Probability hypothesis density filter for radar systematic bias estimation aided by ADS-B
| Autor: | Zhe Zhang, Renbiao Wu, Tao Zhang, Lai Ran |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Estimation
020301 aerospace & aeronautics Computer science Gaussian 020206 networking & telecommunications 02 engineering and technology computer.software_genre law.invention symbols.namesake Probability hypothesis density filter 0203 mechanical engineering Control and Systems Engineering law Filter (video) Signal Processing 0202 electrical engineering electronic engineering information engineering symbols Computer Vision and Pattern Recognition Data mining Electrical and Electronic Engineering Radar computer Software |
| Zdroj: | Signal Processing. 120:280-287 |
| ISSN: | 0165-1684 |
| DOI: | 10.1016/j.sigpro.2015.09.012 |
| Popis: | 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. ADS-B surveillance data is used as the high-accuracy reference source.Estimate the systematic bias without priori information of association.Dynamics model and measurement model of systematic bias of radar are developed.A PHD-filter-based bias estimation algorithm is proposed. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect