Calibration Method for Relativistic Navigation System Using Parallel Q-Learning Extended Kalman Filter.

For the relativistic navigation system where the position and velocity of the spacecraft are determined through the observation of the relativistic perturbations including stellar aberration and starlight gravitational deflection, a novel parallel Q-learning extended Kalman filter (PQEKF) is presented to implement the measurement bias calibration. The relativistic perturbations are extracted from the inter-star angle measurement achieved with a group of high-accuracy star sensors on the spacecraft. Inter-star angle measurement bias caused by the misalignment of the star sensors is one of the main error sources in the relativistic navigation system. In order to suppress the unfavorable effect of measurement bias on navigation performance, the PQEKF is developed to estimate the position and velocity, together with the calibration parameters, where the Q-learning approach is adopted to fine tune the process noise covariance matrix of the filter automatically. The high performance of the presented method is illustrated via numerical simulations in the scenario of medium Earth orbit (MEO) satellite navigation. The simulation results show that, for the considered MEO satellite and the presented PQEKF algorithm, in the case that the inter-star angle measurement accuracy is about 1 mas, after calibration, the positioning accuracy of the relativistic navigation system is less than 300 m.

Sowing Depth Monitoring System for High-Speed Precision Planters Based on Multi-Sensor Data Fusion.

High-speed precision planters are subject to high-speed (12~16 km/h) operation due to terrain undulation caused by mechanical vibration and sensor measurement errors caused by the sowing depth monitoring system's accuracy reduction problems. Thus, this study investigates multi-sensor data fusion technology based on the sowing depth monitoring systems of high-speed precision planters. Firstly, a sowing depth monitoring model comprising laser, ultrasonic, and angle sensors as the multi-sensor monitoring unit is established. Secondly, these three single sensors are filtered using the Kalman filter. Finally, a multi-sensor data fusion algorithm for optimising four key parameters in the extended Kalman filter (EKF) using an improved sparrow search algorithm (ISSA) is proposed. Subsequently, the filtered data from the three single sensors are integrated to address the issues of mechanical vibration interference and sensor measurement errors. In order to ascertain the superiority of the ISSA-EKF, the ISSA-EKF and SSA-EKF are simulated, and their values are compared with the original monitoring value of the sensor and the filtered sowing depth value. The simulation test demonstrates that the ISSA-EKF-based sowing depth monitoring algorithm for high-speed precision planters, with a mean absolute error (MAE) of 0.083 cm, root mean square error (RMSE) of 0.103 cm, and correlation coefficient (R) of 0.979 achieves high-precision monitoring. This is evidenced by a significant improvement in accuracy when compared with the original monitoring value of the sensor, the filtered value, and the SSA-EKF. The results of a field test demonstrate that the ISSA-EKF-based sowing depth monitoring system for high-speed precision planters enhances the precision and reliability of the monitoring system when compared with the three single-sensor monitoring values. The average MAE and RMSE are reduced by 0.071 cm and 0.075 cm, respectively, while the average R is improved by 0.036. This study offers a theoretical foundation for the advancement of sowing depth monitoring systems for high-speed precision planters.

Intelligent mobile robot for contagious disease treatments in hospitals.

This paper provides a novel and applicable work that builds a real system for disinfecting the air and surfaces of the environment in a hospital room, with a non-contact measurement system for supporting contagious disease treatments in hospitals. The system is built on an intelligent mobile robot system that operates autonomously in a simulated real treatment room. The research team uses a new positioning algorithm. It is a combination of data from the Lidar sensor, encoder, and Extended Kalman filter. The program that applies segmentation and image feature extraction algorithms is developed to meet requirements of real-time environment mapping in the room. Control algorithms for moving and avoiding obstacles are also proposed. Next, techniques for collecting health data including patient identification, body temperature, and blood oxygen index via wireless sensor network are also mentioned in the article. Analysis and experimental results show qualified outcomes and promise. The main contribution of the paper can be listed as follows.•Design and build a new CEE-IMR, an intelligent mobile robot that can regconize patients, guide and lead them walking in hospitals, especially keep a safe distance avoiding contagious deseases.•A novel framework for controlling the robot is proposed. The robot can move flexible, avoid obstacles, etc. based on advanced control algorithms. A new control mechanism is also proposed.•Methods of collecting data and processing medical data to support either patients or doctors to improve the effecency in hospitals in contagious disease management.

Angle-of-attack and angle-of-sideslip estimation and complementary filter design for civil aircraft.

Angle-of-Attack (AOA) and angle-of-sideslip (AOS) are critical flight parameters affecting the flight safety, and their accuracy and reliability directly impact the operating status and performance of some significant airborne systems. To enhance the redundancy and accuracy of AOA and AOS, this article investigates the problem of the airflow angles estimation and complementary filter design for civil aircraft. Specifically, an extended Kalman filter based AOA and AOS estimation method considering acceleration correction is developed to increase the redundancy. Subsequently, a novel inertial AOA and inertial AOS calculation method using attitude angles, azimuth angle, and flight path angle is introduced, and two schemes for designing the discrete complementary filter based on Tustin transform are presented to improve the accuracy. Through simulations, the developed algorithms are verified, and the results illustrate that the AOA estimation error is within ± 0.6°, and the AOS estimation error is within ± 0.3°.

An Integrated Navigation Method Aided by Position Correction Model and Velocity Model for AUVs.

When autonomous underwater vehicles (AUVs) perform underwater tasks, the absence of GPS position assistance can lead to a decrease in the accuracy of traditional navigation systems, such as the extended Kalman filter (EKF), due to the accumulation of errors. To enhance the navigation accuracy of AUVs in the absence of position assistance, this paper proposes an innovative navigation method that integrates a position correction model and a velocity model. Specifically, a velocity model is developed using a dynamic model and the Optimal Pruning Extreme Learning Machine (OP-ELM) method. This velocity model is trained online to provide velocity outputs during the intervals when the Doppler Velocity Log (DVL) is not updating, ensuring more consistent and reliable velocity estimation. Additionally, a position correction model (PCM) is constructed, based on a hybrid gated recurrent neural network (HGRNN). This model is specifically designed to correct the AUV's navigation position when GPS data are unavailable underwater. The HGRNN utilizes historical navigation data and patterns learned during training to predict and adjust the AUV's estimated position, thereby reducing the drift caused by the lack of real-time position updates. Experimental results demonstrate that the proposed VM-PCM-EKF algorithm can significantly improve the positioning accuracy of the navigation system, with a maximum accuracy improvement of 87.2% compared to conventional EKF algorithms. This method not only improves the reliability and accuracy of AUV missions but also opens up new possibilities for more complex and extended underwater operations.

Enhanced nonlinear active noise control: A novel approach using brain storm optimization algorithm.

Active Noise Control (ANC) systems play a crucial role in reducing unwanted noise in various settings. Traditional ANC methods, like the Filtered-x Least Mean Squares (FxLMS) algorithm, are effective in linear noise scenarios. However, they often struggle with more nonlinear and complex noise patterns. This paper introduces a novel approach using the brain storm optimization (BSO) algorithm in nonlinear ANC systems, which represents a significant departure from conventional techniques. The BSO algorithm, inspired by human brainstorming processes, excels in addressing the complexities of nonlinear noise by incorporating principles, such as delayed evaluation, free imagination, quantity and quality, and comprehensive improvement. By combining the BSO algorithm with an Extended Kalman Filter (EKF), a new ANC system is proposed that can adapt to a wide range of noise types with improved speed and accuracy. Experimental results showcase the superior performance of the BSO algorithm, achieving an impressive noise reduction of up to 48 dB (dB) in a 500Hz sinusoidal noise scenario, with a convergence time as fast as 0.01 s, outperforming the FxLMS algorithm by a significant margin. Moreover, in complex environments with multi-frequency and random noise, the BSO algorithm consistently demonstrates better noise reduction and quicker convergence, reducing noise levels by up to 27 dB within 0.001 s. The innovative use of the BSO algorithm in ANC systems not only enhances noise reduction capabilities, especially for nonlinear and complex noise signals, but also improves convergence times, paving the way for future advancements in ANC technologies.

Comparison of KF-Based Vehicle Sideslip Estimation Logics with Increasing Complexity for a Passenger Car.

Nowadays, control is pervasive in vehicles, and a full and accurate knowledge of vehicle states is crucial to guarantee safety levels and support the development of Advanced Driver-Assistance Systems (ADASs). In this scenario, real-time monitoring of the vehicle sideslip angle becomes fundamental, and various virtual sensing techniques based on both vehicle dynamics models and data-driven methods are widely presented in the literature. Given the need for on-board embedded device solutions in autonomous vehicles, it is mandatory to find the correct balance between estimation accuracy and the computational burden required. This work mainly presents different physical KF-based methodologies and proposes both mathematical and graphical analysis to explore the effectiveness of these solutions, all employing equal tire and vehicle simplified models. For this purpose, results are compared with accurate sensor acquisition provided by the on-track campaign on passenger vehicles; moreover, to truthfully represent the possibility of using such virtual sensing techniques in real-world scenarios, the vehicle is also equipped with low-end sensors that provide information to all the employed observers.

Online state estimation in water distribution systems via Extended Kalman Filtering.

Operators of water distribution systems (WDSs) need continuous and timely information on pressures and flows to ensure smooth operation and respond quickly to unexpected events. While hydraulic models provide reasonable estimates of pressures and flows in WDSs, updating model predictions with real-time sensor data provides clearer insights into true system behavior and enables more effective real-time response. Despite the growing prevalence of distributed sensing within WDSs, standard hydraulic modeling software like EPANET do not support synchronous data assimilation. This study presents a new method for state estimation in WDSs that combines a fully physically-based model of WDS hydraulics with an Extended Kalman Filter (EKF) to estimate system flows and heads based on sparse sensor measurements. To perform state estimation via EKF, a state-space model of the hydraulic system is first formulated based on the 1-D Saint-Venant equations of conservation of mass and momentum. Results demonstrate that the proposed model closely matches steady-state extended-period models simulated using EPANET. Next, through a holdout analysis it is found that fusing sensor data with EKF produces flow and head estimates that closely match ground truth flows and heads at unmonitored locations, indicating that state estimation successfully infers internal hydraulic states from sparse sensor measurements. These findings pave the way towards real-time operational models of WDSs that will enable online detection and mitigation of hazards like pipe leaks, main bursts, and hydraulic transients.

Neural network aided extended Kalman filtering for inverse imaging of cardiac transmembrane potential.

Objective.The aim of this study is to address the limitations in reconstructing the electrical activity of the heart from the body surface electrocardiogram, which is an ill-posed inverse problem. Current methods often assume values commonly used in the literature in the absence ofa prioriknowledge, leading to errors in the model. Furthermore, most methods ignore the dynamic activation process inherent in cardiomyocytes during the cardiac cycle.Approach.To overcome these limitations, we propose an extended Kalman filter (EKF)-based neural network approach to dynamically reconstruct cardiac transmembrane potential (TMP). Specifically, a recurrent neural network is used to establish the state estimation equation of the EKF, while a convolutional neural network is used as the measurement equation. The Jacobi matrix of the network undergoes a correction feedback process to obtain the Kalman gain.Main results.After repeated iterations, the final estimated state vector, i.e. the reconstructed image of the TMP, is obtained. The results from both the final simulation and real experiments demonstrate the robustness and accurate quantification of the model.Significance.This study presents a new approach to cardiac TMP reconstruction that offers higher accuracy and robustness compared to traditional methods. The use of neural networks and EKFs allows dynamic modelling that takes into account the activation processes inherent in cardiomyocytes and does not requirea prioriknowledge of inputs such as forward transition matrices.

Quaternion-Based Attitude Estimation of an Aircraft Model Using Computer Vision.

Investigating aircraft flight dynamics often requires dynamic wind tunnel testing. This paper proposes a non-contact, off-board instrumentation method using vision-based techniques. The method utilises a sequential process of Harris corner detection, Kanade-Lucas-Tomasi tracking, and quaternions to identify the Euler angles from a pair of cameras, one with a side view and the other with a top view. The method validation involves simulating a 3D CAD model for rotational motion with a single degree-of-freedom. The numerical analysis quantifies the results, while the proposed approach is analysed analytically. This approach results in a 45.41% enhancement in accuracy over an earlier direction cosine matrix method. Specifically, the quaternion-based method achieves root mean square errors of 0.0101 rad/s, 0.0361 rad/s, and 0.0036 rad/s for the dynamic measurements of roll rate, pitch rate, and yaw rate, respectively. Notably, the method exhibits a 98.08% accuracy for the pitch rate. These results highlight the performance of quaternion-based attitude estimation in dynamic wind tunnel testing. Furthermore, an extended Kalman filter is applied to integrate the generated on-board instrumentation data (inertial measurement unit, potentiometer gimbal) and the results of the proposed vision-based method. The extended Kalman filter state estimation achieves root mean square errors of 0.0090 rad/s, 0.0262 rad/s, and 0.0034 rad/s for the dynamic measurements of roll rate, pitch rate, and yaw rate, respectively. This method exhibits an improved accuracy of 98.61% for the estimation of pitch rate, indicating its higher efficiency over the standalone implementation of the direction cosine method for dynamic wind tunnel testing.

The Influence of Temporal Disturbances in EKF Calculations on the Achieved Parameters of Flight Control and Stabilization of UAVs.

This article investigates the causes of occasional flight instability observed in Unmanned Aerial Vehicles (UAVs). The issue manifests as unexpected oscillations that can lead to emergency landings. The analysis focuses on delays in the Extended Kalman Filter (EKF) algorithm used to estimate the drone's attitude, position, and velocity. These delays disrupt the flight stabilization process. The research identifies two potential causes for the delays. First cause is magnetic field distrurbances created by UAV motors and external magnetic fields (e.g., power lines) that can interfere with magnetometer readings, leading to extended EKF calculations. Second cause is EKF fusion step implementation of the PX4-ECL library combining magnetometer data with other sensor measurements, which can become computionally expensive, especially when dealing with inconsistent magnetic field readings. This can significantly increase EKF processing time. The authors propose a solution of moving the magnetic field estimation calculations to a separate, lower-priority thread. This would prevent them from blocking the main EKF loop and causing delays. The implemented monitoring techniques allow for continuous observation of the real-time operating system's behavior. Since addressing the identified issues, no significant problems have been encountered during flights. However, ongoing monitoring is crucial due to the infrequent and unpredictable nature of the disturbances.

Adaptive Multi-Sensor Joint Tracking Algorithm with Unknown Noise Characteristics.

In this study, to solve the low accuracy of multi-space-based sensor joint tracking in the presence of unknown noise characteristics, an adaptive multi-sensor joint tracking algorithm (AMSJTA) is proposed. First, the coordinate transformation from the target object to the optical sensors is considered, and the observation vector-based measurement model is established. Then, the measurement noise characteristics are assumed to be white Gaussian noise, and the measurement covariance matrix is set as a constant. On this premise, the traditional iterative extended Kalman filter is applied to solve this problem. However, in most actual engineering applications, the measurement noise characteristics are unknown. Thus, a forgetting factor is introduced to adaptively estimate the unknown measurement noise characteristics, and the AMSJTA is designed to improve the tracking accuracy. Furthermore, the lower bound of the proposed algorithm is theoretically proved. Finally, numerical simulations are executed to verify the effectiveness and superiority of the proposed AMSJTA.

An Improved Initial Alignment Method Based on SE2(3)/EKF for SINS/GNSS Integrated Navigation System with Large Misalignment Angles.

This paper proposes an improved initial alignment method for a strap-down inertial navigation system/global navigation satellite system (SINS/GNSS) integrated navigation system with large misalignment angles. Its methodology is based on the three-dimensional special Euclidean group and extended Kalman filter (SE2(3)/EKF) and aims to overcome the challenges of achieving fast alignment under large misalignment angles using traditional methods. To accurately characterize the state errors of attitude, velocity, and position, these elements are constructed as elements of a Lie group. The nonlinear error on the Lie group can then be well quantified. Additionally, a group vector mixed error model is developed, taking into account the zero bias errors of gyroscopes and accelerometers. Using this new error definition, a GNSS-assisted SINS dynamic initial alignment algorithm is derived, which is based on the invariance of velocity and position measurements. Simulation experiments demonstrate that the alignment method based on SE2(3)/EKF can achieve a higher accuracy in various scenarios with large misalignment angles, while the attitude error can be rapidly reduced to a lower level.

3D Indoor Position Estimation Based on a UDU Factorization Extended Kalman Filter Structure Using Beacon Distance and Inertial Measurement Unit Data.

The development of the GPS (Global Positioning System) and related advances have made it possible to conceive of an outdoor positioning system with great accuracy; however, for indoor positioning, more efficient, reliable, and cost-effective technology is required. There are a variety of techniques utilized for indoor positioning, such as those that are Wi-Fi, Bluetooth, infrared, ultrasound, magnetic, and visual-marker-based. This work aims to design an accurate position estimation algorithm by combining raw distance data from ultrasonic sensors (Marvelmind Beacon) and acceleration data from an inertial measurement unit (IMU), utilizing the extended Kalman filter (EKF) with UDU factorization (expressed as the product of a triangular, a diagonal, and the transpose of the triangular matrix) approach. Initially, a position estimate is calculated through the use of a recursive least squares (RLS) method with a trilateration algorithm, utilizing raw distance data. This solution is then combined with acceleration data collected from the Marvelmind sensor, resulting in a position solution akin to that of the GPS. The data were initially collected via the ROS (Robot Operating System) platform and then via the Pixhawk development card, with tests conducted using a combination of four fixed and one moving Marvelmind sensors, as well as three fixed and one moving sensors. The designed algorithm is found to produce accurate results for position estimation, and is subsequently implemented on an embedded development card (Pixhawk). The tests showed that the designed algorithm gives accurate results with centimeter precision. Furthermore, test results have shown that the UDU-EKF structure integrated into the embedded system is faster than the classical EKF.

Localization of solar panel cleaning robot combining vision processing and extended Kalman filter.

In this study, we introduce a method for estimating the position of a self-driving solar panel-cleaning mobile robot. This estimation relies on line counts, typically 16 cm in panel width, obtained through image processing on the panel floor, along with wheel encoder information and inertial sensor data. To achieve accurate line counts, we introduce two adjusted threshold values and allow offsets in these values based on the robot's speed. Additionally, inertial measurement unit (IMU) signals assist in determining whether a line is horizontal or vertical, depending on the robot's movement direction on the panel, utilizing the robot's heading angle and detected line angle. When the robot is positioned between lines on the panel, more precise location estimation is necessary beyond simple line counts. To tackle this challenge, we integrate the extended Kalman filter with IMU data and encoder information, significantly enhancing position estimation. This integration achieves an RMSE accuracy value of up to 0.089 m, notably at a relatively high speed of 100 mm/s. This margin of error is almost half that of the vision-based line-counting method.

A Monte Carlo-Based Iterative Extended Kalman Filter for Bearings-Only Tracking of Sea Targets.

In this paper, a Monte Carlo (MC)-based extended Kalman filter is proposed for a two-dimensional bearings-only tracking problem (BOT). This problem addresses the processing of noise-corrupted bearing measurements from a sea acoustic source and estimates state vectors including position and velocity. Due to the nonlinearity and complex observability properties in the BOT problem, a wide area of research has been focused on improving its state estimation accuracy. The objective of this research is to present an accurate approach to estimate the relative position and velocity of the source with respect to the maneuvering observer. This approach is implemented using the iterated extended Kalman filter (IEKF) in an MC-based iterative structure (MC-IEKF). Re-linearizing dynamic and measurement equations using the IEKF along with the MC campaign applied to the initial conditions result in significantly improved accuracy in the estimation process. Furthermore, an observability analysis is conducted to show the effectiveness of the designed maneuver of the observer. A comparison with the widely used UKF algorithm is carried out to demonstrate the performance of the proposed method.

Extended Kalman Filter-Based Vehicle Tracking Using Uniform Planar Array for Vehicle Platoon Systems.

We develop an extended Kalman filter-based vehicle tracking algorithm, specifically designed for uniform planar array layouts and vehicle platoon scenarios. We first propose an antenna placement strategy to design the optimal antenna array configuration for precise vehicle tracking in vehicle-to-infrastructure networks. Furthermore, a vehicle tracking algorithm is proposed to improve the position estimation performance by specifically considering the characteristics of the state evolution model for vehicles in the platoon. The proposed algorithm enables the sharing of corrected error transition vectors among platoon vehicles, for the purpose of enhancing the tracking performance for vehicles in unfavorable positions. Lastly, we propose an array partitioning algorithm that effectively divides the entire antenna array into sub-arrays for vehicles in the platoon, aiming to maximize the average tracking performance. Numerical studies verify that the proposed tracking and array partitioning algorithms improve the position estimation performance.

Digital twin with augmented state extended Kalman filters for forecasting electric power consumption of industrial production systems.

The work aims to develop an effective tool based on Digital Twins (DTs) for forecasting electric power consumption of industrial production systems. DTs integrate dynamic models combined with Augmented State Extended Kalman Filters (ASEKFs) in a learning process. The connection with the real counterpart is realized exclusively through non-intrusive sensors. This architecture enables the model development of industrial systems (components, machinery and processes) on which complete knowledge is not available, by identifying the model's unknown parameters through short online training phases and small amounts of real-time raw data. ASEKFs track the unknowns keeping models updated as physical systems evolve. When a forecast is needed, the current estimates of the uncertain parameters are integrated into the dynamic models. These can then be used without ASEKFs to predict the actual energy use of the system under the desired operating conditions, including scenarios that differ from typical functioning. The approach is validated offline with reference to the electricity consumption of an automatic coffee machine, which represents a real test environment and a blueprint to design DTs for other industrial systems. The appliance is observed by measuring the supply voltage and the absorbed current. The accuracy of the results is analyzed and discussed. This method is developed in the context of energy consumption prediction and optimization in the manufacturing industry through refined energy management and planning.

Fusion-based approach for hydrometeorological drought modeling: a regional investigation for Iran.

The objective of this study was to model a new drought index called the Fusion-based Hydrological Meteorological Drought Index (FHMDI) to simultaneously monitor hydrological and meteorological drought. Aiming to estimate drought more accurately, local measurements were classified into various clusters using the AGNES clustering algorithm. Four single artificial intelligence (SAI) models-namely, Gaussian Process Regression (GPR), Ensemble, Feedforward Neural Networks (FNN), and Support Vector Regression (SVR)-were developed for each cluster. To promote the results of single of products and models, four fusion-based approaches, namely, Wavelet-Based (WB), Weighted Majority Voting (WMV), Extended Kalman Filter (EKF), and Entropy Weight (EW) methods, were used to estimate FHMDI in different time scales, precipitation, and runoff. The performance of single and combined products and models was assessed through statistical error metrics, such as Kling-Gupta efficiency (KGE), Mean Bias Error (MBE), and Normalized Root Mean Square Error (NRMSE). The performance of the proposed methodology was tested over 24 main river basins in Iran. The validation results of the FHMDI (the compliance of the index with the pre-existing drought index) revealed that it accurately identified drought conditions. The results indicated that individual products performed well in some river basins, while fusion-based models improved dataset accuracy more compared to local measurements. The WMV with the highest accuracy (lowest NRMSE) had a good performance in 60% of the cases compared to all other products and fusion-based models. WMV also showed higher efficiency in 100% of the cases than all other fusion-based and SAI models for simultaneous hydrological and meteorological drought estimation. In light of these findings, we recommend the use of fusion-based approach to improve drought modeling.

Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment.

This research delves into advancing an ultra-wideband (UWB) localization system through the integration of filtering technologies (moving average (MVG), Kalman filter (KF), extended Kalman filter (EKF)) with a low-pass filter (LPF). We investigated new approaches to enhance the precision and reduce noise of the current filtering methods-MVG, KF, and EKF. Using a TurtleBot robotic platform with a camera, our research thoroughly examines the UWB system in various trajectory situations (square, circular, and free paths with 2 m, 2.2 m, and 5 m distances). Particularly in the square path trajectory with the lowest root mean square error (RMSE) values (40.22 mm on the X axis, and 78.71 mm on the Y axis), the extended Kalman filter with low-pass filter (EKF + LPF) shows notable accuracy. This filter stands out among the others. Furthermore, we find that integrated method using LPF outperforms MVG, KF, and EKF consistently, reducing the mean absolute error (MAE) to 3.39% for square paths, 4.21% for circular paths, and 6.16% for free paths. This study highlights the effectiveness of EKF + LPF for accurate indoor localization for UWB systems.