Least-squares approach to asynchronous data fusion

The fusion of asynchronous data is usually achieved by sequentially processing the data as it arrives at the central processor. However, if the data rate is too high and the data from different sensors are taken at times that are arbitrarily close together, some other technique is necessary. One way of overcoming this problem is to take the data within a specified time interval and compress it. While there are many ways of compressing data, the method chosen must retain all of the data's salient features. This paper discusses methods that utilize least- squares techniques for compressing data from one or more sensors and a central filter for processing the compressed data. Simulated tracking results from a central filter using the compressed data are compared to the results from an optimal central filter that processes the data sequentially. The error covariance associated with the optical filtering approach is compared with that of the central filter processing the compressed data. Also, some generalized theorems concerning the fusion of synchronized state and measurement vectors of different dimensions are given.