Theses and Dissertations from UMD
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Item SUB-NYQUIST SENSING AND SPARSE RECOVERY OF WIDE-BAND INTENSITY MODULATED OPTICAL SIGNALS(2018) Lee, Robert; Davis, Christopher; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Intensity modulated optical transmitters, wide-bandwidth electro-optical receivers, high-speed digitizers, and digital matched-filters are being used in hybrid lidar-radar systems to measure the range and reflectivity of objects located within degraded visual underwater environments. These methods have been shown to mitigate the adverse effects of the turbid underwater channel due to the de-correlation of the modulated optical signal after undergoing multiple scattering events. The observed frequency-dependent nature of the underwater channel has driven the desire for wider bandwidth waveforms modulated at higher frequencies in order to improve range accuracy and resolution. While the described system has shown promise, the matched filter processing scheme, which is also widely used in the fields of radar and sonar, suffers from inherent limitations. One limitation is based on the achievable range resolution as dictated by the classical time-frequency uncertainty principle, where the bandwidth dictates the measurable resolution. The side-lobes generated during the matched filtering process also present a challenge when trying to detect multiple targets. These limitations are further constrained by currently-available analog-to-digital conversion technologies which restrict the ability to directly sample the wide-band modulated signals. Even in cases where the technology exists that can operate at sufficient rates, often it is prohibitively expensive for many applications and high data rates can pose processing challenges. This research effort addresses both the restrictions imposed by the available analog-to-digital conversion technologies and the limited resolution of the existing time-frequency methods for wide-band signal processing. The approach is based on concepts found within the fields of compressive sensing and sparse signal recovery and will be applied to the detection of objects illuminated with wide-band intensity modulated optical signals. The underlying assumption is that given the directive nature of laser propagation, the illuminated scene is inherently sparse and the limited number of reflecting objects can be treated as point sources. The main objective of this research is to provide results that show, when sampling at rates below those dictated by the traditional Shannon-Nyquist sampling theorem, it is possible to make more efficient use of the samples collected and detect a limited number of reflecting targets using specialized recovery algorithms without reducing system resolution. Through theoretical derivations, empirical simulations, and experimental investigation, it will be shown under what conditions the sub-Nyquist sampling and sparse recovery techniques are applicable, and how the described methods influence resolution, accuracy, and overall performance in the presence of noise.Item MAPPING FOREST STRUCTURE AND HABITAT CHARACTERISTICS USING LIDAR AND MULTI-SENSOR FUSION(2011) Swatantran, Anuradha; Dubayah, Ralph; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation explored the combined use of lidar and other remote sensing data for improved forest structure and habitat mapping. The objectives were to quantify aboveground biomass and canopy dynamics and map habitat characteristics with lidar and /or fusion approaches. Structural metrics from lidar and spectral characteristics from hyperspectral data were combined for improving biomass estimates in the Sierra Nevada, California. Addition of hyperspectral metrics only marginally improved biomass estimates from lidar, however, predictions from lidar after species stratification of field data improved by 12%. Spatial predictions from lidar after species stratification of hyperspectral data also had lower errors suggesting this could be viable method for mapping biomass at landscape level. A combined analysis of the two datasets further showed that fusion could have considerably more value in understanding ecosystem and habitat characteristics. The second objective was to quantify canopy height and biomass changes in in the Sierra Nevada using lidar data acquired in 1999 and 2008. Direct change detection showed overall statistically significant positive height change at footprint level (ΔRH100 = 0.69 m, +/- 7.94 m). Across the landscape, ~20 % of height and biomass changes were significant with more than 60% being positive, suggesting regeneration from past disturbances and a small net carbon sink. This study added further evidence to the capabilities of waveform lidar in mapping canopy dynamics while highlighting the need for error analysis and rigorous field validation Lastly, fusion applications for habitat mapping were tested with radar, lidar and multispectral data in the Hubbard Brook Experimental Forest, New Hampshire. A suite of metrics from each dataset was used to predict multi-year presence for eight migratory songbirds with data mining methods. Results showed that fusion improved predictions for all datasets, with more than 25% improvement from radar alone. Spatial predictions from fusion were also consistent with known habitat preferences for the birds demonstrating the potential of multi- sensor fusion in mapping habitat characteristics. The main contribution of this research was an improved understanding of lidar and multi-sensor fusion approaches for applications in carbon science and habitat studies.