Exoplanet Research with the Stratospheric Observatory for Infrared Astronomy (SOFIA)

When the Stratospheric Observatory for Infrared Astronomy (SOFIA) was conceived and its first science cases defined, exoplanets had not been detected. Later studies, however, showed that optical and near-infrared photometric and spectrophotometric follow-up observations during planetary transits and eclipses are feasible with SOFIA's instrumentation, in particular with the HIPO-FLITECAM and FPI+ optical and near infrared (NIR) instruments. Additionally, the airborne-based platform SOFIA has a number of unique advantages when compared to other ground- and space-based observatories in this field of research. Here we will outline these theoretical advantages, present some sample science cases and the results of two observations from SOFIA's first five observation cycles -- an observation of the Hot Jupiter HD 189733b with HIPO and an observation of the Super-Earth GJ 1214b with FLIPO and FPI+. Based on these early products available to this science case, we evaluate SOFIA's potential and future perspectives in the field of optical and infrared exoplanet spectrophotometry in the stratosphere.Comment: Invited review chapter, accepted for publication in "Handbook of Exoplanets" edited by H.J. Deeg and J.A. Belmonte, Springer Reference Work

Transmission of UHF radiowaves through buildings in urban microcell environments

Results are presented of high-resolution time delay and angle-of-arrival measurements behind a large building in an urban microcell. It is demonstrated that in this particular case the electromagnetic field is dominated by contributions resulting from transmission through the building. The associated loss over free-space loss i

Tracking of various targets in the infrared and issues encountered

The use of a computer to track objects has been a subject of interest for a few decades. The applications these algorithms may be applied to span a large number of fields; anything from homeland security to the study of animal behavior. In particular, visible tracking has been around the longest and has the largest library of algorithms available. Algorithms such as Mean Shift have become a standard for testing algorithms against. However, algorithms such as Mean Shift may work well for visible video data, infrared video data presents some issues beyond many visible algorithms. Infrared video gives certain advantages over visible, such as day/night tracking and camouflage detection. However, it also presents several issues as well. The detectors are more easily saturated, causing a temporary loss of data, as well as the drastic change in object appearance. These issues do not override the utility of infrared video being used for tracking purposes. This paper will go through some of the various applications of tracking, as well as the necessity of algorithm development in the infrared field. A couple algorithm metrics are also considered for a new basis for the testing of algorithms, as well as the introduction of a tracking algorithm testing platform

On particle filters in radar target tracking

The dissertation focused on the research, implementation, and evaluation of particle filters for radar target track filtering of a maneuvering target, through quantitative simulations and analysis thereof. Target track filtering, also called target track smoothing, aims to minimize the error between a radar target's predicted and actual position. From the literature it had been suggested that particle filters were more suitable for filtering in non-linear/non-Gaussian systems. Furthermore, it had been determined that particle filters were a relatively newer field of research relating to radar target track filtering for non-linear, non-Gaussian maneuvering target tracking problems, compared to the more traditional and widely known and implemented approaches and techniques. The objectives of the research project had been achieved through the development of a software radar target tracking filter simulator, which implemented a sequential importance re-sampling particle filter algorithm and suitable target and noise models. This particular particle filter had been identified from a review of the theory of particle filters. The theory of the more conventional tracking filters used in radar applications had also been reviewed and discussed. The performance of the sequential importance re-sampling particle filter for radar target track filtering had been evaluated through quantitative simulations and analysis thereof, using predefined metrics identified from the literature. These metrics had been the root mean squared error metric for accuracy, and the normalized processing time metric for computational complexity. It had been shown that the sequential importance re-sampling particle filter achieved improved accuracy performance in the track filtering of a maneuvering radar target in a non-Gaussian (Laplacian) noise environment, compared to a Gaussian noise environment. It had also been shown that the accuracy performance of the sequential importance re-sampling particle filter is a function of the number of particles used in the sequential importance re-sampling particle filter algorithm. The sequential importance re-sampling particle filter had also been compared to two conventional tracking filters, namely the alpha-beta filter and the Singer-Kalman filter, and had better accuracy performance in both cases. The normalized processing time of the sequential importance re-sampling particle filter had been shown to be a function of the number of particles used in the sequential importance re-sampling particle filter algorithm. The normalized processing time of the sequential importance re-sampling particle filter had been shown to be higher than that of both the alpha-beta filter and the Singer-Kalman filter. Analysis of the posterior Cramér-Rao lower bound of the sequential importance re-sampling particle filter had also been conducted and presented in the dissertation

Probabilistic Framework for Sensor Management

A probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are combined such that long-term effects of possible sensor configurations and stochastic uncertainties resulting from noisy measurements can be incorporated into the sensor management decisions

Exploring Himawari-8 geostationary observations for the advanced coastal monitoring of the Great Barrier Reef

Larissa developed an algorithm to enable water-quality assessment within the Great Barrier Reef (GBR) using weather satellite observations collected every 10 minutes. This unprecedented temporal resolution records the dynamic nature of water quality fluctuations for the entire GBR, with applications for improved monitoring and management

Advanced photonic solutions for high precision astronomical imaging

The sharply rising productivity of exoplanet searches over the past two decades has delivered profound statistical insights into the prevalence and diversity of worlds around other stars. The frontier for astronomers has now expanded into the new era of exoplanet characterisation. Major progress here will only be achieved with new instrumental advances. Most highly sought-after is the capability to separate the faint light from a planet from the glare of the host star. The direct detection of planetary photons will enable unique spatial and spectral studies, revealing intrinsic properties of atmospheres and surfaces. In this project, a prototype instrument GLINT South (Guided Light Interferometric Nulling Technology) was developed. It employs nulling interferometry in which the light from the host star is actively rejected though destructive interference. Such advanced control and processing of starlight is accomplished by way of photonic technology fabricated into integrated optical chips. A monochromatic null depth was measured in the laboratory consistent with 0 within an uncertainty of 10-3. The instrument was tested at the Anglo Australian Telescope, and a sample of infrared-bright stars were observed retrieving uniform disk diameters in close agreement to the literature values, despite the stellar diameters being beyond the telescopes formal di raction limit. Furthermore, an algorithm was created to optimise the design of integrated optics waveguides for pupil remapping chips leading to the design of a 4-input remapping chip which will signi cantly expand capabilities and deliver multi-channel nulling as well as complex visibility data. The photonic nulling devices, inscribed within miniature, robust and environmentally stable monolithic chips are a promising avenue to one of astronomy's grandest challenges of characterising the chemical and physical environments of exoplanets

Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond

Since the landmark work of R. E. Kalman in the 1960s, considerable efforts have been devoted to time series state space models for a large variety of dynamic estimation problems. In particular, parametric filters that seek analytical estimates based on a closed-form Markov–Bayes recursion, e.g., recursion from a Gaussian or Gaussian mixture (GM) prior to a Gaussian/GM posterior (termed ‘Gaussian conjugacy’ in this paper), form the backbone for a general time series filter design. Due to challenges arising from nonlinearity, multimodality (including target maneuver), intractable uncertainties (such as unknown inputs and/or non-Gaussian noises) and constraints (including circular quantities), etc., new theories, algorithms, and technologies have been developed continuously to maintain such a conjugacy, or to approximate it as close as possible. They had contributed in large part to the prospective developments of time series parametric filters in the last six decades. In this paper, we review the state of the art in distinctive categories and highlight some insights that may otherwise be easily overlooked. In particular, specific attention is paid to nonlinear systems with an informative observation, multimodal systems including Gaussian mixture posterior and maneuvers, and intractable unknown inputs and constraints, to fill some gaps in existing reviews and surveys. In addition, we provide some new thoughts on alternatives to the first-order Markov transition model and on filter evaluation with regard to computing complexity

A Hybrid Labeled Multi-Bernoulli Filter With Amplitude For Tracking Fluctuating Targets

The amplitude information of target returns has been incorporated into many tracking algorithms for performance improvements. One of the limitations of employing amplitude feature is that the signal-to-noise ratio (SNR) of the target, i.e., the parameter of amplitude likelihood, is usually assumed to be known and constant. In practice, the target SNR is always unknown, and is dependent on aspect angle hence it will fluctuate. In this paper we propose a hybrid labeled multi-Bernoulli (LMB) filter that introduces the signal amplitude into the LMB filter for tracking targets with unknown and fluctuating SNR. The fluctuation of target SNR is modeled by an autoregressive gamma process and amplitude likelihoods for Swerling 1 and 3 targets are considered. Under Rao-Blackwell decomposition, an approximate Gamma estimator based on Laplace transform and Markov Chain Monte Carlo method is proposed to estimate the target SNR, and the kinematic state is estimated by a Gaussian mixture filter conditioned on the target SNR. The performance of the proposed hybrid filter is analyzed via a tracking scenario including three crossing targets. Simulation results verify the efficacy of the proposed SNR estimator and quantify the benefits of incorporating amplitude information for multi-target tracking