Delayed and asequent data in decentralized sensing networks

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Abstract
This paper presents an exact solution to the delayed data problem for the information form of the Kalman filter, together with its application to decentralised sensing networks. To date, the most common method of handling delayed data in sensing networks has been to use a conservative time alignment of the observation data with the filter time. However, by accounting for the correlation between the late data and the filter over the delayed period, an exact solution is possible. The inclusion of this information correlation term adds little extra complexity, and may be applied in an information filter update stage which is associative. The delayed data algorithm can also be used to handle data that is asequent or out of order. The asequent data problem is presented in a simple recursive information filter form. The information filter equations presented in this paper are applied in a decentralised picture compilation problem. This involves multiple aircraft tracking multiple ground targets and the construction of a single common tactical picture.

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