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| Comparison of different pseudo-linear estimators for vision-based target motion estimation |
| ZianNing1,2,YinZhang2,ShiyuZhao2 |
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| (1 Department of Computer Science and Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China
2 School of Engineering, Westlake University, Hangzhou 310024, Zhejiang, China) |
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| 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 |
| DOI：https://doi.org/10.1007/s11768-023-00161-y |
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| 基金项目: |
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| Comparison of different pseudo-linear estimators for vision-based target motion estimation |
| Zian Ning1,2,Yin Zhang2,Shiyu Zhao2 |
| (1 Department of Computer Science and Technology, Zhejiang University, Hangzhou 310058, Zhejiang, China
2 School of Engineering, Westlake University, Hangzhou 310024, Zhejiang, China) |
| Abstract: |
| 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. |
| Key words: Pseudo-linear measurements · Kalman filter · Least-squares estimator · Vision-based target motion analysis · Fisher information |
