Adaptive Fifth-Degree Cubature Information Filter for Multi-Sensor Bearings-Only Tracking

作者：Jiang, Haonan; Cai, Yuanli^*

来源：Sensors (Switzerland), 2018, 18(10): 3241.

DOI：10.3390/s18103241

• cubature information filter
• multi-sensor bearings-only tracking
• covariance matching
• sensor selection

Standard Bayesian filtering algorithms only work well when the statistical properties of system noises are exactly known. However, this assumption is not always plausible in real target tracking applications. In this paper, we present a new estimation approach named adaptive fifth-degree cubature information filter (AFCIF) for multi-sensor bearings-only tracking (BOT) under the condition that the process noise follows zero-mean Gaussian distribution with unknown covariance. The novel algorithm is based on the fifth-degree cubature Kalman filter and it is constructed within the information filtering framework. With a sensor selection strategy developed using observability theory and a recursive process noise covariance estimation procedure derived using the covariance matching principle, the proposed filtering algorithm demonstrates better estimation accuracy and filtering stability. Simulation results validate the superiority of the AFCIF.

• 出版日期2018-10
• 单位西安交通大学