Download Adaptive antennas and receivers by Melvin M. Weiner PDF

By Melvin M. Weiner

In our sleek age of distant sensing, instant communique, and the approximately unending checklist of alternative antenna-based functions, complicated difficulties require more and more subtle suggestions. traditional antenna structures are not any longer fitted to high-noise or low-signal functions equivalent to intrusion detection. Detailing powerful ways to non-Gaussian vulnerable sign detection, Adaptive Antennas and Receivers presents an authoritative advent to state of the art examine at the modeling, trying out, and alertness of those applied sciences. Edited through leading edge researcher and eminent professional Melvin M. Weiner, this booklet is the 1st to combine 3 complex techniques to non-Gaussian susceptible sign detection right into a unmarried reference: homogeneous partitioning of the surveillance quantity, adaptive antennas, and adaptive receivers. Comprising self-contained chapters contributed by way of popular specialists equivalent to Donald D. Weiner and Ronald Fante, every one bankruptcy explores the concepts, theoretical foundation, and functions of the method below dialogue. The publication considers sign detection within the presence of exterior noise akin to litter residue, interference, atmospheric noise, jammers, exterior thermal noise, in vivo surrounding tissue, and camouflaging fabric, making it perfect to be used throughout a large spectrum of functions. This authoritative reference provides greater than 750 figures and tables, 1460 equations, and 640 references. Adaptive Antennas and Receivers is a perfect source for bettering functionality in surveillance, communique, navigation, synthetic intelligence, laptop tomography, neuroscience, and intrusion detection platforms, to call just a couple of.

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Example text

A fault can arise in two different ways. First, it occurs when two or more different SPAs, those that are applied to the same data, result in different output interpretations. , the same SPA with different parameter values) result in different interpretations when applied to the same data. An example of the first kind of fault would be the situation where SPA # 1, a power level detector, indicates a power level consistent with the BN while SPA # 2, Ozturk’s distribution identification algorithm, indicates a nonGaussian distribution.

In practice, Ozturk’s algorithm may indicate that, several different PDFs are suitable for approximating a particular disturbance. When this is the case, it may be desirable to implement several LODs and fuse the results. Hence, descriptors should be allocated to each cell for describing one or more joint PDFs in terms of their types, scales, locations, and shape parameters. 3. TARGET D ETECTION Indexing of the Cells Target Detection Step 2: Target detection Preprocessor Mapping of the Space Step 1: Classification of the test cells As mentioned previously, the type of detector depends upon whether the detection problem has been classified as a strong signal case (SSC), weak signal case (WSC), or intermediate signal case (ISC).

Mapping S. 2 S. 2 SSC S. 3 S. 1 WSC ISC S. 4 S. 7 S. 4 S. 7 S. 5 S. 8 S. 5 S. 8 S. 12 S. 6 S. 9 S. 12 S. 6 S. 9 S. 10 S. 11 Indexing S. 3 S. 10 Detection (Preprocessing) S. 11 S. : Signal processing block (processes the data) S. : Signal interpretation block (gives an interpretation to the S. P. 4 Signal processing and interpretation blocks in a feedforward expert system. 1 Functioning of the Signal Processing and Interpretation Blocks in a Feedforward Expert System Preprocessing S. P. 1 Collects data.

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