information signals. The information signals are, of course, generated by processing the test signal by a cascade of first-order low pass signals, as described in earlier papers. Improvement resulting from this noise correction technique is particularly remarkable when judged by the pole-estimation accuracy, A comparison of the poles for multiple noise runs for three methods, namely, (a) Prony method, (b) Pencil-of-functions method, and (c) Pencil-of-functions method with noise correction, is given. The signal considered was the impulse response of a third order rational filter H(z), masked by noise with an SNR=12 dB.

Pole-zero modeling of low-pass signals, such as an electromagnetic-scatterer response, is considered in this paper. It is shown by use of pencil-of-functions theorem that (a) the true parameters can be recovered in the ideal case (where the signal is the impulse response of a rational function H(z)), and (b) the parameters are optimal in the generalized least-squares sense when the observed data are corrupted by additive noise or by systematic error. Although the computations are more involved than in all-pole modeling, they are considerably less than those required in iterative schemes of pole-zero modeling. The advantages of the method are demonstrated by simulation example and through application to the electromagnetic response ofa scatterer. The paper also includes very recent and tantalizing results on a new approach to noise correction. In contradistinction with spectral subtraction techniques, where only amplitude information is emphasized (and phase is ignored), we propose a method that (a) estimates the sample variance for the particular data frame, and then performs the subtraction from the Gram matrix.

A newly derived expression for the higher order crosscorrelation function of two jointly stationary Gaussian random processes is used to determine the output autocorrelation function of a Volterra system driven by a Gaussian input and to simplify a previously published expression for the output power spectral density and its derivation.

The object of this work is to investigate the problem of Volterra systems driven by random inputs. General expressions for the second-order output statistics of a Volterra system are obtained and formalized. The input-output crosscorrelation and output autocorrelation functions are expressed in terms of higher order input autocorrelation functions. In the frequency domain, the cross-power and output power spectral densities are expressed in terms of higher order input power spectral densities. These expressions are used to obtain closed-form equations for the cross-power and output power spectral densities of a Volterra system driven by a real, stationary, zero-mean Gaussian input.

Since conventional FM demodulators are incapable of accurately demodulating very wide-band frequency-modulated signals, a new approach to the problem is needed. Based upon a theorem by Sandberg, a practical procedure is presented for demodulating not only typical FM signals but also those employing modulation bandwidths and/or frequency deviations on the order of the cartier frequency. The proposed demodulator utilizes an iterative recovery procedure. The first iteration is analogous to demodulation by a conventional zero crossing discriminator and results in severe distortion of the modulation under wide-band conditions. This distortion is reduced by successive iterations. Because, in practice, the iterative process is truncated after a finite number of terms, a procedure for computing the coefficients of these terms for minimum mean-squared error is given. Using computer simulation, the demodulation technique is demonstrated to work effectively for several very wide-band FM signals.

This correspondence discusses the transformations between the output autocorrelation functions of functionally related zero-memory nonlinear devices excited by signal plus Gaussian noise. The results are applied to show how output autocorrelation functions can easily be obtained for exponential and half-wave power law devices.