By Pramod K. Varshney
This publication presents an introductory remedy of the basics of decision-making in a allotted framework. Classical detection thought assumes that entire observations can be found at a valuable processor for decision-making. extra lately, many purposes were pointed out during which observations are processed in a disbursed demeanour and judgements are made on the allotted processors, or processed info (compressed observations) are conveyed to a fusion heart that makes the worldwide choice. traditional detection idea has been prolonged in order that it may care for such disbursed detection difficulties. A unified therapy of contemporary advances during this new department of statistical choice idea is gifted. allotted detection less than varied formulations and for various detection community topologies is mentioned. This fabric isn't really to be had in the other publication and has seemed rather lately in technical journals. the extent of presentation is such that the hook can be utilized as a graduate-level textbook. quite a few examples are awarded through the publication. it truly is assumed that the reader has been uncovered to detection idea. The e-book also will function an invaluable reference for practising engineers and researchers. i've got actively pursued examine on disbursed detection and information fusion during the last decade, which eventually me in scripting this publication. a lot of persons have performed a key position within the of completion of this book.
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Extra resources for Distributed Detection and Data Fusion
Example text
In the Gaussian case under consideration, the adaptive threshold is based on the estimate of the mean power level of the background obtained from the range cells surrounding the test cell. 3. The square-law detected range samples are sent serially into a shift register of length (N + 1). The leading N/2 samples and the lagging N/2 samples surrounding the test cell form the reference window. The estimate of the background noise power, Z, is obtained from the reference window samples. Two algorithms for the computation of Z will be described later in this section.
It is a reasonable assumption because, at the decision stage, the likelihood ratio is likely to exceed the threshold only by a small amount. 14) Once again, the log likelihood ratio is often attractive from a computational standpoint. 17) 22 2. 18) It was mentioned previously that the number of observations required for tenninating the test is random. We compute the average number of observations required for terminating the test under each hypothesis. Let us assume that the test tenninates at the Kth stage.
21) In this special case, the threshold t l is a constant and is the same as obtained by independently applying the minimum probability of error decision rule at each local detector. It should be emphasized that the 44 3. 20) with k = 2 is a very special cost assignment where t l and t2 are independent of each other. This is not the case in general. Consider the following examples for illustration. 1 Let us assume that the two local observations are conditionally independent, identical and are Gaussian distributed.