• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.328-333
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• 2012

Current differential GPS (DGPS) system consists of reference station (RS), integrity monitor (IM), and control station (CS). The RS computes the pseudorange corrections (PRC) and generates the RTCM messages for broadcasting. The IM receives the corrections from the RS broadcasting and verifies that the information is within tolerance. The CS performs realtime system status monitoring and control of the functional and performance parameters. The primary function of a DGPS integrity monitor is to verify the correction information and transmit feedback messages to the reference station. However, the current algorithms for integrity monitoring have the limitations of integrity monitor functions for satellite outage or anomalies. Therefore, this paper focuses on the detection and identification methods of satellite anomalies for maritime DGPS RSIM. Based on the function analysis of current DGPS RSIM, it first addresses the limitation of integrity monitoring functions for DGPS RSIM, and then proposes the detection and identification method of satellite anomalies. In addition, it simulates an actual GPS clock anomaly case using a GPS simulator to analyze the limitations of the integrity monitoring function. It presents the brief test results using the proposed methods for detection and identification of satellite anomalies.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.6
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• pp.505-510
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• 2013

In order to detect and identify the GPS clock anomaly in the Differential GPS real environment, this paper addresses a method for minimizing the measurement noise of reference receivers. It estimates the real measurement noise that removed the uncommon error source from pseudorange measurement to minimize the measurement noise. Based on the output of two reference receivers, it first removes the uncommon errors, then optimizes the measurement noise by applying the correction data. Finally, it detects and identifies the satellite clock anomaly using the minimized measurement noise. The method will increase the availability of current DGPS reference system.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.