Adaptive Kalman Filtering

Rutan, Sarah C.

doi:10.1021/ac00022a739

Cited by 49 publications

(12 citation statements)

References 18 publications

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“…Aside from nonlinear system methods, AKF methods have been studied to solve problems where mainly the settled and experiential parameters are given. The representative IAE [17], MMAE [18], and AFKF [19] are proposed based on the thought that the model parameter and noise statistics are modified with the observation and judgment during the filtering process. From a literature search, it was seen that some improvements in AKF [36][37][38] were presented recently.…”

Section: Kalman Filter and Its Improvementmentioning

confidence: 99%

“…On the one hand, the adaptive Kalman filter (AKF) was proposed focusing on the parameter adjustment to approximate the filtering process to the practical system. Then common AKF includes innovation-based adaptive estimation (IAE) [17], multiple model adaptive estimation (MMAE) [18] and adaptive fading Kalman filter (AFKF) [19]. On the other hand, some methods focus on nonlinear systems, such as extended Kalman filter (EKF) [20], unscented Kalman filter (UKF) [21], noise-robust filter [22] and other estimation methods [23,24].…”

Section: Introductionmentioning

confidence: 99%

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A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model

Bai

Wang

Jin

et al. 2020

Sensors

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The control effect of various intelligent terminals is affected by the data sensing precision. The filtering method has been the typical soft computing method used to promote the sensing level. Due to the difficult recognition of the practical system and the empirical parameter estimation in the traditional Kalman filter, a neuron-based Kalman filter was proposed in the paper. Firstly, the framework of the improved Kalman filter was designed, in which the neuro units were introduced. Secondly, the functions of the neuro units were excavated with the nonlinear autoregressive model. The neuro units optimized the filtering process to reduce the effect of the unpractical system model and hypothetical parameters. Thirdly, the adaptive filtering algorithm was proposed based on the new Kalman filter. Finally, the filter was verified with the simulation signals and practical measurements. The results proved that the filter was effective in noise elimination within the soft computing solution.

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Section: Kalman Filter and Its Improvementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model

Bai

Wang

Jin

et al. 2020

Sensors

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“…In practical use, several data-based methods are applied to determine Q and S matrices, see e.g. [2], [15]. In simulation, these matrices are defined as a part related to the model uncertainties X and to noise behavior in the measurement vector Y .…”

Section: Extended Kalman Filter and Residuals Generationmentioning

confidence: 99%

On fault detection and isolation applied on unicycle mobile robot sensors and actuators

Mellah

Graton

Adel

et al. 2018

2018 7th International Conference on Systems and Control (ICSC)

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In this paper, a combination of model-based and hardware redundancy methods is proposed for both sensor and actuator fault detection and isolation (FDI) of unicycle mobile robots. A focus is brought on robot drift-like faults on wheels and sensors. The goal is to detect and isolate the faulty component as early as possible. The proposed method is based on a combination of hardware redundancy and a bank of Extended Kalman Filters (EKF). Each filter is tuned for a specific fault, to generate residuals with different signatures under different component faults. The different signatures allow the fault isolation. Simulation results show that the proposed method allow to detect both wheels and sensors small drift-like faults and isolate them as early as possible.

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“…According to the convolution property that two Gaussians convolve to make another Gaussian [ 26 ], the result of new Gaussian form integrating the UWB and the structured light scanner can be written as:

where

and

…”

Section: Data Fusion Strategymentioning

confidence: 99%

A Heterogeneous Sensing System-Based Method for Unmanned Aerial Vehicle Indoor Positioning

Wang

Liang

et al. 2017

Sensors

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The indoor environment has brought new challenges for micro Unmanned Aerial Vehicles (UAVs) in terms of their being able to execute tasks with high positioning accuracy. Conventional positioning methods based on GPS are unreliable, although certain circumstances of limited space make it possible to apply new technologies. In this paper, we propose a novel indoor self-positioning system of UAV based on a heterogeneous sensing system, which integrates data from a structured light scanner, ultra-wideband (UWB), and an inertial navigation system (INS). We made the structured light scanner, which is composed of a low-cost structured light and camera, ourselves to improve the positioning accuracy at a specified area. We applied adaptive Kalman filtering to fuse the data from the INS and UWB while the vehicle was moving, as well as Gauss filtering to fuse the data from the UWB and the structured light scanner in a hovering state. The results of our simulations and experiments demonstrate that the proposed strategy significantly improves positioning accuracy in motion and also in the hovering state, as compared to using a single sensor.

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Adaptive Kalman Filtering

Cited by 49 publications

References 18 publications

A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model

A Neuron-Based Kalman Filter with Nonlinear Autoregressive Model

On fault detection and isolation applied on unicycle mobile robot sensors and actuators

A Heterogeneous Sensing System-Based Method for Unmanned Aerial Vehicle Indoor Positioning

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