Multiple sensors hard-failure diagnosis based on Kalman filter

For effective navigation and tracking applications involving Unmanned Aerial Vehicles (UAVs), data fusion from multiple sensors is utilized. However, asynchronous nature of the sensors, coupled with loss of data and communication delays, makes this process not very reliable. For a better estimation of the data, some sort of filtering scheme is needed. This paper presents an Extended Kalman Filter (EKF) based quadrotor state estimation by exploiting the dynamic model of the UAV. The data coming from the sensors is noisy and intermittent. The EKF filters and provides estimated data for the missing timestamps. An indoor flight test establishes the accuracy of the EKF, and another outdoor flight test validates the developed scheme for the real world scenario.

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The Two-Wheeled Self-Balancing Mobile Robot Based on MEMS

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.468-471.2678 ◽

2012 ◽

Vol 468-471 ◽

pp. 2678-2681

Author(s):

Hu Sun ◽

Yun Guo Li ◽

Xin Biao Li ◽

Pei Cheng

Keyword(s):

Kalman Filter ◽

Inertial Measurement Unit ◽

Optimal Estimation ◽

Measurement Unit ◽

Robot System ◽

Multiple Sensors ◽

Attitude Angle ◽

Wheeled Robot ◽

Kalman Filter Algorithm ◽

Sensors Fusion

In this paper, the MIMU( MEMS Inertial Measurement Unit) was used to detect the attitude angle of the two-wheeled robot. By Kalman filter, the optimal estimation of attitude angle was gotten, and which was applied to the balance controlling. In this system, FPGA is chosen as processor, and the embedded kernel was built up based on SOPC. Furthermore, the software of multiple sensors fusion has been developed. The experiment indicates that the design of this robot system is reasonable, and the Kalman filter algorithm can improve the precision of controlling effectively.

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Development of Liquid Capacity Measuring Algorithm Based on Data Integration from Multiple Sensors

Mathematical Problems in Engineering ◽

10.1155/2016/4260397 ◽

2016 ◽

Vol 2016 ◽

pp. 1-12

Author(s):

Kiwoong Park ◽

Si-Kyoung Lee ◽

Hyeon Cheol Kim

Keyword(s):

Kalman Filter ◽

Data Integration ◽

Real Time ◽

Rotation Angle ◽

Sensor Data ◽

Multiple Sensors ◽

Complementary Filter ◽

Level Sensor

This research proposes an algorithm using a process of integrating data from multiple sensors to measure the liquid capacity in real time regardless of the position of the liquid tank. The algorithm for measuring the capacity was created with a complementary filter using a Kalman filter in order to revise the level sensor data and IMU sensor data. The measuring precision of the proposed algorithm was assessed through repetitive experiments by varying the liquid capacity and the rotation angle of the liquid tank. The measurements of the capacity within the liquid tank were precise, even when the liquid tank was rotated. Using the proposed algorithm, one can obtain highly precise measurements, and it is affordable since an existing level sensor is used.

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ESPEE: Event-Based Sensor Pose Estimation Using an Extended Kalman Filter

Sensors ◽

10.3390/s21237840 ◽

2021 ◽

Vol 21 (23) ◽

pp. 7840

Author(s):

Fabien Colonnier ◽

Luca Della Vedova ◽

Garrick Orchard

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Pose Estimation ◽

Computational Cost ◽

Great Promise ◽

Low Latency ◽

Worst Case ◽

Mapping Algorithm ◽

Multiple Sensors ◽

Event Based

Event-based vision sensors show great promise for use in embedded applications requiring low-latency passive sensing at a low computational cost. In this paper, we present an event-based algorithm that relies on an Extended Kalman Filter for 6-Degree of Freedom sensor pose estimation. The algorithm updates the sensor pose event-by-event with low latency (worst case of less than 2 μs on an FPGA). Using a single handheld sensor, we test the algorithm on multiple recordings, ranging from a high contrast printed planar scene to a more natural scene consisting of objects viewed from above. The pose is accurately estimated under rapid motions, up to 2.7 m/s. Thereafter, an extension to multiple sensors is described and tested, highlighting the improved performance of such a setup, as well as the integration with an off-the-shelf mapping algorithm to allow point cloud updates with a 3D scene and enhance the potential applications of this visual odometry solution.

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Deep Kalman Filter: Simultaneous Multi-Sensor Integration and Modelling; GNSS/IMU Case Study

10.20944/preprints201803.0121.v1 ◽

2018 ◽

Author(s):

Siavash Hosseinyalamdary

Keyword(s):

Kalman Filter ◽

Satellite System ◽

Measurement Unit ◽

Sources Of Information ◽

Error Sources ◽

Multiple Sensors ◽

Efficient Integration ◽

Global Navigation Satellite ◽

Gnss Observations

The Bayes filters, such as Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of the unknowns. Efficient integration of multiple sensors requires deep knowledge of their error sources and it is not trivial for complicated sensors, such as Inertial Measurement Unit (IMU). Therefore, IMU error modelling and efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we develop deep Kalman filter to model and remove IMU errors and consequently, improve the accuracy of IMU positioning. In other words, we add modelling step to the prediction and update steps of Kalman filter and the IMU error model is learned during integration. Therefore, our deep Kalman filter outperforms Kalman filter and reaches higher accuracy.

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The Study on Disturbing Force of Dynamic Mechanical Systems Using Kalman Filter Techniques

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.705.371 ◽

2013 ◽

Vol 705 ◽

pp. 371-377

Author(s):

Zhang Jing

Keyword(s):

Kalman Filter ◽

Mechanical Systems ◽

Dynamic Structure ◽

Estimation Accuracy ◽

Complex Dynamic ◽

Model Quality ◽

Disturbing Force ◽

Dynamic Mechanical ◽

Multiple Sensors ◽

Mems Sensor

According to the approach presented in this paper, the disturbing force of dynamic mechanical systems can be estimated using Kalman Filter techniques. It has been shown how multiple sensors can be included to improve the estimation accuracy and how the algorithm responds to changing parameters and model inaccuracies. Finally, the approach has been experimentally verified with a MEMS-sensor and a more complex dynamic structure of a milling machine with active magnetic guides. Future enhancements may especially regard the model side, since the estimation accuracy is directly determined by the model quality.

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Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion

Mathematical Problems in Engineering ◽

10.1155/2015/854085 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Huadong Wang ◽

Shi Dong

Keyword(s):

Kalman Filter ◽

Data Fusion ◽

Observational Data ◽

Measurement System ◽

Unscented Kalman Filter ◽

Measurement Data ◽

Fusion Algorithm ◽

Multisensor Data Fusion ◽

Multiple Sensors ◽

Multisensor Data

In order to improve the reliability of measurement data, the multisensor data fusion technology has progressed greatly in improving the accuracy of measurement data. This paper utilizes the real-time, recursive, and optimal estimation characteristics of unscented Kalman filter (UKF), as well as the unique advantages of multiscale wavelet transform decomposition in data analysis to effectively integrate observational data from multiple sensors. A new multiscale UKF-based multisensor data fusion algorithm is proposed by combining the UKF with multiscale signal analysis. Firstly, model-based UKF is introduced into the multiple sensors, and then the model is decomposed at multiple scales onto the coarse scale with wavelets. Next, signals decomposed from fine to coarse scales are adjusted using the denoised observational data from corresponding sensors and reconstructed with wavelets to obtain the fused signals. Finally, the processed data are fused using adaptive weighted fusion algorithm. Comparison of simulation and experimental results shows that the proposed method can effectively improve the antijamming capability of the measurement system and ensure the reliability and accuracy of sensor measurement system compared to the use of data fusion algorithm alone.

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