Algoritma Hibrid Extended Kalman Filter dan Inferensi Fuzzy untuk Penjejakan Target Bermanuver

Pada penelitian ini dikembangkan algoritma hibrid Extended Kalman Filter (EKF) dan Sistem Inferensi Fuzzy untuk mendapatkan hasil estimasi yang lebih akurat pada penjejakan target bermanuver. Logika Fuzzy telah digunakan untuk mengatur galat kovarian proses dan galat kovarian pengukuran dari proses EKF pada model sistem. Model state space yang digunakan untuk estimasi adalah model percepatan konstan, dan model pengukurannya adalah model radar. Hasil pengukuran sensor yang mengandung derau diestimasi menggunakan algoritma EKF. Kemudian galat kovarian yang dihasilkan dari proses EKF digunakan sebagai masukan pada Sistem Inferensi Fuzzy untuk koreksi berdasarkan ketidaksesuaian antara vektor inovasi dan kovarian inovasi. Hasil koreksi ini digunakan untuk mendapatkan gain Kalman yang optimal. Berdasarkan simulasi yang dilakukan menggunakan estimasi EKF dan Sistem Inferensi Fuzzy diperoleh peningkatan akurasi sebesar 59,97% dibandingkan dengan hasil pengukuran tanpa melakukan estimasi.In this paper the Extended Kalman Filter and the Fuzzy Inference System hybrid algorithm has developed to get more accurate estimation result for maneuvering target tracking. Fuzzy Logic has used to adjust the process covariance error and measurement covariance error of the Extended Kalman Filter process in the system model. The state space model used for estimation is a constant acceleration motion model, and the measurement model is a radar model. The measurement result of the sensor containing noise estimated using the Extended Kalman Filter (EKF) algorithm. Then, the covariance error resulting from the EKF process is used as input to the Fuzzy Inference System (FIS) for correction based on the mismatch between innovation vector and innovation covariance. The result of this correction used to obtain the optimal Kalman gain. The proposed system model leads to improved accuracy of 59.97% compared to measurement results without estimation in the simulation case.

Eye in hand robot arm based automated object grasping system

The modern robotic systems state that the tracking methodology and the visual servoing are imperative to discover the existence of an object and excite the robot in order to manipulate the target. This paper shows a new object tracking and grasping technique in real time based on Eye in Hand visual servoing structure via a camera mounted at the end of the robot arm. The working principle of the robotic system depends mainly on the prediction based on Kalman filter method that estimates the next location of a moving object in order to specify the path of the target under the scope of the camera. Hereby, the proposed system observes the object and studies its behavior based on the pervious state in order to grasp the target at the exact position. Furthermore, the vision system implements feedback control approach to keep the extracted information of the object updated to solve the stability and the reliability issues that might be encountered. It has to be mentioned that the proposed robotic system was tested by grasping moving objects in different speeds and directions. In addition, the grasping of a stationary object was tested to confirm the practical and the theoretical results. As a final result, it can be stated that the speed of the object is directly proportional with the grasping time and vice versa

Random Matrix Based Extended Target Tracking with Orientation: A New Model and Inference

In this study, we propose a novel extended target tracking algorithm which is capable of representing the extent of dynamic objects as an ellipsoid with a time-varying orientation angle. A diagonal positive semi-definite matrix is defined to model objects' extent within the random matrix framework where the diagonal elements have inverse-Gamma priors. The resulting measurement equation is non-linear in the state variables, and it is not possible to find a closed-form analytical expression for the true posterior because of the absence of conjugacy. We use the variational Bayes technique to perform approximate inference, where the Kullback-Leibler divergence between the true and the approximate posterior is minimized by performing fixed-point iterations. The update equations are easy to implement, and the algorithm can be used in real-time tracking applications. We illustrate the performance of the method in simulations and experiments with real data. The proposed method outperforms the state-of-the-art methods when compared with respect to accuracy and robustness.Comment: 12 pages, 6 figures, submitted to IEEE TS

Extended Object Tracking: Introduction, Overview and Applications

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure

Multi Sensor Multi Target Perception and Tracking for Informed Decisions in Public Road Scenarios

Multi-target tracking in public traffic calls for a tracking system with automated track initiation and termination facilities in a randomly evolving driving environment. Besides, the key problem of data association needs to be handled effectively considering the limitations in the computational resources on-board an autonomous car. The challenge of the tracking problem is further evident in the use of high-resolution automotive sensors which return multiple detections per object. Furthermore, it is customary to use multiple sensors that cover different and/or over-lapping Field of View and fuse sensor detections to provide robust and reliable tracking. As a consequence, in high-resolution multi-sensor settings, the data association uncertainty, and the corresponding tracking complexity increases pointing to a systematic approach to handle and process sensor detections. In this work, we present a multi-target tracking system that addresses target birth/initiation and death/termination processes with automatic track management features. These tracking functionalities can help facilitate perception during common events in public traffic as participants (suddenly) change lanes, navigate intersections, overtake and/or brake in emergencies, etc. Various tracking approaches including the ones based on joint integrated probability data association (JIPDA) filter, Linear Multi-target Integrated Probabilistic Data Association (LMIPDA) Filter, and their multi-detection variants are adapted to specifically include algorithms that handle track initiation and termination, clutter density estimation and track management. The utility of the filtering module is further elaborated by integrating it into a trajectory tracking problem based on model predictive control. To cope with tracking complexity in the case of multiple high-resolution sensors, we propose a hybrid scheme that combines the approaches of data clustering at the local sensor and multiple detections tracking schemes at the fusion layer. We implement a track-to-track fusion scheme that de-correlates local (sensor) tracks to avoid double counting and apply a measurement partitioning scheme to re-purpose the LMIPDA tracking algorithm to multi-detection cases. In addition to the measurement partitioning approach, a joint extent and kinematic state estimation scheme are integrated into the LMIPDA approach to facilitate perception and tracking of an individual as well as group targets as applied to multi-lane public traffic. We formulate the tracking problem as a two hierarchical layer. This arrangement enhances the multi-target tracking performance in situations including but not limited to target initialization(birth process), target occlusion, missed detections, unresolved measurement, target maneuver, etc. Also, target groups expose complex individual target interactions to help in situation assessment which is challenging to capture otherwise. The simulation studies are complemented by experimental studies performed on single and multiple (group) targets. Target detections are collected from a high-resolution radar at a frequency of 20Hz; whereas RTK-GPS data is made available as ground truth for one of the target vehicle\u27s trajectory

Predictive smart relaying schemes for decentralized wireless systems

Recent developments in decentralized wireless networks make the technology potentially deployable in an extremely broad scenarios and applications. These include mobile Internet of Things (IoT) networks, smart cities, future innovative communication systems with multiple aerial layer flying network platforms and other advanced mobile communication networks. The approach also could be the solution for traditional operated mobile network backup plans, balancing traffic flow, emergency communication systems and so on. This thesis reveals and addresses several issues and challenges in conventional wireless communication systems, particular for the cases where there is a lack of resources and the disconnection of radio links. There are two message routing plans in the data packet store, carry and forwarding form are proposed, known as KaFiR and PaFiR. These employ the Bayesian filtering approach to track and predict the motion of surrounding portable devices and determine the next layer among candidate nodes. The relaying strategies endow smart devices with the intelligent capability to optimize the message routing path and improve the overall network performance with respect to resilience, tolerance and scalability. The simulation and test results present that the KaFiR routing protocol performs well when network subscribers are less mobile and the relaying protocol can be deployed on a wide range of portable terminals as the algorithm is rather simple to operate. The PaFiR routing strategy takes advantages of the Particle Filter algorithm, which can cope with complex network scenarios and applications, particularly when unmanned aerial vehicles are involved as the assisted intermediate layers. When compared with other existing DTN routing protocols and some of the latest relaying plans, both relaying protocols deliver an excellent overall performance for the key wireless communication network evolution metrics, which shows the promising future for this brand new research direction. Further extension work directions based on the tracking and prediction methods are suggested and reviewed. Future work on some new applications and services are also addressed