Features extracted from the bispectrum of radar signals are used for classification of unknown radar targets. The classification performance compared with the performance of other classifiers that are not based on higher-order spectral processing of the measured radar data. The radar signals used are experimental measurements that correspond to scattering from real radar targets. The data is corrupted with different types of disturbances that are likely to occur in a typical radar system. Although the number of data samples is relatively small and may be insufficient to produce very accurate bispectral estimates, it is concluded that the bispectrum classifier may outperform other known classifiers under conditions of colored noise and non-Gaussian noise.>

The quality, in terms of the bias and variance, of estimates of the Bhattacharyya coefficient based on n training samples from two classes described by multivariate Gaussian distributions is considered. The case where the classes are described by a common covariance matrix, as well as the case where each class is described by a different covariance matrix, is analyzed. Expressions for the bias and the variance of estimates of the Bhattacharyya coefficient are derived, and numerical examples are used to show the relationship between these parameters, the number of training samples, and the dimensionality of the observation space.>

A syntactic pattern recognition system is evaluated for applications to radar signal identification. Three different level-crossing-based pattern representation algorithms are considered. The utility of the resulting symbolic pattern representations is assessed by evaluating the performance of a maximum-likelihood classifier when the observed symbol strings are used as inputs to the decision algorithm. A syntax analysis algorithm is derived from the likelihood function classifier. Performance results of simulated classification experiments for both maximum-likelihood and language-theoretic classifiers are presented.>

The performance of binary frequency-shift-keyed communications over frequency-selective wide-sense-stationary uncorrelated-scattering Rayleigh fading channels is discussed. Previous analyses of FSK communications over frequency-selective channels have considered the average probability of error for specific models for the fading channel and typically assume that the two FSK signals are orthogonal. A technique for obtaining bounds on the average error probability for FSK in terms of one or two parameters obtainable from multipath spread or frequency correlation functions channel measurements is described.>

Upper and lower bounds on the Bayes risk for multiple, composite-hypothesis classification are obtained. Bounds on the Bayes risk for M simple classes are derived in terms of the risk functions for (M-1) classes, and so on, until the desired result depends only on the pairwise (M=2) Bayes risks. A method of computing upper and lower bounds on the pairwise Bayes risk for composite classes is developed. Algorithms for computing the upper and lower bounds for the general M-class case and for composite-hypothesis classes are presented. Numerical examples of the application of the bounding techniques to a problem involving the classification of aircraft are discussed. Results for the bounds and other performance measures are compared for the most interesting cases.>

The performance of binary differential phase-shift keyed (DPSK) communications over frequency-selective wide-sense-stationary uncorrelated-scattering fading channels is considered. A technique is described for obtaining bounds on the average error probability for DPSK in terms of one or two parameters obtained from multipath spread or frequency-correlation-function channel measurements. A method of approximating the performance of systems using various data-pulse shapes and systems where it is difficult to obtain a complete channel characterization is developed. Numerical examples and comparisons of the most interesting cases are given.>

New analytical results are obtained on the performance of quadriphase spread-spectrum multiple-access communications. The primary performance parameter considered is the signal-to-noise ratio at the receiver output. This parameter includes the effects of multiple-access interference and channel noise, and it has been shown to give a reliable estimate of the average probability of error.