A Sensitive Metric of Data Association Based on Uncertainty

Data association is one of the most important parts in information fusion, whose performance directly affects the final fusion result. By analyzing the shortcoming of these commonly used assessment metrics, this paper proposes an uncertainty degree based sensitive metric of data association. This metric takes state variable and its error covariance as a kind of probability distribution, and further introduces the concept of uncertainty degree to attain an overall assessment on unbiassedness and stability of estimate errors. The simulation results show that by considering the uncertainty of the state estimate for different data association algorithms, the present metric can sensitively reflect their pros and cons.