Download PDF by Gaspare Galati: Advanced radar techniques and systems

By Gaspare Galati

ISBN-10: 086341172X

ISBN-13: 9780863411724

This significant reference quantity includes 13 huge themes, contributed via 9 best researchers (from 6 international locations around the world (including contributions from Europe, Japan and the USA). parts of assurance comprise: process structure, layout, muddle and suppression, surveillance and artificial aperture radars, detection, rejection of jamming, goal identity, phased and matched arrays, and radar sign processing. Contributions are whole mathematically and plenty of comprise unique appendices.

Also available:

Optimised Radar Processors - ISBN 9780863411182 rules of Space-Time Adaptive Processing, third variation - ISBN 9780863415661

The establishment of Engineering and know-how is without doubt one of the world's best specialist societies for the engineering and expertise neighborhood. The IET publishes greater than a hundred new titles each year; a wealthy mixture of books, journals and magazines with a again catalogue of greater than 350 books in 18 diverse topic components together with:

-Power & power -Renewable power -Radar, Sonar & Navigation -Electromagnetics -Electrical dimension -History of know-how -Technology administration

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Example text

This function gives zero penalty if all the components of the estimation error are less than £, and penalty one in the opposite case (one component or more greater than e). t. 167) since an error occurs whenever one or more than one estimated component has an error greater than £. 168) and the first integral equals 1. 169) and to minimise the cost function it is necessary to choose p(6\z) as large as possible. 170) In practice, this estimator tends to maximise the a posteriori probability, and for this reason it is called the MAP estimator; it is also called the conditional mode estimator, because it leads to the maximum value of the PDF p(duc\z), that is the (conditional) mode.

221) Taking the logarithm of eqn. a, can be obtained putting equal to zero the power of the stochastic component of the useful signal in eqns. 218.

The estimate does not depend on the noise variance O2. 144) so that, as is intuitive, the precision of the estimate is improved as TV increases. Let us now find the CRLB for the stated problem. e. 1(0) = — E[L"(x\0)] is used. 145) So we have obtained the important result that this ML estimator is efficient, in the sense that it satisfies the CRLB with the equality sign. 4 Comments on the application of the maximum likelihood estimation with one example^ The examples of the application of ML estimation shown before lead to solutions in closed form, that is 0ML is expressed as a well-defined function of the observations.

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Advanced radar techniques and systems by Gaspare Galati

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