• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.5
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• pp.703-710
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• 2014

An Image-Based Target Tracker maintains LOS(Line Of Sight) to a target by controlling azimuth and elevation gimbals of an ISS(Inertially Stabilized System). Its controller produces the gimbals commands of the ISS using tracking errors provided by an image tracker. The control performance of the target tracker with PI controller generally used for tracking controller is limited because of bandwidth limitation by time delay yielded by image capture and processing of the image tracker. In this paper, tracking controller using target states estimator is proposed which can enhance the tracking performance under the highly dynamic maneuvering conditions of the ISS and the target. Simulation results show that the proposed method can improve the tracking performance than that with only PI controller.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.1
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• pp.60-66
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• 2002

In this paper, a new target pointing guidance algorithm is proposed by combining the optimal target pointing solution and a simple time-to-Go estimator. Also investigated are some properties of the guidance algorithm which include a relation to conventional PNG, convergence region and convergence trajectories of error states according to the time-to-go estimator gain. Some guidelines for designing the pointing guidance law are commented based on the convergence properties. A design example in the case of large initial heading errors is presented and its performance is investigated by simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.181-184
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• 1999

Leading vehicle states are useful and essential elements in adaptive cruise control (ACC) system, collision warning (CW) and collision avoidance (CA) system, and automated highway system (AHS). There are many approaches in ACC using Kalman filter. Mostly only distance to leading vehicle and velocity difference are estimated and used for the above systems. Applications in road vehicle in curved road need to obtain more informations such as yaw angle, steering angle which can be estimated using vision system. Since vision system is not robust to environment change, we used Kalman filter to estimate distance, velocity, yaw angle, and steering angle. Application to active tracking of target vehicle is shown.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.182-196
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• 2023

Accurate and timely estimation of crop yields is crucial for various purposes, including global food security planning and agricultural policy development. Remote sensing techniques, particularly using vegetation indices (VIs), have show n promise in monitoring and predicting crop conditions. However, traditional VIs such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) have limitations in capturing rapid changes in vegetation photosynthesis and may not accurately represent crop productivity. An alternative vegetation index, the near-infrared reflectance of vegetation (NIRv), has been proposed as a better predictor of crop yield due to its strong correlation with gross primary productivity (GPP) and its ability to untangle confounding effects in canopies. In this study, we investigated the potential of NIRv in estimating crop yield, specifically for corn and soybean crops in major crop-producing regions in 14 states of the United States. Our results demonstrated a significant correlation between the peak value of NIRv and crop yield/area for both corn and soybean. The correlation w as slightly stronger for soybean than for corn. Moreover, most of the target states exhibited a notable relationship between NIRv peak and yield, with consistent slopes across different states. Furthermore, we observed a distinct pattern in the yearly data, where most values were closely clustered together. However, the year 2012 stood out as an outlier in several states, suggesting unique crop conditions during that period. Based on the established relationships between NIRv peak and yield, we predicted crop yield data for 2022 and evaluated the accuracy of the predictions using the Root Mean Square Percentage Error (RMSPE). Our findings indicate the potential of NIRv peak in estimating crop yield at the county level, with varying accuracy across different counties.