Study on blimp-based low-cost remote sensing platform

2008 ◽  
Author(s):  
Wuming Zhang ◽  
Xinping Guo ◽  
Guoqing Zhou ◽  
Guangjian Yan
2020 ◽  
Vol 10 (11) ◽  
pp. 3730 ◽  
Author(s):  
Josep M. Maso ◽  
Jordi Male ◽  
Joaquim Porte ◽  
Joan L. Pijoan ◽  
David Badia

Every year more interest is focused on high frequencies (HF) communications for remote sensing platforms due to their capacity to establish links of more than 250 km without a line of sight and due to them being a low-cost alternative to satellite communications. In this article, we study the ionospheric ordinary and extraordinary waves to improve the applications of near vertical incidence skywave (NVIS) on a single input multiple output (SIMO) configuration. To obtain the results, we established a link of 95 km to test the diversity combining of ordinary and extraordinary waves by using selection combining (SC) and equal-gain combining (EGC) on a remote sensing platform. The testbench is based on digital modulation transmissions with power transmission between 3 and 100 W. The results show us the main energy per bit to noise spectral density ratio (Eb/N0) and the bit error rate (BER) differences between ordinary and extraordinary waves, SC, and EGC. To conclude, diversity techniques show us a decrease of the power transmission need, allowing for the use of compact antennas and increasing battery autonomy. Furthermore, we present three different improvement options for NVIS SIMO remote sensing platforms depending on the requirements of bitrate, power consumption, and efficiency of communication.


Author(s):  
Calvin Coopmans ◽  
Long Di ◽  
Austin Jensen ◽  
Aaron A. Dennis ◽  
YangQuan Chen

Remote sensing is a field traditionally dominated by expensive, large-scale operations. This paper presents our efforts to improve our unmanned aircraft (UA) platforms for low-cost personal remote sensing purposes. Safety concerns are first emphasized regarding the local airspace and multiple fail-safe features are shown in the current system. Then the AggieAir unmanned system architecture is briefly described including the Paparazzi UA autopilot, AggieAir JAUS implementation, AggieNav navigation unit and payload integration. Some preliminary flight test results and images acquired using an example thermal IR payload system are also shown. Finally Multi-UAV and heterogeneous platform capabilities are discussed with respect to their applications. Based on our approaches on the new architecture design, personal remote sensing on smaller-scale operations can be more beneficial and common.


Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4163 ◽  
Author(s):  
Chisheng Wang ◽  
Junzhuo Ke ◽  
Wenqun Xiu ◽  
Kai Ye ◽  
Qingquan Li

Current satellite remote sensing data still have some inevitable defects, such as a low observing frequency, high cost and dense cloud cover, which limit the rapid response to ground changes and many potential applications. However, passenger aircraft may be an alternative remote sensing platform in emergency response due to the high revisit rate, dense coverage and low cost. This paper introduces a volunteered passenger aircraft remote sensing method (VPARS) for emergency response. It uses the images captured by the passenger volunteers during flight. Based on computer vision algorithms and geocoding procedures, these images can be processed into a mosaic orthoimage for rapid ground disaster mapping. Notable, due to the relatively low flight latitude, small clouds can be easily removed by stacking multi-angle tilt images in the VPARS method. A case study on the 2019 Guangdong flood monitoring validates these advantages. The frequent aircraft revisit time, intensive flight coverage, multi-angle images and low cost of the VPARS make it a potential way to complement traditional remote sensing methods in emergency response.


2011 ◽  
Vol 79 (12) ◽  
pp. 1240-1245 ◽  
Author(s):  
Joel F. Campbell ◽  
Michael A. Flood ◽  
Narasimha S. Prasad ◽  
Wade D. Hodson

2017 ◽  
Vol 11 (1) ◽  
pp. 016006 ◽  
Author(s):  
David D. W. Ren ◽  
Siddhant Tripathi ◽  
Larry K. B. Li

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