Modeling Of Data Association Based On Temporal Finite Automata

With the development of science and technology, especially the rapid development of sensor technology, computer technology and information technology, modern tracking environment is becoming mare and mare complex and modern tracking theory is also being developed and improved.Data association is a key technology of target tracking, especially for multi-target tracking, and it is also a hot subject all along. A lot of DA algorithms have been proposed, but there is no effective way to analyse and evaluate these algorithms. This paper describes how to use mathematical model of DA. Temporal finite automata (TFA) which is an extend version of finite automata is defined, then an algorithm to build TFA for DA is proposed. TFA will provide a formal language to assess the performance of the DA algorithms and the accuracy of computation. A practical case has been used to check the proposed model, and results show that TFA can be used to model DA and the model-based method for performance evaluation is valid.This paper studies on data association, the major contributions and innovations are as following:1. A method of temporal finite automata modeling for data association problem is proposed in this article.2. In order to support temporal observation for data association, timed sequence is introduced to finite automata, and temporal finite automata is defined to model data association problem.3. A lot of data association algorithm is proposed, but there is no data association model for their performance evaluation. This model is applied to validate the veracity of multi-target track.