Browsing by Subject "Maximum likelihood"
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Item Open Access Land Use Land Cover in the Western Ghats, India(2013-04-26) Gerlach, Paul; Hubbard, Malissa; Norment, ElizabethIn India’s Western Ghats mountain range, a UNESCO World Heritage Site and Conservation International biodiversity hotspot, human-caused habitat loss threatens many native species. A number of protected areas have been created to provide a refuge for these species and prevent further habitat loss. However, encroaching development continues to threaten these delicate ecosystems. Despite the area’s environmental value, there is no reliable, high-resolution land use land cover (LULC) map that would allow managers to estimate the extent and distribution of development as well as habitat condition and connectivity across the region. Using ASTER imagery, we conducted LULC classifications of 6 protected areas and their surroundings (20 km buffers). Separate classifications were conducted on Anshi-Dandeli National Park, Nagarahole and Bandipur National Parks, BRT Wildlife Sanctuary, and Kudremukh and Bhadra Wildlife Sanctuaries, for a total of four classification regions. We conducted both supervised maximum likelihood and unsupervised ISODATA classifications. Accuracy of the supervised classifications was higher than accuracy of the unsupervised classifications, with values ranging from 75.6-84.4%. Forest class accuracy ranged from 74% - 91%. We used the LULC classifications to assess the amount of forest cover within the protected areas and in the 20 km surrounding buffer. Within the classifications, 45-67% of the land is forested, while 17-35% of the land has been cleared for human use. We also conducted pilot analyses of forest fragmentation, patch connectivity, and human-affected areas in different parks. The LULC maps will be used to help managers set conservation goals and establish land use baselines for the region.Item Open Access Localization of Dynamic Acoustic Sources with a Maneuverable Array(2010) Rogers, Jeffrey SThis thesis addresses the problem of source localization and time-varying spatial spectrum estimation with maneuverable arrays. Two applications, each having different environmental assumptions and array geometries, are considered: 1) passive broadband source localization with a rigid 2-sensor array in a shallow water, multipath environment and 2) time-varying spatial spectrum estimation with a large, flexible towed array. Although both applications differ, the processing scheme associated with each is designed to exploit array maneuverability for improved localization and detection performance.
In the first application considered, passive broadband source localization is accomplished via time delay estimation (TDE). Conventional TDE methods, such as the generalized cross-correlation (GCC) method, make the assumption of a direct-path signal model and thus suffer localization performance loss in shallow water, multipath environments. Correlated multipath returns can result in spurious peaks in GCC outputs resulting in large bearing estimate errors. A new algorithm that exploits array maneuverability is presented here. The multiple orientation geometric averaging (MOGA) technique geometrically averages cross-correlation outputs to obtain a multipath-robust TDE. A broadband multipath simulation is presented and results indicate that the MOGA effectively suppresses correlated multipath returns in the TDE.
The second application addresses the problem of field directionality mapping (FDM) or spatial spectrum estimation in dynamic environments with a maneuverable towed acoustic array. Array processing algorithms for towed arrays are typically designed assuming the array is straight, and are thus degraded during tow ship maneuvers. In this thesis, maneuvering the array is treated as a feature allowing for left and right disambiguation as well as improved resolution towards endfire. The Cramer Rao lower bound is used to motivate the improvement in source localization which can be theoretically achieved by exploiting array maneuverability. Two methods for estimating time-varying field directionality with a maneuvering array are presented: 1) maximum likelihood estimation solved using the expectation maximization (EM) algorithm and 2) a non-negative least squares (NNLS) approach. The NNLS method is designed to compute the field directionality from beamformed power outputs, while the ML algorithm uses raw sensor data. A multi-source simulation is used to illustrate both the proposed algorithms' ability to suppress ambiguous towed-array backlobes and resolve closely spaced interferers near endfire which pose challenges for conventional beamforming approaches especially during array maneuvers. Receiver operating characteristics (ROCs) are presented to evaluate the algorithms' detection performance versus SNR. Results indicate that both FDM algorithms offer the potential to provide superior detection performance in the presence of noise and interfering backlobes when compared to conventional beamforming with a maneuverable array.
Item Open Access Momentum Scale Estimation Using Maximum LikelihoodTemplate Fitting(2010) Zeng, YuA maximum likelihood template fitting procedure is performed by using Upsilon --> mu+mu- events to extract the momentum scale, a scale factor applied to measured momentum, of the CDF detector at Fermilab. The constructed invariant mass spectrum from data events is compared with the invariant mass spectrum from Monte Carlo simulated events, with the momentum scale varying as a free parameter in the simulation. The invariant mass spectrum from simulation which best matches the data spectrum gives the maximum likelihood estimation of the momentum scale. We find the momentum scale is dp/p = (-1.330 ± 0.028(stat) ± 0.099(syst)) × 10^{-3}.