Robust partly strong tracking consider SDRE filter for direct INS/GNSS integration with biases

Lou, Taishan; Wang, Xiaoqian; Zhao, Hongmei; Chen, Zhiwu

doi:10.1088/1361-6501/ab8d59

Cited by 5 publications

(3 citation statements)

References 21 publications

(25 reference statements)

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“…Since the dynamics of these two variables are not linear, nonlinear filters are preferable. SDRE filter high degree of freedom provides singularity avoidance making it a proper choice employed in various applications as the optimal solution for nonlinear estimation and control problems [36,37,38]. This filter incorporates the nonlinearity into the estimation equations based on a parameterization which compared to the EKF is counted as a significant advantage since the EKF linearizes the nonlinearity [39].…”

Section: Related Work and Motivationmentioning

confidence: 99%

A Framework for 3D Tracking of Frontal Dynamic Objects in Autonomous Cars

Lotfi¹,

Taghirad²

2021

Preprint

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Both recognition and 3D tracking of frontal dynamic objects are crucial problems in an autonomous vehicle, while depth estimation as an essential issue becomes a challenging problem using a monocular camera. Since both camera and objects are moving, the issue can be formed as a structure from motion (SFM) problem. In this paper, to elicit features from an image, the YOLOv3 approach is utilized beside an OpenCV tracker. Subsequently, to obtain the lateral and longitudinal distances, a nonlinear SFM model is considered alongside a state-dependent Riccati equation (SDRE) filter and a newly developed observation model. Additionally, a switching method in the form of switching estimation error covariance is proposed to enhance the robust performance of the SDRE filter. The stability analysis of the presented filter is conducted on a class of discrete nonlinear systems. Furthermore, the ultimate bound of estimation error caused by model uncertainties is analytically obtained to investigate the switching significance. Simulations are reported to validate the performance of the switched SDRE filter. Finally, real-time experiments are performed through a multi-thread framework implemented on a Jetson TX2 board, while radar data is used for the evaluation.

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Section: Related Work and Motivationmentioning

confidence: 99%

A Framework for 3D Tracking of Frontal Dynamic Objects in Autonomous Cars

Lotfi¹,

Taghirad²

2021

Preprint

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“…However, inaccurate statistics of the measurement information, such as means and covariances, and improper information fusion methods can lead to unpredictable or undesired navigation errors. Therefore, it is necessary to consider the most probable biases, such as drifts of the inertial measurement unit (IMU), receiver clock biases, multi-path biases of the GNSS, temperature biases of barometers, and other biases of the navigation system [4][5][6], when designing information fusion methods for a multisensor integrated navigation system. Among the information fusion algorithms, Kalman filter and its variants are the most successful and widely used in the integrated navigation field [7].…”

Section: Introductionmentioning

confidence: 99%

A consider unscented particle filter with genetic algorithm for UAV multi-source integrated navigation

Lou

Chen

Jiao

et al. 2023

Meas. Sci. Technol.

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The unscented particle filter (UPF) combines the advantages of both the unscented Kalman filter (UKF) and particle filter (PF) algorithms. However, during the fusion of multi-source information for UAV integrated navigation, the UPF may encounter problems related to measurement biases and particle depletion. These issues can lead to algorithm results that are unsatisfactory or divergent. To address these challenges, a novel approach called the consider unscented particle filter with genetic algorithm (GA-CUPF) is proposed. The GA-CUPF algorithm incorporates covariance and co-covariance of biases into the state estimation covariance through the "consider" method employed by the CUPF, and it should be noted that the biases are not estimated in this process. Moreover, the GA-CUPF algorithm enhances the importance probability density of particles to avoid the degeneracy of PF algorithm and improves particle diversity through the application of selection, crossover, and mutation methods in GA. Simulation results demonstrate that the proposed GA-CUPF algorithm can effectively mitigate the adverse effects of measurement biases and particle depletion that the UPF algorithm faces in the UAV navigation system. Additionally, it significantly improves the accuracy of UAV integrated navigation.

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“…In this case, the performance of the Kalman filter based on MMSE will be seriously degraded. Robust Kalman filters have been proposed to solve this problem [21][22][23], e.g., the Huberbased Kalman filter (HKF) [24] and the maximum correntropy criterion-based Kalman filters [25]. The interference of non-Gaussian noise is controlled by the HKF with the help of l 1 and l 2 norms.…”

Section: Introductionmentioning

confidence: 99%

An improved multi-state constraint kalman filter based on maximum correntropy criterion

Liu,

Guo

2023

Phys. Scr.

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In recent years, the multi-state constraint Kalman filter has been widely used in the visual-inertial navigation of unmanned systems. However, in most previous studies, the measurement noise of the navigation system was assumed to be Gaussian noise, but this is not the case in practice. In this paper, the maximum correntropy criterion is introduced into the multi-state constraint Kalman filter to improve the robustness of the visual-inertial system. First, the new maximum correntropy criterion-based Kalman filter is introduced, it uses the maximum correntropy criterion to replace the minimum mean square error criterion to suppress the interference of measurement outliers on the filtering results, and it has no numerical problem in the presence of large measurements outliers. Then, an improved multi-state constraint Kalman filter is designed by applying the new maximum correntropy criterion-based Kalman filter to the multi-state constraint Kalman filter, which improved the robustness of the multi-state constraint Kalman filter. The results of numerical simulation and dataset experiments show that the proposed filter improves the accuracy and robustness of the visual-inertial system.

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Robust partly strong tracking consider SDRE filter for direct INS/GNSS integration with biases

Cited by 5 publications

References 21 publications

A Framework for 3D Tracking of Frontal Dynamic Objects in Autonomous Cars

A Framework for 3D Tracking of Frontal Dynamic Objects in Autonomous Cars

A consider unscented particle filter with genetic algorithm for UAV multi-source integrated navigation

An improved multi-state constraint kalman filter based on maximum correntropy criterion

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