A new fuzzy strong tracking cubature Kalman filter for INS/GNSS

Chang, Yuanzhi; Wang, Yongqing; Shen, Yuyao; Ji, Chunguo

doi:10.1007/s10291-021-01148-5

Cited by 14 publications

(6 citation statements)

References 29 publications

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“…The image features represent landmarks in the map [73]. As represented in Equations ( 9) and ( 10), the Kalman filter updates the full state vector with the UAV position and the feature locations, assuming the states earlier than n − 1 are not related to the nth state [138]. The linear system can be solved using Kalman filters (KF) and the nonlinear non-Gaussian system can be solved using extended Kalman filters (EKF), where the discontinuities in scanning landmarks are filtered and corrected [139].…”

Section: Search Space Reduction In Linear Systems Using Kalman Filtermentioning

confidence: 99%

“…As a proposed solution in robotics and autonomous vehicles, particle filters complement the SLAM-based navigation systems with absolute position estimation. The particle filtering process updates the states during each window around each particle during a measurement timeframe T F [138]. To address the shortcomings of EKF, such as the linearization error and noise due to Gaussian distribution assumptions, particle filters have been used while a UAV navigation is mapped using the estimates provided by the sensors [149].…”

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confidence: 99%

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Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Gupta¹,

Fernando²

2022

Preprint

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<p>This article presents a survey of simultaneous localization and mapping (SLAM) and data fusion techniques for object detection and environmental scene perception in unmanned aerial vehicles (UAVs). We critically evaluate some current SLAM implementations in robotics and autonomous vehicles and their applicability and scalability to UAVs. SLAM is envisioned as a potential technique for object detection and scene perception to enable UAV navigation through continuous state estimation. In this article, we bridge the gap between SLAM and data fusion in UAVs while also comprehensively surveying related object detection techniques such as visual odometry and aerial photogrammetry. We begin with an introduction to applications where UAV localization is necessary, followed by an analysis of multimodal sensor data fusion to fuse the information gathered from different sensors mounted on UAVs. We then discuss SLAM techniques such as Kalman filters and extended Kalman filters to address scene perception, mapping, and localization in UAVs. The findings are summarized to correlate prevalent and futuristic SLAM and data fusion for UAV navigation, and some avenues for further research are discussed.</p>

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Section: Search Space Reduction In Linear Systems Using Kalman Filtermentioning

confidence: 99%

mentioning

confidence: 99%

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Gupta¹,

Fernando²

2022

Preprint

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“…Chang et al. presented a new fuzzy strong tracking cubature Kalman filter (FSTCKF) algorithm for data fusion (2021). Li et al.…”

Section: Introductionmentioning

confidence: 99%

Low-cost integrated INS/GNSS using adaptive H∞ Cubature Kalman Filter

Taghizadeh

Safabakhsh

2023

J. Navigation

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We proposed an adaptive H-infinity Cubature Kalman Filter (AH∞CKF) to improve the navigation accuracy of a highly manoeuvrable unmanned aerial vehicle (UAV). AH∞CKF fuses the Inertial Navigation System (INS) and Global Navigation Satellite System (GNSS) measurements. Traditional state estimation filters like extended Kalman filters (EKF) and cubature Kalman filters (CKF) assume Gaussian noises. However, their performance degrades for non-Gaussian noises and system uncertainties encountered in real-world applications. Thus, designing filters robust to noise and distribution is crucial. AH∞CKF combines H∞CKF design with an added adaptive factor to adjust the state estimation covariance matrix according to measurements by exploiting the square root method to yield more numerically stable results (SrAH∞CKF). We conducted multiple dynamically rich flight tests to validate our claims using a UAV equipped with a commercially well-known GNSS solution. Results show that the SrAH∞CKF state estimation outperforms EKF and CKF methods on average by 90% in various statistical measures.

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“…To prevent filtering divergence, a large number of scholars have studied this issue [ 8 , 9 , 10 ]. Deep learning prediction networks can effectively improve the prediction accuracy by scaling and translating the input learnable parameters [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter

Sun

Zhang

Liu

et al. 2022

Sensors

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An integrated navigation algorithm based on a multiple fading factors Kalman filter (MFKF) is proposed to solve the problems that the Kalman filtering (KF) algorithm easily brings about diffusion when the model becomes a mismatched or noisy, and the MFKF accuracy is reduced when the fading factor is overused. Based on the innovation covariance theory, the algorithm designs an improved basis for judging filtering anomalies and makes the timing of the introduction of the fading factor more reasonable by switching the filtering state. Different from the traditional basis of filter abnormality judgment, the improved judgment basis adopts a recursive way to continuously update the estimated value of the innovation covariance to improve the estimation accuracy of the innovation covariance, and an empirical reserve factor for the judgment basis is introduced to adapt to practical engineering applications. By establishing an inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation model, the results show that the average positioning accuracy of the proposed algorithm is improved by 26.52% and 7.48%, respectively, compared with the KF and MFKF, and shows better robustness and self-adaptability.

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A new fuzzy strong tracking cubature Kalman filter for INS/GNSS

Cited by 14 publications

References 29 publications

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges

Low-cost integrated INS/GNSS using adaptive H∞ Cubature Kalman Filter

Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter

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