Design of robust cubature fission particle filter algorithm in multi-source cooperative navigation

Sun, Wei; Liu, Jingzhou

doi:10.1038/s41598-022-08189-x

Cited by 5 publications

(4 citation statements)

References 31 publications

(19 reference statements)

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“…Since the development of navigation and positioning technology, the best estimate of the moving vehicle’s attitude can be obtained using an attitude measurement system, which includes the Global Navigation Satellite System (GNSS) and Inertial Navigation Systems (INS) [ 1 , 2 ]. Since GNSS cannot be applied to all environments, the need to find an alternative attitude measurement system is stronger.…”

Section: Introductionmentioning

confidence: 99%

MEMS strapdown inertial attitude measurement system using rotational modulation technology

Sun,

Huang,

Sun

et al. 2024

PLoS ONE

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Attitude determination involves the integration of methodologies and systems for estimating the time varying attitude of moving objects. Strapdown Inertial Attitude Measurement System (SIAMS) is among the most widely used navigation systems. The development of cost effective Micro Electro Mechanic System (MEMS) based inertial sensors has made attitude measurement system more affordable. However, MEMS sensors suffer from various errors that have to be calibrated and compensated to get acceptable attitude results. Given the auto-compensation of inertial sensor bias in rotation error modulation, the objective of this paper is to develop a MEMS-based rotary SIAMS, in which the significant sensor bias is automatically compensated by rotating the IMU, to offer comparable performance with respect to a tactical-grade Inertial Measurement Unit (IMU). With the analysis of the relationship between the MEMS error and misalignment, a MEMS calibration model is derived, and a combined calibration method of multi position rotation is applied to estimate the deterministic sensor errors such as bias, scale factor, and misalignment. Simulation and experiment results indicate that the proposed method can further modulate and compensate the MEMS errors, thereby improving the MEMS attitude accuracy.

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Section: Introductionmentioning

confidence: 99%

MEMS strapdown inertial attitude measurement system using rotational modulation technology

Sun,

Huang,

Sun

et al. 2024

PLoS ONE

Self Cite

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“…These faults affect the performance of the filter and reduce the navigation and positioning accuracy and robustness of the system [7,8]. Therefore, how to effectively enhance the fault-tolerant of the system becomes the key to improving the navigation and positioning performance of the GNSS/SINS integrated navigation system [9,10].…”

Section: Introductionmentioning

confidence: 99%

A GNSS/SINS fault detection and robust adaptive algorithm based on sliding average smooth bounded layer width

Zhao,

Wang,

Gao

et al. 2024

Meas. Sci. Technol.

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The Global Navigation Satellite System/Strapdown Inertial Navigation System (GNSS/SINS) integrated navigation system is an important technology for UAV measurement and vehicle movement measurement. But in the operational process of the GNSS/SINS integrated navigation system, the Global Navigation Satellite System (GNSS) signal is vulnerable to external interference, resulting in abnormal system measurement data, and system faults. These faults will reduce the navigation and positioning performance of the system and reduce the measurement accuracy of the system. Aiming at this problem, a GNSS/SINS fault detection and robust adaptive algorithm based on sliding average smooth bounded layer width is proposed. The algorithm evaluates the system measurement data based on the innovation residual and incorporates the sliding average filter to design the fault detection function based on the smooth bounded width layer. Accurate detection of system faults using fault detection function. The fault detection function value is used to construct the robust cofactor matrix to correct the measurement error in real-time, to improve the accuracy and robustness of the state estimation. The experimental results show that: The proposed algorithm in the paper compares with two traditional robust adaptive algorithms based on smooth bounded layer fault detection and residual chi-square fault detection. The fault detection rates for small step faults show an increase of more than 44.26% and 9.54%, respectively. Similarly, for slowly varying faults, the fault detection rates exhibit an increase of more than 29.32% and 13.56%, respectively. Throughout the fault, the filtering accuracy demonstrates an increase of more than 16.52% and 15.47%, respectively. The algorithm effectively improves the measurement accuracy of the GNSS/SINS integrated navigation system.

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“…In order to solve this problem, the Kalman filter, the EKF, the UKF, and the CKF are adopted to construct the importance density function. Thus, the Kalman particle filter, the extended particle filter, and the unscented particle filter were proposed and applied [26,27]. Based on the reduced INS and the GNSS, an autonomous vehicle sideslip angle estimation algorithm was proposed based on the consensus and the vehicle kinematics/dynamics synthesis, and a multi-sensor framework was developed [28,29].…”

Section: Introductionmentioning

confidence: 99%

A Multi-GNSS/IMU Data Fusion Algorithm Based on the Mixed Norms for Land Vehicle Applications

et al. 2023

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As a typical application of geodesy, the GNSS/INS (Global Navigation Satellite System and Inertial Navigation System) integrated navigation technique was developed and has been applied for decades. For the integrated systems with multiple sensors, data fusion is one of the key problems. As a well-known data fusion algorithm, the Kalman filter can provide optimal estimates with known parameters of the models and noises. In the literature, however, the data fusion algorithm of the GNSS/INS integrated navigation and positioning systems is performed under a certain norm, and performance of the conventional filtering algorithms are improved only under this fixed and limited frame. The mixed norm-based data fusion algorithm is rarely discussed. In this paper, a mixed norm-based data fusion algorithm is proposed, and the hypothesis test statistics are constructed and adopted based on the chi-square distribution. Using the land vehicle data collected through the multi-GNSS and the IMU (Inertial Measurement Unit), the proposed algorithm is tested and compared with the conventional filtering algorithms. Results show that the influences of the outlying measurements and the uncertain noises are weakened with the proposed data fusion algorithm, and the precision of the estimates is further improved. Meanwhile, the proposed algorithm provides an open issue for geodetic applications with mixed norms.

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Design of robust cubature fission particle filter algorithm in multi-source cooperative navigation

Cited by 5 publications

References 31 publications

MEMS strapdown inertial attitude measurement system using rotational modulation technology

MEMS strapdown inertial attitude measurement system using rotational modulation technology

A GNSS/SINS fault detection and robust adaptive algorithm based on sliding average smooth bounded layer width

A Multi-GNSS/IMU Data Fusion Algorithm Based on the Mixed Norms for Land Vehicle Applications

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