A asynchronous radar track association algorithm based on discrete data volatility is proposed to address the issues of low efficiency and decreased correlation performance near sensors of traditional algorithms.A segmentation method is proposed to convert unequal length sequences into equal length sequences based on the track length,in order to adapt the complex changing of actual tracks.The weighted variance of a mixed discrete dataset is defined for correlation judgment,describing the volatility of the same origin track sequence dataset.The experiment results show that compared with traditional asynchronous track correlation algo-rithms,the proposed algorithm has a higher correct correlation rate,lower time consumption,and is not affected by radar error dis-tribution and target motion position,with stronger stability.
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