Comparison of different pseudo-linear estimators for vision-based target motion estimation  

作　　者：Zian Ning Yin Zhang Shiyu Zhao 

机构地区：[1]Department of Computer Science and Technology,Zhejiang University,Hangzhou 310058,Zhejiang,China [2]School of Engineering,Westlake University,Hangzhou 310024,Zhejiang,China

出　　处：《Control Theory and Technology》2023年第3期448-457,共10页控制理论与技术（英文版）

摘　　要：Vision-based target motion estimation based Kalman filtering or least-squares estimators is an important problem in many tasks such as vision-based swarming or vision-based target pursuit.In this paper,we focus on a problem that is very specific yet we believe important.That is,from the vision measurements,we can formulate various measurements.Which and how the measurements should be used?These problems are very fundamental,but we notice that practitioners usually do not pay special attention to them and often make mistakes.Motivated by this,we formulate three pseudo-linear measurements based on the bearing and angle measurements,which are standard vision measurements that can be obtained.Different estimators based on Kalman filtering and least-squares estimation are established and compared based on numerical experiments.It is revealed that correctly analyzing the covariance noises is critical for the Kalman filtering-based estimators.When the variance of the original measurement noise is unknown,the pseudo-linear least-squares estimator that has the smallest magnitude of the transformed noise can be a good choice.

关 键 词：Pseudo-linear measurements Kalman filter Least-squares estimator Vision-based target motion analysis Fisher information 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]