Browsing by Author "Zhang, Pei"
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- An Experimental Localization Testbed based on UWB Channel Impulse Response MeasurementsPublication . Li, Kai; Ni, Wei; Zhang, PeiIn this paper, we demonstrate a new ultra-wideband (UWB) localization testbed, which tracks a UWB tag and estimates locations of obstacles based on channel impulse response measurements. Anchor nodes that are developed with off-the-shelf Decawave DW1000 UWB transceivers are deployed to cover the area of interest. The testbed is implemented and preliminary experiments are carried out to estimate the location of the object by analyzing channel impulse response strength of the UWB tag.
- Fusion flow-enhanced graph pooling residual networks for unmanned aerial vehicles surveillance in day and night dual visionsPublication . Noor, Alam; Li, Kai; Tovar, Eduardo; Zhang, Pei; Wei, BoRecognizing unauthorized Unmanned Aerial Vehicles (UAVs) within designated no-fly zones throughout the day and night is of paramount importance, where the unauthorized UAVs pose a substantial threat to both civil and military aviation safety. However, recognizing UAVs day and night with dual-vision cameras is nontrivial, since red-green-blue (RGB) images suffer from a low detection rate under an insufficient light condition, such as on cloudy or stormy days, while black-and-white infrared (IR) images struggle to capture UAVs that overlap with the background at night. In this paper, we propose a new optical flow-assisted graph-pooling residual network (OF-GPRN), which significantly enhances the UAV detection rate in day and night dual visions. The proposed OF-GPRN develops a new optical fusion to remove superfluous backgrounds, which improves RGB/IR imaging clarity. Furthermore, OF-GPRN extends optical fusion by incorporating a graph residual split attention network and a feature pyramid, which refines the perception of UAVs, leading to a higher success rate in UAV detection. A comprehensive performance evaluation is conducted using a benchmark UAV catch dataset. The results indicate that the proposed OF-GPRN elevates the UAV mean average precision (mAP) detection rate to 87.8%, marking a 17.9% advancement compared to the residual graph neural network (ResGCN)-based approach.
- Poster Abstract: Multi-Drone Assisted Internet of Things Testbed Based on Bluetooth 5 CommunicationsPublication . Li, Kai; Lu, Ning; Zhang, Pei; Ni, Wei; Tovar, EduardoIn this paper, a multi-hop airborne system is built based on Bluetooth 5 connected autonomous drones to relay real-time data of Internet of Things (IoT). A new lightweight Onboard Bluetooth Transceiver (OBT) is developed for reliable drone-to-drone and drone-to-ground communications. A graphical user interface is presented to monitor real-time flight trajectory of the drones and end-to-end data delivery. Outdoor experiments are conducted in real world to test autonomous flight control of the drones and received signal strength of the OBT communications.