Centralized Kalman Filter for Fusion of Multiple On-Board Auxiliary Sensors with INS for Underwater Navigation

Sahu, Nitesh; Gupta, Rinki; Kumar, Arun; Bahl, Rajendar; Kar, Indra Narayan

doi:10.1109/ut.2019.8734318

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…The data of the INS and BDS were processed by using a centralized Kalman filter [29]. In brief, the observed data from the BDS and INS were processed by the optimal error estimation of Kalman filtering, so as to correct the original system and produce high-precision attitude data.…”

Section: Implementation Of the Ins And Bds Integrated Algorithmmentioning

confidence: 99%

An Acquisition Method of Agricultural Equipment Roll Angle Based on Multi-Source Information Fusion

et al. 2020

Sensors

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Accurately obtaining roll angles is one of the key technologies to improve the positioning accuracy and operation quality of agricultural equipment. Given the demand for the acquisition of agricultural equipment roll angles, a roll angle monitoring model based on Kalman filtering and multi-source information fusion was established by using the MTi-300 AHRS inertial sensor (INS) and XW-GI 5630 BeiDou Navigation Satellite System (BDS), which were installed on agricultural equipment. Data of the INS and BDS were fused by MATLAB; then, Kalman filter was used to optimize the data, and the state equation and measurement equation of the integrated system were established. Then, an integrated monitoring terminal man-machine interactive interface was designed on MATLAB GUI, and a roll angle monitoring system based on the INS and BDS was designed and applied into field experiments. The mean absolute error of the integrated monitoring system based on multi-source information fusion during field experiments was 0.72 • , which was smaller compared with the mean absolute errors of roll angle monitored by the INS and BDS independently (0.78 • and 0.75 • , respectively). Thus, the roll angle integrated model improves monitoring precision and underlies future research on navigation and independent operation of agricultural equipment.

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Section: Implementation Of the Ins And Bds Integrated Algorithmmentioning

confidence: 99%

An Acquisition Method of Agricultural Equipment Roll Angle Based on Multi-Source Information Fusion

et al. 2020

Sensors

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“…more than 1000 samples) or multiple drift faults at separate time intervals with excess noise. In addition, multi sensor fusion approaches based on estimation theory and especially centralized [6] and decentralized fusion [7] approaches using the extended Kalman filter have been used often to get an accurate pose. Though these methods are not sufficient for fault detection and isolation, thus FROIF is introduced as a decentralized fusion framework, incorporating fault detection and isolation framework as it has been presented in [8].…”

Section: Introductionmentioning

confidence: 99%

A Resilient to Faults Auto-Encoder Enabled Kalman based Multi-Sensorial Fusion

Mukherjee

Banerjee

Nikolakopoulos

2022

2022 30th Mediterranean Conference on Control and Automation (MED)

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This article presents a novel Auto-encoder-enabled fault resilient multi-sensor fusion architecture while incorporating an extended Kalman filter framework. The auto-encoder facilitate reconstruction of the faulty measurements from multiple onboard sensors, while the centralized extended Kalman filter enables an accurate fusion architecture. Moreover, the process is capable of successfully eliminating the additive noise appearing from the raw sensor data. The proposed method provides a robust reconstruction mechanism in the presence of time-dependent anomalies and faulty sensor measurement. The efficacy of the proposed scheme is extensively evaluated in the context of pose estimation for a micro aerial vehicle equipped with multiple onboard sensors. In addition, the evaluation process incorporates various realistic failure scenarios with artificially introduced inaccurate measurements. The superiority of the proposed Auto-encoder enabled centralized Kalman filter (AEKF) fusion is demonstrated through an extensive comparison with a recently developed Fault Resilient Optimal Information Filter (FROIF) method.

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“…At present, research on UAV location algorithm based on multi-sensor data fusion is mainly based on Kalman filter [5] data fusion. Among them, multi-sensor data fusion can be divided into centralized fusion Kalman filter [7], [9] and distributed fusion Kalman filter [8], [10] according to different fusion modes. Although the fusion accuracy of centralized fusion filter is relatively high, it has the disadvantages of large computation, complicated computation and high requirement on the central processor.…”

Section: Introductionmentioning

confidence: 99%

UAV Positioning Based on Multi-Sensor Fusion

et al. 2020

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Real-time and stable positioning data is essential for the UAV to perform various tasks. The traditional multi-sensor data fusion algorithm needs to know the measurement noise of sensor data, and even if there are corresponding adaptive methods to estimate the noise, most methods cannot deal with time-varying noise. In addition, traditional fusion algorithms usually are complicated, causing a large amount of calculation. In this paper, a multi-sliding window classification adaptive unscented Kalman filter (MWCAUKF) method with timestamp sort updating was proposed, which can improve the accuracy and stability of positioning. This method consists of three phases. First, according to the timestamp of sensor data, the multi-sensor data are added with fusion filtering in order. Then it estimates the measurement noise of multiple sensors through multiple sliding Windows. Finally, the sensor data classification method is adopted to deal with the filter instability caused by time-varying noise. Both theoretical analysis and experimental results show that this method has a low computational cost, high accuracy, and good stability. INDEX TERMS Multi-sensor fusion, unmanned aerial vehicle, positioning, adaptive Kalman filtering.

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Centralized Kalman Filter for Fusion of Multiple On-Board Auxiliary Sensors with INS for Underwater Navigation

Cited by 4 publications

References 9 publications

An Acquisition Method of Agricultural Equipment Roll Angle Based on Multi-Source Information Fusion

An Acquisition Method of Agricultural Equipment Roll Angle Based on Multi-Source Information Fusion

A Resilient to Faults Auto-Encoder Enabled Kalman based Multi-Sensorial Fusion

UAV Positioning Based on Multi-Sensor Fusion

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