• 智能时空信息服务技术 •

### 贝叶斯序贯重要性积分滤波器

1. 1.中山大学航空航天学院，广东 广州 510725
2.中国科学院空间精密测量技术重点实验室，陕西 西安 710119
3.北京东方计量测试研究所，北京 100029
• 收稿日期:2021-06-06 修回日期:2022-01-06 出版日期:2022-04-25 发布日期:2022-04-25
• 作者简介:张宏伟 女，1982年出生，河南南阳人.博士，中山大学航天航空学院副研究员.主要研究方向为智能信息处理、信息融合、目标跟踪. E-mail: zhanghw69@sysu.edu.cn
张小虎 男，1973年出生，陕西凤翔人.博士，中山大学航天航空学院教授，图像感知与信息处理学科学术带头人.主要研究方向为精密测量、图像信息处理.
• 基金资助:
广东省基础与应用基础研究基金(2019A1515111099);中山大学青年培育项目(20lgpy72);中国科学院空间精密测量重点实验室开放基金(SPMT2021002)

### Bayesian Sequential Importance Quadrature Filter

ZHANG Hong-wei1,2, ZHANG Xiao-hu1, CAO Yong3

1. 1.School of Aeronautics and Astronautics，Sun Yat-sen University，Guangzhou，Guangdong 510725，China
2.CAS Key Laboratory of Space Precision Measurement Technology，Xi’an，Shaanxi 710119，China
3.BeiJing Orient Institute of Measurement and Test，Beijing 100029，China
• Received:2021-06-06 Revised:2022-01-06 Online:2022-04-25 Published:2022-04-25

Abstract:

To solve the mismatch problems between the measurement likelihood function, importance density function and the target true distribution for the nonlinear filtering in the presence of the model attribute ambiguity and prediction bias, we derive and present a Bayesian sequential importance quadrature filter(SIQF) algorithm. To reduce the deviation between the likelihood function and the target true distribution in the Bayesian reference, the bounded measurement likelihood of the latest measurement is defined via the soft spatiotemporal constraint, the modified prior of the feasible area is approximated by truncating the probability density function of the measurement noise. To modulate the matching degree between the importance function and the target distribution, the state under the modified and original priors is evaluated via Gauss-Hermite Kalman filter in parallel, the maximum correntropy criterion is introduced to construct the mixture importance function, both the diversity of sequential importance sample and the tolerance of prediction covariance can be thereby improved. The simulation results show that, compared with the unscented particle filter for the estimation of one-dimensional univariate nonstationary growth model, the average estimate error of the SIQF algorithm has decreased 63% without sacrificing computational complexity. Compared with the multi-model Rao-blackwell particle filter for the maneuvering target tracking in the airspace, the root mean square error of the SIQF algorithm has decreased 33%, and the computational load is reduced by an order of magnitude.