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In this paper we discuss a kind of multitarget tracking and association method based on the data fusion of heterogeneous multiple feature data gained by a sensor such as space state, signal amplitude, Doppler frequency and so on. In order to introduce quantitatively those heterogeneous multiple feature data which are possibly gailied by a sensor into the discussion of tracking and association problem, we define a correlation measure which we explain as the generalization of conventional association decision. In conventional Nearest Neighbor method, the decision function can take only two values, 1 or 0, to represent the decision of association or not association. In our method, correlation measure can take any real value from 0 to 1 to represent the extent of correlation. Considering the practical circumstances that some feature data might not be easily gained continuously, we introduce an effective factor to deal with these cases. In the paper we also discuss the comparative computer simulation tests and give the results.
In this paper we discuss a kind of multitarget tracking and association method based on the data fusion of heterogeneous multiple feature data gained by a sensor such as space state, signal amplitude, Doppler frequency and so on. data which are possibly gailied by a sensor into the discussion of tracking and association problem, we define a correlation measure which we explain as the generalization of conventional association decision. 0, to represent the decision of association or not association. In our method, correlation measure can take any real value from 0 to 1 to represent the extent of correlation. Considering the practical circumstances that some feature data might not be easily growing continuously, we introduce an effective factor to deal with these cases. In the paper we also discuss the comparative computer simulation tests and give the results.