Probability hypothesis density filter for radar systematic bias estimation aided by ADS-B

摘要

This paper provides a solution for systematic bias estimation of radar without priori information of data association based on the probability hypothesis density (PHD) filter aided by automatic dependent surveillance broadcasting (ADS-B). Novel dynamics model and measurement model of systematic bias are developed by using ADS-B surveillance data as the high-accuracy reference source. The Gaussian mixture probability hypothesis density (GM-PHD) filter is applied for recursive estimation of systematic bias by introducing the novel dynamics model and measurement model of systematic bias into the filter. Numerical results are provided to verify the effectiveness and improved performance of the proposed method for systematic bias estimation.