<![CDATA[ADS-B is a surveillance technology for transportation. For an aircraft, ADS-B broadcasts its information simultaneously such as identity, flight number, position, altitude, velocity, etc. Due to the limited range of receiving the data, ADS-B needs multi-receiver to increase the capability of receiving data. But, the usage of multi-receiver raises new problems in the ADS-B data stream, such as redundant and outliers. These problems can reduce the quality of data received because it does not represent its actual flight conditions, in other words the nature of time-series data are blurred. This paper proposed a new method for preprocessing the data stream of the ADS-B multi-receiver to get good quality of data. The good quality of data means all unique data successfully restored and redundant data is removed, so its time-series characteristic is regained. This method used coordinate information (latitude and longitude) in ADS-B data as a reference for preprocessing. Applying agglomeration clustering techniques and the rule of distance consistency as merging strategy of the cluster, this method is able to generate the data ADS-B that represents the status of the real aircraft, which is time-series data. This simple method can generate good quality of data from the data ADS-B multi-receiver.]]>
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