complementary filter

Inversion of magnetic data to characterisegeological structures, such as dikes, is a fundamental challenge in engineering geophysics due to its highly non-linear and ill-posed nature, necessitating robust optimization methods. This study introduces and evaluates for the first time, the Mountain Gazelle Optimizer (MGO) for the first time, examining its efficiency and potential as an effective solution to this problem. The MGOalgorithm is designed to find the global optimum by intelligently balancing exploration and exploitation within the parameter space. The performance of the MGO was assessed by comparing it with two distinct approaches: a powerful machine learning algorithm called Random Forest (RF), and a classic processing-estimation method based on Reduction to the Pole (RTP). Evaluations were conducted on synthetic data (with noise levels ranging from 0% to 20%) as well as on real field data from the Gansu iron deposit in China. The results clearly demonstrated the superiority of MGO in all scenarios. Not only did the algorithm exhibit greater stability against noise than RF, it also, achieved a Root Mean Square Error (RMSE) of 0.48 in the real data case study,, which was significantly lower than the error achieved by the classic method (0.88). Furthermore, the parameters estimated by MGO showed better alignment with the geological information from existing drilling data in the area. This study suggests that MGO's superiority obtained from its direct and global inversion approach. Ultimately, MGO is presented as an accurate and reliable tool for exploration and engineering applications.

Bu calismada, ataletsel olcumlerin yeterliligi icin ivmeolcer ve jiroskop datalarinin birlesimi ile elde edilen bilgilerin kullanildigi bir dijital filtre uygulamasi gelistirilmistir. Ilk olarak ivmeolcer ve jiroskop datalarinin ozellikleri ele alinarak aciklandi. Sonra hareket edinim, egim ve sapma acilari acikladi. Bu islemlerden sonra elde edilen gerekli parametreler ivmeolcer ve jiroskopun cikisina uygulandi. Sonuc olarak uygulanan filtreler bu olcum birimlerinin kombinasyonu ile karar verme islemini gerceklestirdi. Bu uygulama ile elde edilen veriler ayni zamanda ornek calismalarda denendi.

This paper presents a solution for detecting and recovery for the spoof error of Global Positioning System receiver signals, in order to increase the accuracy of the navigation system integrating inertial systems with GPS signals. Integrated inertial navigation and GPS data has many advantages. However, due to the weakness of satellite signals against jamming and spoof attacks of providing analytical solutions, they have a special place in improving Kalman filter estimation compared to hardware solutions. In this paper, a new method for loosely coupled of INS/GPS is presented, in which the steady state of Kalman matrix gain parameters is used during deception detection and recovery. With the gain parameters of the Kalman filter tending to constant values, with the aim of correcting and predicting the error of state variables, it can be used to detect GPS spoofed data. It can be detected by spoof in the GPS receiver signal when couple with inertial waves through the amount of Kalman filter gain fluctuations. In the case of closed loop, the Kalman filter's gain matrix denominators tend to a constant value, and in case of deception, this function is associated with many fluctuations. By using dynamic weighting, the effect of errors caused by these attacks is recovered.

Piezoelectric tube actuators are widely used in nanopositioning applications, especially in scanning probe microscopes to manipulate matter at nanometer scale. Accurate displacement control of these actuators is critical, and in order to avoid the expense and practical limits of highly accurate displacement sensors, sensorless control has recently attracted much attention. As the electrical charge on these actuators is an accurate indicator of their displacement exhibiting almost no hysteresis over a wide range of frequencies, it suggests that charge measurement can replace displacement sensors. However, chargebased methods suffer from poor low frequency response and voltage drop across the sensing capacitor. In this paper, a displacement estimator is presented that complements a digitally implemented charge amplifier with an artificial neural network (ANN) designed and trained to estimate the piezoelectric tube's displacement using the piezoelectric voltage at low frequencies of excitation where the charge methods fail. A complementary filter combines the grounded-load digital charge amplifier (GDCDE) and the ANN to estimate displacement over a wide bandwidth and to overcome drift. The discrepancy between the desired and estimated displacement is fed back to the piezoelectric actuator using proportional control. Experimental results highlight the effectiveness of the proposed design.

Orientation estimation is very important for development of unmanned aerial systems (UASs), and is performed by combining data from several sources and sensors. Kalman filters are widely used for this task, however they typically assume linearity and Gaussian noise statistics. While these assumptions work well for highquality, high-cost sensors, it does not work as well for low-cost, low-quality sensors. For lowcost sensors, complementary filters can be used since no assumptions are made with regards to linearity and noise statistics. In this article, the history and basics of complementary filters are included with examples, the concepts of filtering based on fractional-order calculus are applied to the complementary filter, and the efficacy of C. Coopmans (B)

Research in the field of robotics is tightly connected to simulation tools for many reasons. On one side, simulation supports the development of new advanced control algorithms and on the other side it is always not feasible to build a whole robot system to test some algorithms or it is not safe to perform tests on a real system (at least in the first design stages). In the paper we present an integrated environment for the design and testing of advanced robot control schemes, including visual tracking, force feedback on a single robot or in multi-robot applications. The kernel of our simulation environment is MATLAB/Simulink. The main capabilities are: the simulation of the kinematics and dynamics of manipulators, the integration of different sensor systems like vision and force sensors, scenarios for complex robot tasks, the visualization of robots and their environment and the integration of real robots in the simulation loop. The advantage of our system is the simplicity, which allows easy integration of different robots, sensors and other devices. Some of these can be easier simulated by using other tools. Hence, other simulation tools can be used for the simulation of different parts of the system and then these subsystems are integrated in out simulation environment. The other important feature is easy final testing of developed control algorithms. Namely, for final testing of the control algorithms the models in the simulation scheme are just replaced by interface blocks for real system and the user does not need to consider implementation details. Finally, to show the efficiency and usability of our control design environment we outline some typical experimental examples using our robots. We explain some typical control design procedures from the "pure" simulation to the testing of algorithms on real robots.

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to

It is well known that stand-alone inertial navigation systems (INS) have their errors diverging with time. Consequently, an upper bound on the duration of INS systems precludes their use in low-cost micro unmanned aerial vehicles. The traditional approach for solving this matter is to resort to aiding devices, e.g., global navigation satellite system (GNSS) receivers, sighting devices, etc. Two philosophies have been extensively applied to perform data fusion: extended Kalman filtering (EKF) and complementary filtering (CF). Previous work in the literature showed that the computationally less expensive CF can be robustly applied to attitude estimation using low-cost sensors and achieve performance that is comparable to that of a full EKF. However, performance is degraded by vehicle manoeuvres and no measurement on estimate uncertainties is given. Furthermore, a large number of sensors makes it impracticable for optimal tuning of the CF. The present work lays the foundation for sensor filtering that employs the CF for attitude estimation by means of a magnetometer as an external aid, and an EKF for additional sensors integration. The main feature of this architecture is the possibility of deployment in a distributed multi-platform system and implementation of fault isolation by running the CF stage in a separate low-throughput reliable machine for stand-alone degraded mode operation. A case study is performed on synthetic data from inertial, magnetic and GNSS sensors.

A magnetic and inertial measurement unit (MIMU) usually measures acceleration, rotation rate, and earth's magnetic field in order to determine a body's attitude. In order to find the orientation information using all sensor information a fusion algorithm is used. Extended Kalman filtering is a well known technique that has been widely applied in many applications used for state estimation. The main idea behind the algorithm is that a series of observations over time is used to produce estimates of unknown states leading to more precise orientation information than compared to when estimated states were only based on single observations. However, one problem exists, namely the Extended Kalman filtering solution becomes very poor when abrupt acceleration motions occur. In order to avoid this problem, an optimization algorithm can be integrated into the filtering mechanism as a dynamic model correction. Thus, this paper introduces a genetic algorithm to be utilized as a noise-adaptive mechanism in order to tune the Extended Kalman filter process.

Bu calismada, ataletsel olcumlerin yeterliligi icin ivmeolcer ve jiroskop datalarinin birlesimi ile elde edilen bilgilerin kullanildigi bir dijital filtre uygulamasi gelistirilmistir. Ilk olarak ivmeolcer ve jiroskop datalarinin ozellikleri ele alinarak aciklandi. Sonra hareket edinim, egim ve sapma acilari acikladi. Bu islemlerden sonra elde edilen gerekli parametreler ivmeolcer ve jiroskopun cikisina uygulandi. Sonuc olarak uygulanan filtreler bu olcum birimlerinin kombinasyonu ile karar verme islemini gerceklestirdi. Bu uygulama ile elde edilen veriler ayni zamanda ornek calismalarda denendi.

Inertial Measurement Unit is commonly used in various applications especially as a low-cost system for localization and attitude estimation. Some applications are: real-time motion capture system, gait analysis for rehabilitation purposes, biomedical applications, advanced robotic applications such as mobile robot localization and Unmanned Aerial Vehicles (UAV) attitude estimation. In all the mentioned applications the accuracy and the fast response are the most important requirements, thus the research is focused on the design and the implementation of highly accurate hardware systems and fast sensor data fusion algorithms, named Attitude and Heading Reference System (AHRS), aimed at estimating the orientation of a rigid body with respect to a reference frame. A large number of different solutions can be found in the literature, and an experimental comparison of the most popular is presented in this work. In particular, the algorithm based on the gradient descent method and the algorithm based on a nonlinear complementary filter are compared to a standard Extended Kalman Filter (EKF) with the aim to show that a general method can easily compete with ad-hoc solutions and even outperform them in particular conditions. In order to validate the estimation accuracy a Kuka robot is used to compute the ground truth. Moreover, in order to estimate the computational burden, the algorithms are implemented on an ARM-Cortex M4-based evaluation board.

Inertial Measurement Units (IMU) are in highlight for joint and motion monitoring applications. Several IMU sensor fusion algorithms have been proposed in literature. Kalman Filter and its variants are the most used for more precision. However, they are computationally expensive. However, for faster computations, researchers and industry use complementary filter. More recently, a new variant of Kalman Filter was introduced as a Double Stage Kalman Filter in order to reduce the Kalman Filters computation cost. Our research investigates the performance of the Complementary and Double Stage Kalman filters in monitoring of joints in serial manipulators using Microelectromechanical-system MEMS based IMU. This study carried dynamic experiments using a serial robot to estimate the orientation of IMU, thus the joint angle of the associated segment. The study showed that both filters yield accurate estimations. The study showed also that Double Stage Kalman Filter has lower RMSE and achieves more precise estimates than Complementary filter mainly when the movement is around IMU x-and y-axis. Our findings indicate that the Double Stage Kalman Filter can achieve higher precision than the complementary filter using lower computation time than the former variants of the Kalman Filters in serial manipulator joint monitoring applications.

In this paper, a method for improving the performance of Complementary filter in the Attitude and Heading Reference System for estimating the orientation in accelerated movements is presented. Although existing complementary filters have advantages such as low computational volume, stability in different dynamic conditions, effectiveness at low sampling rates, and simplicity in the parameter setting process; But in the situation where the mobile device is exposed to non-gravitational accelerations, they show inappropriate performance. The proposed algorithm is designed based on the threshold-based path selection method and by adjusting the gain of complementary filters according to the size of the external acceleration, it improves the estimation of angles. In the following, the proposed algorithm is compared with the Extended Kalman filter and its three adaptive versions. The simulation and evaluation results of the proposed method show that the improved complementary filters achieve good performance in accelerated movements compared to the AEKF in the direction and alignment reference system.

Several robot-related studies have been conducted in recent years; however, studies on the autonomous travel of small mobile robots in small spaces are lacking. In this study, we investigate the development of autonomous travel for small robots that need to travel and cover the entire smooth surface, such as those employed for cleaning tables or solar panels. We consider an obstacle-available surface and target this travel on it by proposing a spiral motion method. To achieve the spiral motion, we focus on developing autonomous avoidance of obstacles, return to original path, and fall prevention when robots traverse a surface. The development of regular travel by a robot without an encoder is an important feature of this study. The traveled distance was measured using the traveling time. We achieved spiral motion by analyzing the data from multiple small sensors installed on the robot by introducing a new attitude-control method, and we ensured that the robot returned to the origina...

Hand motion tracking plays an important role in virtual reality systems for immersion and interaction purposes. This paper discusses the problem of finger tracking and proposes the application of the extension of the Madgwick filter and a simple switching (motion recognition) algorithm as a comparison. The proposed algorithms utilize the three-link finger model and provide complete information about the position and orientation of the metacarpus. The numerical experiment shows that this approach is feasible and overcomes some of the major limitations of inertial motion tracking. The paper’s proposed solution was created in order to track a user’s pointing and grasping movements during the interaction with the virtual reconstruction of the cultural heritage of historical cities.

The paper introduces the attitude estimation and compensation in odometric localization of a differential drive indoor mobile robot. A mobile robot navigates through an inclined indoor environment, wherein localization using only wheel encoder is erroneous. The robot uses inertial sensors such as gyroscope, accelerometer and magnetometer to calculate its attitude and acquires a three degree of rotational data. It is observed that the attitude update using gyroscopes alone are prone to diverge and hence error needs to be eliminated. The advantage of MEMS sensors is less-cost while complementary filter algorithm is low complexity in implementation. The performance of the proposed complementary filter algorithm for attitude estimation and compensation in odometric localization are shown by experiment and analysis of results.

Several robot-related studies have been conducted in recent years; however, studies on the autonomous travel of small mobile robots in small spaces are lacking. In this study, we investigate the development of autonomous travel for small robots that need to travel and cover the entire smooth surface, such as those employed for cleaning tables or solar panels. We consider an obstacle-available surface and target this travel on it by proposing a spiral motion method. To achieve the spiral motion, we focus on developing autonomous avoidance of obstacles, return to original path, and fall prevention when robots traverse a surface. The development of regular travel by a robot without an encoder is an important feature of this study. The traveled distance was measured using the traveling time. We achieved spiral motion by analyzing the data from multiple small sensors installed on the robot by introducing a new attitude-control method, and we ensured that the robot returned to the origina...