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1. An equivalent circuit model for Vanadium Redox Batteries via hybrid extended Kalman filter and Particle filter methods

   Khaki, Bahman; Das, Pritam (January 2021, Journal of energy storage)

   Uwe Sauer, Dirk (Ed.)

   A B S T R A C T This paper proposes a model for parameter estimation of Vanadium Redox Flow Battery based on both the electrochemical model and the Equivalent Circuit Model. The equivalent circuit elements are found by a newly proposed optimization to minimized the error between the Thevenin and KVL-based impedance of the equivalent circuit. In contrast to most previously proposed circuit models, which are only introduced for constant current charging, the proposed method is applicable for all charging procedures, i.e., constant current, constant voltage, and constant current-constant voltage charging procedures. The proposed model is verified on a nine-cell VRFB stack by a sample constant current-constant voltage charging. As observed, in constant current charging mode, the terminal voltage model matches the measured data closely with low deviation; however, the terminal voltage model shows discrepancies with the measured data of VRFB in constant voltage charging. To improve the proposed circuit model’s discrepancies in constant voltage mode, two Kalman filters, i.e., hybrid extended Kalman filter and particle filter estimation algorithms, are used in this study. The results show the accuracy of the proposed equivalent with an average deviation of 0.88% for terminal voltage model estimation by the extended KF-based method and the average deviation of 0.79% for the particle filter-based estimation method, while the initial equivalent circuit has an error of 7.21%. Further, the proposed procedure extended to estimate the state of charge of the battery. The results show an average deviation of 4.2% in estimating the battery state of charge using the PF method and 4.4% using the hybrid extended KF method, while the electrochemical SoC estimation method is taken as the reference. These two Kalman Filter based methods are more accurate compared to the average deviation of state of charge using the Coulomb counting method, which is 7.4%. 

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2. Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments

   https://doi.org/10.3390/s21041250  

   Medina, Daniel; Li, Haoqing; Vilà-Valls, Jordi; Closas, Pau (February 2021, Sensors)

   null (Ed.)

   Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. Exploiting carrier phase observations allows for precise positioning solutions at the complexity cost of resolving integer phase ambiguities, a procedure that is particularly affected by non-nominal conditions. This limits the applicability of conventional filtering techniques in challenging scenarios, and new robust solutions must be accounted for. This contribution deals with real-time kinematic (RTK) positioning and the design of robust filtering solutions for the associated mixed integer- and real-valued estimation problem. Families of Kalman filter (KF) approaches based on robust statistics and variational inference are explored, such as the generalized M-based KF or the variational-based KF, aiming to mitigate the impact of outliers or non-nominal measurement behaviors. The performance assessment under harsh propagation conditions is realized using a simulated scenario and real data from a measurement campaign. The proposed robust filtering solutions are shown to offer excellent resilience against outlying observations, with the variational-based KF showcasing the overall best performance in terms of Gaussian efficiency and robustness. 

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3. Relative target estimation using a cascade of extended Kalman filters

   Nielsen, Jerel; Beard, Randal W. (September 2017, Proceedings of the International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+))

   This paper presents a method of tracking multiple ground targets from an unmanned aerial vehicle (UAV) in a 3D reference frame. The tracking method uses a monocular camera and makes no assumptions on the shape of the terrain or the target motion. The UAV runs two cascaded estimators. The first is an Extended Kalman Filter (EKF), which is responsible for tracking the UAV’s state, such as position and velocity relative to a fixed frame. The second estimator is an EKF that is responsible for estimating a fixed number of landmarks within the camera’s field of view. Landmarks are parameterized by a quaternion associated with bearing from the camera’s optical axis and an inverse distance parameter. The bearing quaternion allows for a minimal representation of each landmark’s direction and distance, a filter with no singularities, and a fast update rate due to few trigonometric functions. Three methods for estimating the ground target positions are demonstrated: the first uses the landmark estimator directly on the targets, the second computes the target depth with a weighted average of converged landmark depths, and the third extends the target’s measured bearing vector to intersect a ground plane approximated from the landmark estimates. Simulation results show that the third target estimation method yields the most accurate results. 

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4. A Noise Suppression of LSTM algorithm combined with Kalman filter for Agriculture Automation

   https://doi.org/10.1145/3589883.3589918  

   Barry, Abdoulaye; Kim, Junwhan; Yu, Byunggu; O'Hara, Sabine (March 2023, ACM)

   An immense volume of data is produced by sensor devices in the fields of aquaponics, hydroponics, and soil-based food production, where these devices track various environmental factors. Data stream mining is the method of retrieving data from fast-sampled data sources that are constantly streaming. The accuracy of data obtained through data stream mining is largely determined by the algorithm utilized to filter out noise. For threshold-based automation, an actuator can be activated when the value of sensor data is above a permissible threshold. Noise from sensors may activate the actuator. Several statistical and machine learning-based noise-suppression algorithms have been proposed in the literature. They have been evaluated based on the mean squared error metric (MSE). The Long Short-Term Memory – LSTM filter (MSE: 0.000999943) performs better noise suppression than other traditional filters – Kalman (MSE: 0.0015982). We propose a new noise suppression filter – LSTM combined with Kalman (LSTM-KF). In LSTM-KF, the Kalman filter acts as an encoder and the LSTM becomes the decoder, resulting in a significantly lower MSE – 0.000080789592. The LSTM-KF is installed in our threshold-based aquaponics automation to maximize sustainable food production at minimum cost. 

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5. Velocimeter LIDAR-Based Multiplicative Extended Kalman Filter for Terrain Relative Navigation Applications

   https://doi.org/10.2514/6.2022-1711  

   Adams, Davis W.; Peck, Caleb; Majji, Manoranjan (January 2022, AIAA SCITECH 2022 Forum)

   This paper presents a Multiplicative Extended Kalman Filter (MEKF) framework using a state-of-the-art velocimeter Light Detection and Ranging (LIDAR) sensor for Terrain Relative Navigation (TRN) applications. The newly developed velocimeter LIDAR is capable of providing simultaneous position, Doppler velocity, and reflectivity measurements for every point in the point cloud. This information, along with pseudo-measurements from point cloud registration techniques, a novel bulk velocity batch state estimation process and inertial measurement data, is fused within a traditional Kalman filter architecture. Results from extensive emulation robotics experiments performed at Texas A&M’s Land, Air, and Space Robotics (LASR) laboratory and Monte Carlo simulations are presented to evaluate the efficacy of the proposed algorithms. 

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