By Pramod K. Varshney
This ebook offers an introductory remedy of the basics of decision-making in a allotted framework. Classical detection concept assumes that whole observations can be found at a imperative 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 disbursed processors, or processed information (compressed observations) are conveyed to a fusion middle that makes the worldwide selection. traditional detection concept has been prolonged in order that it could take care of such allotted detection difficulties. A unified remedy of contemporary advances during this new department of statistical selection idea is gifted. disbursed detection below diverse formulations and for quite a few detection community topologies is mentioned. This fabric isn't to be had in the other booklet 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. a number of examples are offered in the course of the publication. it's assumed that the reader has been uncovered to detection concept. The ebook 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 ebook. lots of individuals have performed a key function within the finishing touch of this book.
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Additional info for Distributed Detection and Data Fusion
05, both of the unequal threshold solutions are greater than 1/4. 3. 2. 8 50 3. 05, one of the solutions becomes less than 1/4 and is, therefore, set equal to 1/4. The other one is determined based on the threshold value 1/4. 3 yields the minimum risk. 98, the t l tz solution results in a smaller value of risk and is, therefore, used. * M-ary Hypothesis Testing Using N Sensors Next, we consider the more general M-hypothesis, N-sensor distributed detection problem. 4. Once again, N detectors observe a common phenomenon and make local decisions regarding the hypothesis present.
When there are several local minima. each must be examined to determine the globally optimum solution. It is important to observe that the two thresholds are coupled and this is a result of our objective of systemwide optimization. In general, the resulting thresholds are not the same as would be obtained if each local detector was optimized independently. Next, we consider a special case where the thresholds t l and t2 decouple. Let the cost assignment be = C III = 0, Cooo COlO = C IOO = COli = C IOI = 1, CllO = C()(J1 = k.
28) = OIH), j = 46 3. Distributed Bayesian Detection: Parallel Fusion Network p(U 2 =0 IH~ =erf O"IOg t 2 ( ml-mo + J m I -m0 , 20" and p(u2 = OIH) = erf O"IOg t 2 - ( ml-mo where erf(x) 1 y2 exp( -_) dy . 28) and its companion equation, (k- 1) + (2- k) erf ~= (oIOgt, nll-m. 29) ",,-m,) . 30) 1 + (k- 2) erf (OIOgt, -~) ml-m. 2a and (k- 1) + (2- k) erf ~= (oIOgt. + 2a '"l-mO '"l-m. 30) yields the thresholds t l and t2• As noted before, multiple solutions may result, and each one must be examined to determine the best solution.
Distributed Detection and Data Fusion by Pramod K. Varshney