A simulation of the single scan accuracy of a two-dimensional pulsed surveillance radar

Bibliography: leaves 194-198.The following dissertation considers the single-scan two-dimensional positional accuracy of a pulsed surveillance radar. The theoretical aspects to the positional accuracy are considered and a generalized analytical approach is presented. Practical position estimators are often complex, and theoretical predictions of their performance generally yield unfriendly mathematical equations. In order to evaluate the performance of these estimators, a simulation method is described based on replicating the received video signal. The accuracy of such a simulation is determined largely by the accuracy of the models applied, and these are considered in detail. Different azimuth estimation techniques are described, and their performances are evaluated with the aid of the signal simulation. The best azimuth accuracy performance is obtained with the class of analogue processing estimators, but they are found to be more susceptible to interference than their binary processing counterparts. The class of binary processing estimators offer easily implemented techniques which are relatively insensitive to radar cross-section scintillation characteristics. A hybrid estimator, using both analogue and binary processing, is also evaluated and found to give an improved accuracy performance over the binary processing method while still maintaining the relative insensitivity to radar cross-section fluctuation

Wavefront Curvature Sensing from Image Projections

This research outlines the development and simulation of a signal processing approach to real time wavefront curvature sensing in adaptive optics. The signal processing approach combines vectorized Charge Coupled Device (CCD) read out with a wavefront modal estimation technique. The wavefront sensing algorithm analyzes vector projections of image intensity data to provide an estimate of the wavefront phase as a combination of several low order Zernike polynomial modes. This wavefront sensor design expands on an existing idea for vector based tilt sensing by providing the ability to compensate for additional modes. Under the proposed wavefront sensing approach, the physical wavefront sensor would be replaced by a pair of imaging devices capable of generating vector projections of the image data. Using image projections versus two-dimensional image data allows for faster CCD read out and decreased read noise

Subsampling MCMC - An introduction for the survey statistician

The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical inference method in applied work. However, MCMC algorithms tend to be computationally demanding, and are particularly slow for large datasets. Data subsampling has recently been suggested as a way to make MCMC methods scalable on massively large data, utilizing efficient sampling schemes and estimators from the survey sampling literature. These developments tend to be unknown by many survey statisticians who traditionally work with non-Bayesian methods, and rarely use MCMC. Our article explains the idea of data subsampling in MCMC by reviewing one strand of work, Subsampling MCMC, a so called pseudo-marginal MCMC approach to speeding up MCMC through data subsampling. The review is written for a survey statistician without previous knowledge of MCMC methods since our aim is to motivate survey sampling experts to contribute to the growing Subsampling MCMC literature.Comment: Accepted for publication in Sankhya A. Previous uploaded version contained a bug in generating the figures and reference

Analytic Expressions for Stochastic Distances Between Relaxed Complex Wishart Distributions

The scaled complex Wishart distribution is a widely used model for multilook full polarimetric SAR data whose adequacy has been attested in the literature. Classification, segmentation, and image analysis techniques which depend on this model have been devised, and many of them employ some type of dissimilarity measure. In this paper we derive analytic expressions for four stochastic distances between relaxed scaled complex Wishart distributions in their most general form and in important particular cases. Using these distances, inequalities are obtained which lead to new ways of deriving the Bartlett and revised Wishart distances. The expressiveness of the four analytic distances is assessed with respect to the variation of parameters. Such distances are then used for deriving new tests statistics, which are proved to have asymptotic chi-square distribution. Adopting the test size as a comparison criterion, a sensitivity study is performed by means of Monte Carlo experiments suggesting that the Bhattacharyya statistic outperforms all the others. The power of the tests is also assessed. Applications to actual data illustrate the discrimination and homogeneity identification capabilities of these distances.Comment: Accepted for publication in the IEEE Transactions on Geoscience and Remote Sensing journa

Teaching old sensors New tricks: archetypes of intelligence

In this paper a generic intelligent sensor software architecture is described which builds upon the basic requirements of related industry standards (IEEE 1451 and SEVA BS- 7986). It incorporates specific functionalities such as real-time fault detection, drift compensation, adaptation to environmental changes and autonomous reconfiguration. The modular based structure of the intelligent sensor architecture provides enhanced flexibility in regard to the choice of specific algorithmic realizations. In this context, the particular aspects of fault detection and drift estimation are discussed. A mixed indicative/corrective fault detection approach is proposed while it is demonstrated that reversible/irreversible state dependent drift can be estimated using generic algorithms such as the EKF or on-line density estimators. Finally, a parsimonious density estimator is presented and validated through simulated and real data for use in an operating regime dependent fault detection framework

Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering

[EN] Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a yperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densities are estimated using Independent Component Analysis. Based on the K-means result, a model-based agglomerative clustering is performed, which provides a hierarchy of clusterings. Finally, a validation algorithm selects a clustering of the hierarchy; the number of clusters it contains is the estimated number of materials. Besides estimating the number of endmembers, the proposed method can approximately obtain the endmember (or spectrum) of each material by computing the centroid of its corresponding cluster. We have tested the proposed method using several hyperspectral images. The results show that the proposed method obtains approximately the number of materials that these images contain.This work was supported by Spanish Administration (Ministerio de Ciencia, Innovacion y Universidades) and European Union (FEDER) under grant TEC2017-84743-P.Prades Nebot, J.; Safont Armero, G.; Salazar Afanador, A.; Vergara Domínguez, L. (2020). Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering. Remote Sensing. 12(21):1-22. https://doi.org/10.3390/rs12213585S122122