Maximum Likelihood Estimation of Exponentials in Unknown Colored Noise for Target Identification in Synthetic Aperture Radar Images

This dissertation develops techniques for estimating exponential signals in unknown colored noise. The Maximum Likelihood (ML) estimators of the exponential parameters are developed. Techniques are developed for one and two dimensional exponentials, for both the deterministic and stochastic ML model. The techniques are applied to Synthetic Aperture Radar (SAR) data whose point scatterers are modeled as damped exponentials. These estimated scatterer locations (exponentials frequencies) are potential features for model-based target recognition. The estimators developed in this dissertation may be applied with any parametrically modeled noise having a zero mean and a consistent estimator of the noise covariance matrix. ML techniques are developed for a single instance of data in colored noise which is modeled in one dimension as (1) stationary noise, (2) autoregressive (AR) noise and (3) autoregressive moving-average (ARMA) noise and in two dimensions as (1) stationary noise, and (2) white noise driving an exponential filter. The classical ML approach is used to solve for parameters which can be decoupled from the estimation problem. The remaining nonlinear optimization to find the exponential frequencies is then solved by extending white noise ML techniques to colored noise. In the case of deterministic ML, the computationally efficient, one and two-dimensional Iterative Quadratic Maximum Likelihood (IQML) methods are extended to colored noise. In the case of stochastic ML, the one and two-dimensional Method of Direction Estimation (MODE) techniques are extended to colored noise. Simulations show that the techniques perform close to the Cramer-Rao bound when the model matches the observed noise

Maximum Likelihood Estimation of Exponentials in Unknown Colored Noise for Target in Identification Synthetic Aperture Radar Images

This dissertation develops techniques for estimating exponential signals in unknown colored noise. The Maximum Likelihood ML estimators of the exponential parameters are developed. Techniques are developed for one and two dimensional exponentials, for both the deterministic and stochastic ML model. The techniques are applied to Synthetic Aperture Radar SAR data whose point scatterers are modeled as damped exponentials. These estimated scatterer locations exponentials frequencies are potential features for model-based target recognition. The estimators developed in this dissertation may be applied with any parametrically modeled noise having a zero mean and a consistent estimator of the noise covariance matrix. ML techniques are developed for a single instance of data in colored noise which is modeled in one dimension as 1 stationary noise, 2 autoregressive AR noise and 3 autoregressive moving-average ARMA noise and in two dimensions as 1 stationary noise, and 2 white noise driving an exponential filter. The classical ML approach is used to solve for parameters which can be decoupled from the estimation problem. The remaining nonlinear optimization to find the exponential frequencies is then solved by extending white noise ML techniques to colored noise. In the case of deterministic ML, the computationally efficient, one and two-dimensional Iterative Quadratic Maximum Likelihood IQML methods are extended to colored noise. In the case of stochastic ML, the one and two-dimensional Method of Direction Estimation MODE techniques are extended to colored noise. Simulations show that the techniques perform close to the Cramer-Rao bound when the model matches the observed noise

Proceedings of the MECA Workshop on The Evoluation of the Martian Atmosphere

Topics addressed include: Mars' volatile budget; climatic implications of martian channels; bulk composition of Mars; accreted water inventory; evolution of CO2; dust storms; nonlinear frost albedo feedback on Mars; martian atmospheric evolution; effects of asteroidal and cometary impacts; and water exchange between the regolith and the atmosphere/cap system over obliquity timescales

SAM-2 ground-truth plan: Correlative measurements for the Stratospheric Aerosol Measurement-2 (SAM 2) sensor on the Nimbus G satellite

The SAM-2 will fly aboard the Nimbus-G satellite for launch in the fall of 1978 and measure stratospheric vertical profiles of aerosol extinction in high latitude bands. The plan gives details of the location and times for the simultaneous satellite/correlative measurements for the nominal launch time, the rationale and choice of the correlative sensors, their characteristics and expected accuracies, and the conversion of their data to extinction profiles. The SAM-2 expected instrument performance and data inversion results are presented. Various atmospheric models representative of polar stratospheric aerosols are used in the SAM-2 and correlative sensor analyses

Optimal bait density for delivery of acute toxicants to vertebrate pests

Oral baiting is a fundamental method for delivering toxicants to pest species. Planning baiting strategies is challenging because bait-consumption rates depend on dynamic processes including space use and demographics of the target species. To determine cost-effective strategies for optimizing baiting, we developed a spatially explicit model of population dynamics using field-based measures of wild-pig (Sus scrofa) space use, bait consumption, and mortality probabilities. The most cost-effective baiting strategy depended strongly on the population reduction objective and initial density. A wide range of baiting strategies were cost-effective when the objective was 80% population reduction. In contrast, only a narrow range of baiting strategies allowed for a 99% reduction. Cost-effectiveness was lower for low densities of wild pigs because of the increased effort for locating target animals. Bait avoidance due to aversive conditioning from sub-lethal dosing had only minor effects on cost-effectiveness when the objective was an 80% reduction, whereas the effect was much stronger when the objective was 99% population reduction. Our results showed that a bait-based toxicant could be cost-effective for substantially reducing populations of wild pigs, but for elimination it may be most cost-effective to integrate additional management techniques following initial toxicant deployment. The nonlinear interaction of cost-effectiveness, initial population size, and reduction objective also emphasized the importance of considering the dynamics of space use and bait consumption for predicting effective baiting strategies. Although we used data for an acute toxicant and wild-pig consumption rates, our framework can be readily adapted to other vertebrate pest species and toxicant characteristics

Development of Self Regulated Learning Model in Studying Nursing (Srlsn) to Improve Student Learning Competence

Introduction: New students at the college have to adjust to the learning process in a way more independent, not dependent on the lecturer, and self-regulation in learning. The purpose of this study is to develop a model of competence SRLSN to increased achievement among undergraduate students in the fourth semester of nursing STIKES Pemkab Jombang. Methods: The design used is explanatory and quasi-experimental pre-post test with control group. The population in this study were 71 nursing students of 4th semester of the academic year 2012–2013. The sample used 60 students with simple random sampling. Data was collected using focus group discussions, observation and questionnaires, then analyzed using regression results. Results: The results showed that the correlation between SRLSN preparation phase and implementation phase of 0.976, the correlation between the phase and the implementation phase has a self-reflection of 0.374, the relationship between the phase of preparation and reflection phase of 0.576. There are significant differences between treatment and control groups on aspects of cognitive competence achievement, competence affective, and psychomotor competencies. Discussion: SRLSN models are systematically formed by the preparation, implementation and reflection phase. The application of the model SRLSN will enhance student learning in the cognitive, affective, and psychomotor in achieving competence. Psychomotor competency has a value greater signi fi cance than other competencies. SRLSN models should be generalized to all learning processes, especially in nursing students