Qualitative performance comparison of reactivity estimation between the extended Kalman filter technique and the inverse point kinetic method

2014 ◽  
Vol 66 ◽  
pp. 161-166 ◽  
Author(s):  
Y. Shimazu ◽  
W.F.G. van Rooijen
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Kinetic Method ◽  
Performance Comparison ◽  
Filter Technique ◽  
Qualitative Performance ◽  
Inverse Point

Performance Comparison of Inertial Measurement Units Fused With Odometry in Extended Kalman Filter for Dead-Reckoning Navigation

2019 ◽  
Author(s):  
Kevin Carey ◽  
Benjamin Abruzzo ◽  
David P. Harvie ◽  
Christopher Korpela
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Autonomous Vehicle ◽  
Dead Reckoning ◽  
Ground Truth ◽  
Performance Comparison ◽  
Inertial Measurement Units ◽  
Ground Vehicle ◽  
Inertial Measurement ◽  
Measurement Units

Abstract This paper aims to aid robot and autonomous vehicle designers by providing a comparison between four different inertial measurement units (IMUs) which could be used to aid in vehicle navigation in a GPS-denied or inertial-only scenario. A differential-drive ground vehicle was designed to carry the multiple different IMUs, mounted coaxially, to enable direct comparison of performance in a planar environment. The experiments focused on the growth of pose error of the ground vehicle originating from the odometry senors and the IMUs. An extended Kalman Filter was developed to fuse the odometry and inertial measurements for this comparison. The four specific IMUs evaluated were: CNS 5000, Xsens 300, Microstrain GX5-35, and Phidgets 1044 and the ground truth for experiments was provided by an Optitrack motion capture system (MCS). Finally, metrics for choosing IMUs, merging cost and performance considerations, are proposed and discussed. While the CNS 5000 has the best objective error specifications, based on these metrics the Xsens 300 exhibits the best absolute performance while the Phidgets 1044 provides the best performance-per-dollar.

Extended Kalman Filter for Reconstruction of a Nonlinear Thermofluid System

1986 ◽  
Vol 108 (2) ◽  
pp. 156-158 ◽  
Author(s):  
R. Shoureshi ◽  
K. McLaughlin
Keyword(s):  
Experimental Data ◽  
Kalman Filter ◽  
Heat Pump ◽  
Extended Kalman Filter ◽  
Eigenvalue Distribution ◽  
Integration Time ◽  
Time Step ◽  
Filter Technique ◽  
Gain Matrix ◽  
Good Agreement

Extended Kalman filter technique is used to develop an observer for a nonlinear thermofluid system, namely a heat pump. The observer’s optimal gain matrix is designed based on the eigenvalue distribution, integration time step, and stability of the system along a desired trajectory. The observer response is compared with experimental data and very good agreement is obtained.

scholarly journals A Fuzzy-Innovation-Based Adaptive Kalman Filter for Enhanced Vehicle Positioning in Dense Urban Environments

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1142 ◽  
Author(s):  
Rinara Woo ◽  
Eun-Ju Yang ◽  
Dae-Wha Seo
Keyword(s):  
Kalman Filter ◽  
Urban Environment ◽  
Extended Kalman Filter ◽  
Estimation Error ◽  
Micro Electro Mechanical System ◽  
Performance Comparison ◽  
Positioning System ◽  
Positioning Systems ◽  
Adaptive Extended Kalman Filter ◽  
Dense Urban Environment

In this paper, a fuzzy-innovation based adaptive extended Kalman filter (FI-AKF)is proposed to improve the performance of the GNSS/INS fusion system, which is degradeddue to satellite signal cutoff and attenuation and inaccurate modeling in dense urbanenvironments. The information used for sensor fusion is obtained from real-time kinematic (RTK),micro-electro-mechanical system based inertial measumrement unit (MEMS-IMU), and on-boarddiagnostics (OBD). The fuzzy logic system is proposed to adaptively update the measurementcovariance matrix of the RTK according to the position dilution of precision (PDOP), the numberof receivable satellites, and the innovation of the extended Kalman filter (EKF). In addition, thedriving state of the vehicle is defined as stop, straight run, left/right turn, and the like. To reduce theheading estimation error of the Kalman filter, the estimated heading is corrected according to thedriving state. Also, the measurement covariance matrices of IMU and OBD are applied adaptivelyconsidering the characteristics of each sensor according to the driving state. In order to analyze theperformance of the proposed FI-AKF positioning system in a dense urban environment, a computersimulation is performed. The proposed FI-AKF is compared to the performance of the existingextended Kalman filter and the innovation-based adaptive extended Kalman filter. In addition, weconduct a performance comparison experiment with a commercial positioning system in the field test.Through each experiment, it is confirmed that the proposed FI-AKF system has higher positioningperformance than the comparison positioning systems in a dense urban environment.

scholarly journals Incorporating neuro-fuzzy with extended Kalman filter for simultaneous localization and mapping

2019 ◽  
Vol 16 (5) ◽  
pp. 172988141987464 ◽  
Author(s):  
Cong Hung Do ◽  
Huei-Yung Lin
Keyword(s):  
Kalman Filter ◽  
Mobile Robot ◽  
Extended Kalman Filter ◽  
Optimization Technique ◽  
Computational Cost ◽  
Simultaneous Localization And Mapping ◽  
Mobile Robot Navigation ◽  
Filter Technique ◽  
Localization And Mapping ◽  
Neuro Fuzzy

Extended Kalman filter is well-known as a popular solution to the simultaneous localization and mapping problem for mobile robot platforms or vehicles. In this article, the development of a neuro-fuzzy-based adaptive extended Kalman filter technique is presented. The objective is to estimate the proper values of the R matrix at each step. We design an adaptive neuro-fuzzy extended Kalman filter to minimize the difference between the actual and theoretical covariance matrices of the innovation consequence. The parameters of the adaptive neuro-fuzzy extended Kalman filter is then trained offline using a particle swarm optimization technique. With this approach, the advantages of high-dimensional search space can be exploited and more effective training can be achieved. In the experiments, the mobile robot navigation with a number of landmarks under two benchmark situations is evaluated. The results have demonstrated that the proposed adaptive neuro-fuzzy extended Kalman filter technique provides the improvement in both performance efficiency and computational cost.

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