A synthetic aperture radar (SAR) is a radar sensor that provides azimuth resolution superior to that achievablewithits real beambysynthesizingalongapertureusingplatformmotion.

An acoustic enclosure usually reduces the intelligibility of the speech transmitted through it because the transmission path is not ideal.

In this article we examine the problem of communication channel equalization and how it relates to the inversion of a linear system of equations.

Signal reception has numerous applications in communications, radar, sonar, and geoscience among others. However, the adverse effects of noise in these applications limit their utility.

Image recovery constitutes a significant portion of the inverse problems in image processing. Here, by image recoverywe refer to two classes of problems, image restoration and image reconstruction.

The focus of this chapter is on inverse problems—what they are, where they manifest themselves in the realmof digital signal processing (DSP), and how they might be “solved1.”

This section addresses the inverse problem in robust speech processing. A problem that speaker and speech recognition systems regularly encounter in the commercialized applications is the dramatic degradation of performance due to the mismatch of the training and operating environments.

Computed tomography is the process of reconstructing the interiors of objects fromdata collected based on transmitted or emitted radiation.

Signal recovery has been an active area of research for applications in many different scientific disciplines.

One of the earliest andmost successful applications of adaptive filters is adaptive channel equalization in digital communication systems.

In comparison with adaptive finite impulse response (FIR) filters, adaptive infinite impulse response (IIR) filters offer the potential to implement an adaptive filter meeting desired performance levels, as measured by mean-square error, for example, with much less computational complexity.

One of the earliest works on transformdomain adaptive filtering was published in 1978 by Dentino et al. [4], in which the concept of adaptive filtering in the frequency domain was proposed.

The central problemin estimation is to recover, to good accuracy, a set of unobservable parameters fromcorrupted data. Several optimization criteria have been used for estimation purposes over the years, but the most important,

Adaptive filters are systems that adjust themselves to a changing environment. They are designed to meet certain performance specifications and are expected to perform reasonably well under the operating conditions for which they have been designed.

In adaptive filtering, the least-mean-square (LMS) adaptive filter [1] is the most popular and widely used adaptive system, appearing in numerous commercial and scientific applications.

An adaptive filter is a computational device that attempts to model the relationship between two signals in real time in an iterative manner.

Processes encountered in statistical signal processing, communications, and time series analysis applications are often assumed stationary.

Linear parametric models of stationary random processes, whether signal or noise, have been found to be useful in a wide variety of signal processing tasks such as signal detection, estimation, filtering,

Estimation is one of four modeling problems. The other three are representation (how something should be modeled), measurement (which physical quantities should be measured and how they should be measured), and validation (demonstrating confidence in the model)

The main objective of spectrumestimation is the determination of the power spectrumdensity (PSD) of a random process.