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Absfracf-Data fusion is the process to synthetically calculate information coming from several sensors, which helps reduce possible mistakes o r uncertainty of apperceiving targets in the information process. A general performance evaluation platform is introduced for data fusion algorithms which can be used for the distributed multi-target tracing process. The improved insert-value method was implemented to simulate the original three-dimension data and a disturb model which considers the sensor performance and uncertain factors was applied. Furthermore, we put forward a criterion to assess the synthetic performance. Efficiency and validity were substantiated by the simulated examination. For testing one or several data fusion algorithms which are available o r being investigated, the system can supply a practical appraising method. I ,

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This paper proposes a novel, U-tree based indexing technique that addresses the problem of managing uncertain data in a constantly evolving environment. The technique called TPU-tree is capable of indexing the moving objects with uncertainty in multi-dimensional spaces. Along with the data models capturing the temporal and spatial uncertainty, a modified p-bound based range query (MP_BBRQ) algorithms for probabilistic queries is also developed. Experimental evaluations demonstrate that the TPU-tree supports queries on uncertain moving objects quite efficiently. It yields rather good update performance even under frequent update environments, and has a practical value.