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The problem of distributed estimation of an unknown deterministic scalar parameter (the target signal) in a wireless sensor network is considered, where each sensor receives a single snapshot of the field. It is assumed that the observation at each node randomly falls into one of two modes: a valid or an invalid observation mode. Specifically, mode one corresponds to the desired signal plus noise...
Algorithms are studied for distributed least-squares (DLS) estimation of a scalar target signal in sensor networks. Due to the observation locality and the limited sensing ability, the individual sensor estimates are far from being reliable. To obtain a more reliable estimate of the target signal, the sensors could collaborate by iteratively exchanging messages with their neighbors, to refine their...
Poor information means incomplete and insufficient information, such as small sample and unknown distribution. As for the evaluation of repeatability and reproducibility in an information poor process, statistical methods which relied on large sample sizes and known distributions may become ineffective. For this end, a method for analysis of the point variation, the interval variation, and the comprehensive...
Based on the fuzzy set theory and the norm theory, the fuzzy norm method is proposed to solve some of problems about interval estimation under the condition of poor information system with unknown probability distributions and small samples. By extracting difference value information from data series, the membership function and empirical distribution function are defined with a minimum of the maximum...
This paper deals with application of fuzzy least square method to the land-cover classification of remotely sensed data. The proposed method has a considerable advantage of extracting the reflectance of spectral bands for each category from pixel data and thus avoiding difficulties in electing training sets for each land-cover class when the class number gets large, which exist in conventional supervised...
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