By Stergios Stergiopoulos
Advances in electronic sign processing algorithms and desktop know-how have mixed to supply real-time platforms with functions a ways past these of simply few years in the past. Nonlinear, adaptive equipment for sign processing have emerged to supply greater array achieve functionality, in spite of the fact that, they lack the robustness of traditional algorithms. The problem continues to be to advance an idea that exploits some great benefits of both-a scheme that integrates those tools in sensible, real-time systems.The complex sign Processing instruction manual is helping you meet that problem. past delivering an excellent creation to the rules and functions of complicated sign processing, it develops a time-honored processing constitution that takes good thing about the similarities that exist between radar, sonar, and scientific imaging structures and integrates traditional and nonlinear processing schemes.
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Additional info for Advanced Signal Processing Handbook: Theory and Implementation for Radar, Sonar, and Medical Imaging Real Time Systems
In their own individual ways, the LMS and GAL algorithms are just two members of the stochastic gradient family of linear adaptive filters, although it must be said that the LMS algorithm is by far the most popular member of this family. 2 Least-Squares Estimation The second approach to the development of linear adaptive filtering algorithms is based on the method of least squares. According to this method, we minimize a cost function or index of performance that is defined as the sum of weighted error squares, where the error or residual is itself defined as the difference between some desired response and the actual filter output.
Successively, it would compare the model to an existing land mine engineering database deciding or assisting the operator to make a decision as to the nature of the detected object. A general approach of the engineering database concept and its applicability in the aforementioned DSP systems would assume that an effective engineering database will be a function of the available information on the subjects of interest, such as underwater targets, radar targets, and medical diagnostic images. Moreover, the functionality of an engineering database would be highly linked with the multisensor data fusion process, which is the subject of discussion in the next section.
In any case, a continuous-time signal may always be represented by a sequence of samples that are derived by observing the signal at uniformly spaced instants of time. No loss of information is incurred during this conversion process provided, of course, we satisfy the well-known sampling theorem, according to which the sampling rate has to be greater than twice the highest frequency component of the continuous-time signal (assumed to be of a low-pass kind). We may thus represent a continuous-time signal u(t) by the sequence u(n), n = 0, ±1, ±2, …, where for convenience we have normalized the sampling period to unity, a practice that we follow throughout this chapter.