Matched field processing (MFP) refers to signal and array processing techniques in which, rather than a planewave arrival model, complex-valued (amplitude and phase) field predictions for propagating signals are used. Matched field processing has been successfully applied in ocean acoustics. In this paper the extension of MFP to the electromagnetic domain, i.e., electromagnetic (EM) MFP (EM-MFP) is described. Simulations of EM-MFP in the tropospheric setting suggest that, under suitable conditions, EM-MFP methods can enable EM sources to be both detected/localized and used as sources of opportunity for estimating the environmental parameters that determine EM propagation.

A third-order Wigner distribution called the Wigner bispectrum is presented. This mixed time-frequency-frequency representation extends the standard Wigner distribution in the same way that the bispectrum extends the power spectrum. The Wigner bispectrum preserves many of the essential features and appealing properties of the Wigner distribution.>

An adaptive matched differential pulse-code modulator (AMDPCM) is analyzed. The adaptation of the symmetric uniform quantizer parameter\Delta_{n}is performed by fixed multipliers assigned to the quantizer output levels. The input is stationary first-order Gauss-Markov. The correlation of the samples is used as the leakage parameter in the matched integrator, with the predictive reconstruction similarly matched. For a4-level quantizer and multipliers(\gamma^{-1}, \gamma)the limiting joint distribution of the prediction error and\Delta_{n}is derived and the asymptotic sample-point and time-averaged mean-square error (rose) and mean and variance of\Delta_{n}as functions of\gamma \in (1,2]are computed and plotted. It is found that the asymptotic performance of AMDPCM does not depend on the choice of\Delta_{0}, that the increase in mse incurred by using A(M)DPCM instead of (M)DPCM with\Delta_{opt}is small, with mse(A(M)DPCM)\downarrow \min_{\Delta}mse ((M)DPCM) as\gamma \downarrow 1, and that the signal-to-noise ratio of AMDPCM does not depend on the input power.

While delta modulation (DM) simply compares the current predictive estimate of the input with the current sample, delayed delta modulation (DDM) also compares with the upcoming sample so as to detect and anticipate slope overloading. Since this future sample must be available before the present output is determined and the estimate updated, delay is introduced at the encoding. The performance of DDM with perfect integration and step-function reconstruction is analyzed for each of three random input signals. In every case, the stochastic stability of the system is established. For a discrete time, independent and identically distributed input, the (limiting) joint distribution of input and output is derived, and the (asymptotic) mean-square sample point error mse(SP) is computed when the input is Gaussian. For a Wiener input, the joint distribution of the sample point and prediction errors is derived, and mse(SP) and the time-averaged mse (mse(TA)) are computed. For a stationary first-order Gauss-Markov input, the joint distribution of input and output is derived and mse(SP) and mse(TA) computed. Graphs of the mse's illustrate the improvement attainable by using DDM instead of DM. With optimal setting of parameters, mse(SP) (mse(TA)) is reduced about15percent (35percent).

A simple class of quantizers is introduced which are asymptotically optimal, as the number of quantization levels increases to infinity, with respect to a meanrth power absolute error distortion measure. These asymptotically optimal quantizers are very easy to compute. Their performance is evaluated for several distributions and compares favorably with the performance of the optimal quantizers in all cases for which the latter have been computed. In addition their asymptotic robustness is studied under location, scale, and shape mismatch for several families of distributions.