Erna Apriliani

Ensemble and Fuzzy Kalman Filter for position estimation of an autonomous underwater vehicle based on dynamical system of AUV motion

Ensemble Kalman Filter (EnKF) algorithm can be applied to estimate AUV position.Fuzzy Kalman Filter (FKF) algorithm can be applied to estimate AUV position.The system dynamic of AUV motion is used for true trajectory of estimation.EnKF estimation is better than FKF estimation in AUV position estimation.Performance of each method based on RMSE and computational time. An underwater vehicle is useful in the monitoring of the unstructured and dangerous underwater conditions. One of the unmanned underwater vehicle is AUV. AUV is a robotic device that is driven through the water by a propulsion system, controlled and piloted by an onboard computer, and maneuverable in three dimensions. This research explains about position estimation of AUV based on the Ensemble Kalman Filter (EnKF) and the Fuzzy Kalman Filter (FKF). EnKF is used as the estimation method of AUVs position that maneuvering in 6 DOF (Degrees of Freedom) with the specified trajectory. The estimation results are simulated with Matlab. The simulations show the AUV position estimation based on the EnKF with some of the different ensembles and the comparison results of the position estimation between the EnKF and the FKF. The final result of these study shows that Ensemble Kalman Filter is better to estimate the trajectory of the dynamical equation of AUV motion with the error estimation of EnKF is 92% smaller in the x-position dan y-position, 6.5% smaller in the z-position, 93% smaller in the angle dan the computation of time is 50% faster than the estimation results of FKF.

Estimate and control position autonomous Underwater Vehicle based on determined trajectory using Fuzzy Kalman Filter method

Unmanned Underwater Vehicle (UUV), known as underwater drones, are any vehicle that are able to operate underwater without human occupant. AUV (Autonomous Underwater Vehicle) are one of categories of these vehicles which operate independently of direct human input. This AUV is required to have a navigation system that can manoeuvred 6 Degree of Freedom (DOF) and able to estimate the exact position based on the determined trajectory. Fuzzy Kalman Filter (FKF) method is used to estimate the position of the AUV. This process is used to maintain the accuracy of the trajectory. The performance of FKF algorithm on some several trajectory cases show that this method has relatively small Root Means Square Error (RSME), which is less than 10%.

Identification and estimation of state variables on reduced model using balanced truncation method

In this paper, we study the identification of variables on a model reduction process and estimation of variables on reduced system. We aim to relate variables on reduced and original system, so that we can compare the estimation accuracy of the original system and reduced system. As such, the objective of this paper is to discuss identification and estimation of variables on reduced model. First, model order reduction is done by using balanced truncation method. This process begins with the construction of balanced system. After that, we identify the relationship between variables of the balanced system and the original system. Then, we eliminate variables of the balanced system that have a small influence on the system. Furthermore, we estimate state variables on the original system and reduced system using a Kalman Filter algorithm. Finally, we compare the estimation result of the identified reduced and original system.

Estimation of three-dimensional radar tracking using modified extended kalman filter

Kalman filter is an estimation method by combining data and mathematical models then developed be extended Kalman filter to handle nonlinear systems. Three-dimensional radar tracking is one of example of nonlinear system. In this paper developed a modification method of extended Kalman filter from the direct decline of the three-dimensional radar tracking case. The development of this filter algorithm can solve the three-dimensional radar measurements in the case proposed in this case the target measured by radar with distance r, azimuth angle θ, and the elevation angle . Artificial covariance and mean adjusted directly on the three-dimensional radar system. Simulations result show that the proposed formulation is effective in the calculation of nonlinear measurement compared with extended Kalman filter with the value error at 0.77% until 1.15%.

Fractional kalman filter to estimate the concentration of air pollution

Air pollution problem gives important effect in quality environment and quality of humans life. Air pollution can be caused by nature sources or human activities. Pollutant for example Ozone, a harmful gas formed by NOx and volatile organic compounds (VOCs) emitted from various sources. The air pollution problem can be modeled by TAPM-CTM (The Air Pollution Model with Chemical Transport Model). The model shows concentration of pollutant in the air. Therefore, it is important to estimate concentration of air pollutant. Estimation method can be used for forecast pollutant concentration in future and keep stability of air quality. In this research, an algorithm is developed, based on Fractional Kalman Filter to solve the model of air pollutions problem. The model will be discretized first and then it will be estimated by the method. The result shows that estimation of Fractional Kalman Filter has better accuracy than estimation of Kalman Filter. The accuracy was tested by applying RMSE (Root Mean Square Error).

Data Assimilation to Estimate the Water Level of River

Data assimilation is an estimation method for stochastic dynamic system by combining the mathematical model with measurement data. Water level and velocity of river are stochastic dynamic system, and it is important to estimate the water level and velocity of river flow to reduce flood risk disaster. Here, we estimate the water level and velocity of river flow by using data assimilation specially Kalman filter and Ensemble Kalman filter. We define mathematical model of river flow, discretize and do simulation by Kalman filter and Ensemble Kalman filter. In data assimilation, we forecast the water level and velocity by using mathematical model and based on the measurement data, the correction of forecasting is made.

The distance between the cylinder affect the cylinder pressure

Trimaran is a vessel with a hull that is associated with the three bridge structures. Assuming that the elliptical cylinder-shaped hull, trimaran built by three elliptical cylinder with side-by-side configuration. The distance between the ellipse varies will determine the pressure received by the ellipse in the center. This problem is solved using the Navier-Stokes equations and solved by the finite difference. Results from this study is the lowest pressure received by elliptical midddle.

Perbandingan Algoritma Golub Kahan danQR Simetri untuk Dekomposisi Nilai Singular

Estimasi variabel maupun parameter pada sistem berskala besar, khusus- nya dengan Filter Kalman dibutuhkan waktu komputasi yang lama. Dengan melakukan reduksi rank matriks kovariansi, waktu komputasi dapat diper- cepat. Reduksi rank dapat dilakukan dengan Dekomposisi nilai singular (SVD), reduksi rank ini tidak mengurangi tingkat akurasi hasil estimasi. Pada paper ini dibahas perbandingan dua algoritma untuk dekomposisi nilai singular, yaitu Golub Kahan dan QR Simetri. Dilakukan uji empiris pada berbagai macam matriks untuk membandingkan waktu kerja kedua algoritma tersebut. Dari hasil simulasi diperoleh bahwa algoritma QR Simetri memerlukan waktu komputasi yang lebih cepat dibandingkan dengan algoritma Golub Kahan.