The high resolution statistics of a periodogram-type spectral estimate for a periodic deterministic signal plus Gaussian noise are well understood. If, however, the signal plus noise is first passed through a nonlinear memoryless device (such as a hard limiter), the situation becomes much more complicated. The main result of this correspondence is the derivation of the high resolution limit of the joint statistics of theIandQcomponents of a periodogram for a certain class of nonlinear devices with a deterministic periodic signal and Gaussian noise as input. This result permits the calculation of the asymptotic central moments of the spectral estimate formed from the foregoing periodogram. It then becomes possible to subject a device employing this technique for spectral estimation to a detailed performance analysis.

Maximum-entropy processing is a method for computing the power density spectrum from the firstNlags of the autocorrelation function. Unlike the discrete Fourier transform, maximum-entropy processing does not assume that the other lag values are zero. Instead, one mathematically ensures that the fewest possible assumptions about unmeasured data are made by choosing the spectrum that maximizes the entropy for the process. The use of the maximum entropy approach to spectral analysis was introduced by Burg [1]. In this correspondence, the authors derive the maximum-entropy spectrum by obtaining a spectrum that is forced to maximize the entropy of a stationary random process.