Accurate Tracking Information Research Articles

Using AI to predict calibration constants for the central drift chamber in GlueX at Jefferson Lab

The AI for Experimental Controls project team at Jefferson Lab has developed an AI system to control and calibrate a large drift chamber system in near-real time. The AI system will monitor environmental and experimental variables to recommend voltage settings that maintain consistent dE/dx gain and optimal resolution throughout the experiment. At present, calibrations are performed after data have been recorded and require a considerable amount of time and attention from experts. The calibrations currently require multiple iterations and depend on accurate tracking information. Our approach uses environmental data, such as atmospheric pressure and gas temperature, and beam conditions, such as the flux of incident particles, as inputs to a Gaussian Process Regression (GPR) model. For the data taken during the GlueX 2020 run period, the GPR is able to predict the existing gain correction factors to within 3.5%. This talk will briefly describe the development, testing, and future plans for this system at Jefferson Lab.

Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning

This paper investigates the problem of providing ultra-reliable and power-efficient virtual reality (VR) experiences for wireless mobile users. To ensure reliable ultra-high-definition (UHD) video frame delivery to mobile users and enhance their immersive visual experiences, a coordinated multipoint (CoMP) transmission technique and millimeter wave (mmWave) communications are exploited. Owing to user movement and time-varying wireless channels, the wireless VR experience enhancement problem is formulated as a sequence-dependent and mixed-integer problem with a goal of maximizing users’ feeling of presence (FoP) in the virtual world, subject to power consumption constraints on access points (APs) and users’ head-mounted displays (HMDs). The problem, however, is hard to be directly solved due to the lack of users’ accurate tracking information and the sequence-dependent and mixed-integer characteristics. To overcome this challenge, we develop a parallel echo state network (ESN) learning method to predict users’ tracking information by training fresh and historical tracking samples separately collected by APs. With the learnt results, we propose a deep reinforcement learning (DRL) based optimization algorithm to solve the formulated problem. In this algorithm, we implement deep neural networks (DNNs) as a scalable solution to produce integer decision variables and solve a continuous power control problem to criticize the integer decision variables. Finally, the performance of the proposed algorithm is compared with various benchmark algorithms, and the impact of different design parameters is also discussed. Simulation results demonstrate that the proposed algorithm is more 4.14% power-efficient than the benchmark algorithms.

Design and Implementation of a Vehicle Tracking Mechanism using Wireless Network Infrastructure

This study presents the design and implementation of a vehicle tracking system with the goal of assisting victims of road accidents in obtaining prompt assistance from the rescue team. This is accomplished by sending tracking data to the rescue team's mobile equipment. The system was developed utilising available electronics components, installed in a vehicle. Accident situations were recreated by dropping loads of different sizes from a height and loads linked to a rope to shatter the glass on the automobile in order to determine the minimal impart energy necessary to break the glass and the likelihood of a catastrophic accident. If an accident occurs, the GPS engine sends the coordinates of the crash site to the rescue team's mobile equipment through the SIM900 module's GSM engine over a GSM frequency for tracking. The system's tracking accuracy was determined using a standard GPS device, the GERMIN GPSMAP78s, to get the GPS coordinates of the scene and tracking them using the Google map API. When tracked using Google map, the tracking information obtained, when compared with the GARMIN GPSMAP78s, were exact, indicating that proposed device is capable of transmitting accurate tracking information in a short period of time, thereby saving lives.

An Approach to Target Reselection for Anti-Ship Missile Against Centroid Jamming with Accurate Tracking Information

By equaling the detection of centroid jamming to the “outlier” detection in the process of tracking with Kalman filter, an approach to target reselection for anti-ship missile against centroid jamming with accurate tracking information is proposed in this paper considering the mutation of the law of motion at the tracking point under centroid jamming. For this approach, an accurate target tracking model is built on the basis of extended Kalman filter (EKF). Using the information collected by radar seeker including distance, velocity and angle, it could achieve the accurate tracking of the target and determine the motion state of the target accurately. On this basis, the orthogonality of innovation in the process of Kalman filtering is utilized to detect any mutation of the motion state of the target, so that the existence of centroid jamming is detected when there is any mutation of the law of motion at the tracking point in the process of chaff centroid jamming. This offers a new solution for remote beyond-visual-range (BVR) anti-ship missile against centroid jamming.

A Diffusion of Innovations Approach to Investigate the RFID Adoption in Taiwan Logistics Industry

Logistics tasks heavily depend on reliable shipment and accurate tracking information. For this reason, logistics today have evolved into a high-technology industry. Distribution is no longer simply about moving cargo on the road or via air from location A to B, but is a complex process based on intelligent system for sorting, planning, routing, and consolidation that supports faster transportation. [1] The purpose of this study was to investigate how RFID technology was implemented and adopted in Taiwan’s logistics industry. Specifically, this study focused on the positive influence of using RFID technology on the industry and the strategical benefits the RFID system had provided to companies, which had accepted and utilized this technology. This study also aimed to determine the concern factors of adopting the RFID system into current company management systems. An integral part of this research was to develop and to empirically test a model of the adoption of RFID in the context of the logistics industry in Taiwan. Based on the concepts of Rogers [2]-- the theory of technology diffusion, this research used a questionnaire to assess Taiwan logistics companies' cognition and perspective of the relative advantage, compatibility, complexity, trialability and observability of the RFID system; as well as to assess their attitudes toward the RFID system and intentions of using the system. Research findings revealed the attributes of innovations mentioned above were significantly positively associated with the adoption of RFID. According to the research results, managerial implications and opportunities for future research were discussed in the final.

3D Walking Human Detection and Tracking based on the IMPRESARIO Framework

In this paper, we propose a real-time people tracking system with multiple CCD cameras for security inside the building. The camera is mounted from the ceiling of the laboratory so that the image data of the passing people are fully overlapped. The implemented system recognizes people movement along various directions. To track people even when their images are partially overlapped, the proposed system estimates and tracks a bounding box enclosing each person in the tracking region. The approximated convex hull of each individual in the tracking area is obtained to provide more accurate tracking information. To achieve this goal, we propose a method for 3D walking human tracking based on the IMPRESARIO framework incorporating cascaded classifiers into hypothesis evaluation. The efficiency of adaptive selection of cascaded classifiers have been also presented. We have shown the improvement of reliability for likelihood calculation by using cascaded classifiers. Experimental results show that the proposed method can smoothly and effectively detect and track walking humans through environments such as dense forests.

Performance of dead reckoning‐based location service for mobile ad hoc networks

AbstractA predictive model‐based mobility tracking method, called dead reckoning, is developed for mobile ad hoc networks. It disseminates both location and movement models of mobile nodes in the network so that every node is able to predict or track the movement of every other node with a very low overhead. The basic technique is optimized to use ‘distance effect’, where distant nodes maintain less accurate tracking information to save overheads. The dead reckoning‐based location service mechanism is evaluated against three known location dissemination service protocols: simple, distance routing effect algorithm for mobility (DREAM) and geographic region summary service (GRSS). The evaluation is done with geographic routing as an application. It is observed that dead reckoning significantly outperforms the other protocols in terms of packet delivery fraction. It also maintains low‐control overhead. Its packet delivery performance is only marginally impacted by increasing speed or noise in the mobility model, that affects its predictive ability. Copyright © 2004 John Wiley & Sons, Ltd.

The Storm Cell Identification and Tracking Algorithm: An Enhanced WSR-88D Algorithm

Accurate storm identification and tracking are basic and essential parts of radar and severe weather warning operations in today’s operational meteorological community. Improvements over the original WSR-88D storm series algorithm have been made with the Storm Cell Identification and Tracking algorithm (SCIT). This paper discusses the SCIT algorithm, a centroid tracking algorithm with improved methods of identifying storms (both isolated and clustered or line storms). In an analysis of 6561 storm cells, the SCIT algorithm correctly identified 68% of all cells with maximum reflectivities over 40 dB Z and 96% of all cells with maximum reflectivities of 50 dBZ or greater. The WSR-88D storm series algorithm performed at 24% and 41%, respectively, for the same dataset. With better identification performance, the potential exists for better and more accurate tracking information. The SCIT algorithm tracked greater than 90% of all storm cells correctly. The algorithm techniques and results of a detailed performance evaluation are presented. This algorithm was included in the WSR-88D Build 9.0 of the Radar Products Generator software during late 1996 and early 1997. It is hoped that this paper will give new users of the algorithm sufficient background information to use the algorithm with confidence.