High-Altitude Platform Station (HAPS) provides communication services from an altitude of 20km via a stratospheric platform such as a balloon, solar-powered airship, or other aircraft, and is attracting much attention as a new mobile communication platform for ultra-wide coverage areas and disaster-resilient networks. HAPS can provide mobile communication services directly to the existing smartphones commonly used in terrestrial mobile communication networks such as Fourth Generation Long Term Evolution (4G LTE), and in the near future, Fifth Generation New Radio (5G NR). In order to design efficient HAPS-based cell configurations, we need a radio wave propagation model that takes into consideration factors such as terrain, vegetation, urban areas, suburban areas, and building entry loss. In this paper, we propose a new vegetation loss model for Recommendation ITU-R P.833-9 that can take transmission frequency and seasonal characteristics into consideration. It is based on measurements and analyses of the vegetation loss of deciduous trees in different seasons in Japan. Also, we carried out actual stratospheric measurements in the 700MHz band in Kenya to extend the lower frequency limit. Because the measured results show good agreement with the results predicted by the new vegetation loss model, the model is sufficiently valid in various areas including actual HAPS usage.

In this paper, the problem of lifetime extension of wireless sensor networks (WSNs) with redundant sensor nodes deployed in 3D vegetation-covered fields is modeled, which includes building communication models, network model and energy model. Generally, such a problem cannot be solved by a conventional method directly. Here we propose an Artificial Bee Colony (ABC) based optimal grouping algorithm (ABC-OG) to solve it. The main contribution of the algorithm is to find the optimal number of feasible subsets (FSs) of WSN and assign them to work in rotation. It is verified that reasonably grouping sensors into FSs can average the network energy consumption and prolong the lifetime of the network. In order to further verify the effectiveness of ABC-OG, two other algorithms are included for comparison. The experimental results show that the proposed ABC-OG algorithm provides better optimization performance.

We develop an effective algorithm, based on the filtered backprojection (FBP) approach, for the imaging of vegetation. Under the FBP scheme, the reconstruction amounts at a non-trivial Fourier inversion, since the data are Fourier samples arranged on a non-Cartesian grid. The computational issue is efficiently tackled by Non-Uniform Fast Fourier Transforms (NUFFTs), whose complexity grows asymptotically as that of a standard FFT. Furthermore, significant speed-ups, as compared to fast CPU implementations, are obtained by a parallel versions of the NUFFT algorithm, purposely designed to be run on Graphic Processing Units (GPUs) by using the CUDA language. The performance of the parallel algorithm has been assessed in comparison to a CPU-multicore accelerated, Matlab implementation of the same routine, to other CPU-multicore accelerated implementations based on standard FFT and employing linear, cubic, spline and sinc interpolations and to a different, parallel algorithm exploiting a parallel linear interpolation stage. The proposed approach has resulted the most computationally convenient. Furthermore, an indoor, polarimetric experimental setup is developed, capable to isolate and introduce, one at a time, different non-idealities of a real acquisition, as the sources (wind, rain) of temporal decorrelation. Experimental far-field polarimetric measurements on a thuja plicata (western redcedar) tree point out the performance of the set up algorithm, its robustness against data truncation and temporal decorrelation as well as the possibility of discriminating scatterers with different features within the investigated scene.

Synthetic aperture radar interferometry have been established in the past two decades, and used extensively for many applications including topographic mapping of terrain and surface deformation. Vegetation analysis is also a growing area of its application. In this paper, we propose an polarimetric SAR interferometry technique for interferometric phase extraction of each local scatterer. The estimated position of local scattering centers has an important information for effective tree height estimation of forest. The proposed method formulated for local scattering center extraction is based on the ESPRIT algorithm which is known for high-resolution capability of closely located incident waves. The method shows high-resolution performance when local scattered waves are uncorrelated and have different polarization characteristics. Using the method, the number of dominant local scattering centers and interferometric phases in each image pixel can be estimated directly. Validity of the algorithm is demonstrated by using examples derived from SIR-C data.