Sensor data fusion using Pitman's closeness technique and complete linkage algorithm

Multi-sensor data fusion fuses the output from two or more devices that contain sensor or sensor groups and retrieve one or more particular properties of the environment. Since the measurements obtained by the sensors are uncertain due to noise, the sensor data is not always reliable. So, directly using this data may cause inaccurate, even wrong actions, for systems. This paper discusses sensor data fusion using Pitman's closeness and complete linkage algorithm. The data fusion considered involves testing sensor data closeness, merging close sensor data and optimizing the close sensor data. Pitman's closeness technique is used to test sensor data closeness, and the complete linkage algorithm is used for merging close sensors, and the maximum likelihood estimation method is applied for optimizing close sensor data. In this paper we show how to apply these well known mathematical techniques for general sensor data fusion.<
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