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This technique can detect multiple drones in static and quasi-static mixing scenarios, while failing in time-varying scenarios. After extracting the features, the support vector machines (SVM) and the k-nearest neighbors (KNN) are used to identify multiple drones in the field.
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In this paper, a method is proposed in which the mixed signals are first separated taking the ICA technique into account. In previous research, we presented an acoustic signals-based multiple drones detection technique utilizing independent component analysis (ICA) in the presence of interfering sources. Currently, detection methods based on thermal, video, radio frequency (RF) and acoustic signals exist. Because this method does not require prior information or additional sensor implementation for high-positioning performance in deep urban areas, it is expected to gain wide usage in not only the automotive industry but also future intelligent transportation systems.ĭetection of unauthorized drones is mandatory for defense organizations and also for human life protection. The availability is improved from 64% to 100% and the error RMS is reduced from 11.1 m to 1.2 m on Teheran-ro, Seoul, Korea. It also presents a mode-switching algorithm between the DGNSS and multipath mitigating mode and shows that this technique can be effectively utilized for automobiles in a deep urban environment without any help from sensors other than GNSS. This paper introduces a real-time multipath estimation and mitigation technique, which considers compensation for the time offset between constellations. Despite the wide usage of the GNSS in populated urban areas, it is difficult to suggest a generalized method because multipath errors are user-specific errors that cannot be eliminated by the DGNSS or a real-time kinematic technique. Multipath and non-line-of-sight (NLOS) signals are the major causes of poor accuracy of a global navigation satellite system (GNSS) in urban areas. Our proposed system is expected to be a useful and practical solution to integrate drones into the airspace in the near future. The proposed system can practically solve the drawbacks of the current SBAS, considering the characteristics of the low-cost receivers on the market. The protection levels calculated with the accurate position regardless of the current location could denote the thrust level and availability of the navigation solution. In SBAS signal-denied cases, the position accuracy was improved by 40% and the uncorrected 13.4 m vertical error was reduced to 5.6 m by applying an SBAS message delivered online. The proposed system not only improves the position accuracy with timely and proper protection levels in an open sky, but also reduces the initialization time from 70–100 s to 1 s, enabling a drone of short endurance to perform its mission successfully. This study proposes an onboard module including correction conversion, integrity information calculation, and fast initialization requests, which can enable the application of an online SBAS to drone operation. Owing to the high demand for drone operation in high-elevation masked areas, it is necessary to develop a more effective method of transmitting and applying Satellite-Based Augmentation System (SBAS) messages for drones.