An Adaptive Approach based on Multi-State Constraint Kalman Filter for UAVs

2021 ◽  
Author(s):  
Hoang Viet Do ◽  
Yong Hun Kim ◽  
Yeong Seo Kwon ◽  
San Hee Kang ◽  
Hak Ju Kim ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Constraint ◽  
Adaptive Approach

Accommodating the multi-state constraint Kalman filter for visual-inertial navigation in a moving and stationary flight

2022 ◽  
pp. 095441002110297
Author(s):  
Arshiya Mahmoudi ◽  
Mahdi Mortazavi ◽  
Mehdi Sabzehparvar
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Inertial Navigation ◽  
State Constraint ◽  
The State ◽  
Light Weight ◽  
Minor Error ◽  
State Augmentation ◽  
Low Speeds

For more than a decade, the multi-state constraint Kalman filter is used for visual-inertial navigation. Its advantages are the light-weight calculations, consistency, and similarity to the current mature GPS/INS Kalman filters. For using it in an airborne platform, an important deficiency exists. It diverges while the object stops moving. In this work, this deficiency is accounted for, by changing the state augmentation and measurement update policy from a time-based to horizontal travel-based scheme, and by reusing the oldest tracked point over and over. Besides the computational savings, it works infinitely with no extra errors in full-stops and with minor error build up in very low speeds.

scholarly journals MCC-CKF: A Distance Constrained Kalman Filter Method for Indoor TOA Localization Applications

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 478 ◽  
Author(s):  
Cheng Xu ◽  
Mengmeng Ji ◽  
Yue Qi ◽  
Xinghang Zhou
Keyword(s):  
Kalman Filter ◽  
Negative Impact ◽  
Extreme Environment ◽  
State Constraint ◽  
Line Of Sight ◽  
Filter Method ◽  
Inequality State ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian ◽  
Non Line Of Sight

Non-Gaussian noise may have a negative impact on the performance of the Kalman filter (KF), due to its adoption of only second-order statistical information. Thus, KF is not first priority in applications with non-Gaussian noises. The indoor positioning based on arrival of time (TOA) has large errors caused by multipath and non-line of sight (NLOS). This paper introduces the inequality state constraint to enhance the ranging performance. Based on these considerations, we propose a constrained Kalman filter based on the maximum correntropy criterion (MCC-CKF) to enhance the TOA performance in the extreme environment of multipath and non-line of sight. Pratical experimental results indicate that MCC-CKF outperforms other estimators, such as Kalman filter and Kalman filter based on maximum entropy.

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