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Ke W.,Key Laboratory of Disaster Reduction | Ke W.,Nanjing Normal University | Wu L.,Nanjing Southeast University
Progress In Electromagnetics Research B | Year: 2012

In the received signal strength (RSS) based indoor wireless localization system, RSS measurements are very susceptible to the complex structures and dynamic nature of indoor environments, which will result in the system failure to achieve a high location accuracy. In this paper, we investigate the indoor positioning problem in the existence of RSS variations without prior knowledge about the localization area and without time-consuming off-line surveys. An adaptive sparsity-based localization algorithm is proposed to mitigate the effects of RSS variations. The novel feature of this method is to adjust both the overcomplete basis (a.k.a. dictionary) and the sparse solution using a dictionary learning (DL) technology based on the quadratic programming approach so that the location solution can better match the actual RSS scenario. Moreover, we extend this algorithm to deal with the problem of positioning targets from multiple categories, a novel problem that few works have ever concerned before. Simulation results demonstrate the superiority of the proposed algorithm over some state-of-art environmental-adaptive indoor localization methods. Source


Chen W.,Wuhan University | Chen W.,Key Laboratory of Disaster Reduction | Li X.,Wuhan University | Wang Y.,State Key Laboratory of Biogeology and Environmental Geology | And 2 more authors.
Environmental Earth Sciences | Year: 2013

The objective of this study is to map landslide susceptibility in Zigui segment of the Yangtze Three Gorges area that is known as one of the most landslide-prone areas in China by using data from light detection and ranging (LiDAR) and digital mapping camera (DMC). The likelihood ratio (LR) and logistic regression model (LRM) were used in this study. The work is divided into three phases. The first phase consists of data processing and analysis. In this phase, LiDAR and DMC data and geological maps were processed, and the landslide-controlling factors were derived such as landslide density, digital elevation model (DEM), slope angle, aspect, lithology, land use and distance from drainage. Among these, the landslide inventories, land use and drainage were constructed with both LiDAR and DMC data; DEM, slope angle and aspect were constructed with LiDAR data; lithology was taken from the 1:250,000 scale geological maps. The second phase is the logistic regression analysis. In this phase, the LR was applied to find the correlation between the landslide locations and the landslide-controlling factors, whereas the LRM was used to predict the occurrence of landslides based on six factors. To calculate the coefficients of LRM, 13,290,553 pixels was used, 29.5 % of the total pixels. The logical regression coefficients of landslide-controlling factors were obtained by logical regression analysis with SPSS 17.0 software. The accuracy of the LRM was 88.8 % on the whole. The third phase is landslide susceptibility mapping and verification. The mapping result was verified using the landslide location data, and 64.4 % landslide pixels distributed in "extremely high" zone and "high" zone; in addition, verification was performed using a success rate curve. The verification result show clearly that landslide susceptibility zones were in close agreement with actual landslide areas in the field. It is also shown that the factors that were applied in this study are appropriate; lithology, elevation and distance from drainage are primary factors for the landslide susceptibility mapping in the area, while slope angle, aspect and land use are secondary. © 2012 Springer-Verlag Berlin Heidelberg. Source


Lin Y.,Key Laboratory of Disaster Reduction | Lin Y.,National Key Laboratory of Science and Technology on Reactor System Design Technology | Lin Y.,CAS Institute of Electronics | Xiang Y.,National Key Laboratory of Science and Technology on Reactor System Design Technology | And 9 more authors.
Proceedings of 2011 IEEE CIE International Conference on Radar, RADAR 2011 | Year: 2011

Imaging algorithm based on compressed sensing (CS) is studied for multiple inputs and multiple outputs (MIMO) SAR using orthogonal code waveforms. It can reduce the sampling rate and improve reconstruction results compared with traditional SAR algorithms. Through modelling all the echoes of orthogonal signals, it can also reduce the range ambiguity caused be nonideal orthogonality of multiple transmitted signals. © 2011 IEEE. Source


Lin Y.,Key Laboratory of Disaster Reduction | Fan Y.,Key Laboratory of Disaster Reduction | Jiang H.,CAS National Astronomical Observatories | Wang W.,Key Laboratory of Disaster Reduction
International Journal of Antennas and Propagation | Year: 2014

The high-resolution wide-swath (HRWS) SAR system uses a small antenna for transmitting waveform and multiple antennas both in elevation and azimuth for receiving echoes. It has the potential to achieve wide spatial coverage and fine azimuth resolution, while it suffers from elevation pattern loss caused by the presence of topographic height and impaired azimuth resolution caused by nonuniform sampling. A new approach for HRWS SAR imaging based on compressed sensing (CS) is introduced. The data after range compression of multiple elevation apertures are used to estimate direction of arrival (DOA) of targets via CS, and the adaptive digital beamforming in elevation is achieved accordingly, which avoids the pattern loss of scan-on-receive (SCORE) algorithm when topographic height exists. The effective phase centers of the system are nonuniformly distributed when displaced phase center antenna (DPCA) technology is adopted, which causes Doppler ambiguities under traditional SAR imaging algorithms. Azimuth reconstruction based on CS can resolve this problem via precisely modeling the nonuniform sampling. Validation with simulations and experiment in an anechoic chamber are presented. © 2014 Yueguan Lin et al. Source

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