Probabilistic Approach to Robust Wearable Gaze Tracking

Creative Commons Attribution License (CC BY 4.0)This paper presents a method for computing the gaze point using camera data captured with a wearable gaze tracking device. The method utilizes a physical model of the human eye, ad- vanced Bayesian computer vision algorithms, and Kalman filtering, resulting in high accuracy and low noise. Our C++ implementation can process camera streams with 30 frames per second in realtime. The performance of the system is validated in an exhaustive experimental setup with 19 participants, using a self-made device. Due to the used eye model and binocular cam- eras, the system is accurate for all distances and invariant to device movement. We also test our system against a best-in-class commercial device which is outperformed for spatial accuracy and precision. The software and hardware instructions as well as the experimental data are pub- lished as open source.Peer reviewe

Doubly Robust Smoothing of Dynamical Processes via Outlier Sparsity Constraints

Coping with outliers contaminating dynamical processes is of major importance in various applications because mismatches from nominal models are not uncommon in practice. In this context, the present paper develops novel fixed-lag and fixed-interval smoothing algorithms that are robust to outliers simultaneously present in the measurements {\it and} in the state dynamics. Outliers are handled through auxiliary unknown variables that are jointly estimated along with the state based on the least-squares criterion that is regularized with the $\ell_1$-norm of the outliers in order to effect sparsity control. The resultant iterative estimators rely on coordinate descent and the alternating direction method of multipliers, are expressed in closed form per iteration, and are provably convergent. Additional attractive features of the novel doubly robust smoother include: i) ability to handle both types of outliers; ii) universality to unknown nominal noise and outlier distributions; iii) flexibility to encompass maximum a posteriori optimal estimators with reliable performance under nominal conditions; and iv) improved performance relative to competing alternatives at comparable complexity, as corroborated via simulated tests.Comment: Submitted to IEEE Trans. on Signal Processin

Conditional Posterior Cramer-Rao Lower Bound and Distributed Target Tracking in Sensor Networks

Sequential Bayesian estimation is the process of recursively estimating the state of a dynamical system observed in the presence of noise. Posterior Cramer-Rao lower bound (PCRLB) sets a performance limit onany Bayesian estimator for the given dynamical system. The PCRLBdoes not fully utilize the existing measurement information to give anindication of the mean squared error (MSE) of the estimator in the future. In many practical applications, we are more concerned with the value of the bound in the future than in the past. PCRLB is an offline bound, because it averages out the very useful measurement information, which makes it an off-line bound determined only by the system dynamical model, system measurement model and the prior knowledge of the system state at the initial time. This dissertation studies the sequential Bayesian estimation problem and then introduces the notation of conditional PCRLB, which utilizes the existing measurement information up to the current time, and sets the limit on the MSE of any Bayesian estimators at the next time step. This work has two emphases: firstly, we give the mathematically rigorous formulation of the conditional PCRLB as well as the approximate recursive version of conditional PCRLB for nonlinear, possibly non-Gaussian dynamical systems. Secondly, we apply particle filter techniques to compute the numerical values of the conditional PCRLB approximately, which overcomes the integration problems introduced by nonlinear/non-Gaussian systems. Further, we explore several possible applications of the proposed bound to find algorithms that provide improved performance. The primary problem of interest is the sensor selection problem for target tracking in sensor networks. Comparisons are also made between the performance of sensor selection algorithm based on the proposed bound and the existing approaches, such as information driven, nearest neighbor, and PCRLB with renewal strategy, to demonstrate the superior performances of the proposed approach. This dissertation also presents a bandwidth-efficient algorithm for tracking a target in sensor networks using distributed particle filters. This algorithm distributes the computation burden for target tracking over the sensor nodes. Each sensor node transmits a compressed local tracking result to the fusion center by a modified expectationmaximization (EM) algorithm to save the communication bandwidth. The fusion center incorporates the compressed tracking results to give the estimate of the target state. Finally, the target tracking problem in heterogeneous sensor networks is investigated extensively. Extended Kalman Filter and particle filter techniques are implemented and compared for tracking a maneuvering

The evaluation of radar guided missile performance using various target glint models

A radar guided missile seeker approaching a target arrives at a crossover point beyond which the entire target is illuminated by the missile seeker\u27s radar antenna beam. When this occurs, for a complex target, the radar senses a time changing target center location which may greatly complicate the terminal tracking portion of the seeker\u27s flight. This thesis documents various glint models in use today and compares the performance of these models by using a seeker model to determine the effect of glint on terminal tracking performance. A large volume of data has been compiled describing various radar characteristics of complex targets. Some of the glint models discussed herein are the result of independent investigations and some models are derived analytically. The return energy transmitted from a radar and reflected from a complex target is generally statistical in nature due to the random nature of the reflecting surfaces dispersed over the target vehicle and also because the target is continually changing aspect. A radar target\u27s reflecting characteristics are of concern when a seeker is attempting to acquire the target and again when the seeker is closing on the target. A large amount of data exists for determining long range acquisition capabilities of various radar schemes. The acquisition of a target in a sea clutter background has been thoroughly investigated in the past decade and today\u27s improved techniques (Moving Target Indication and Pulse Compression) have enabled moderately powered radars to acquire very small targets in high sea states. At long ranges, the individual target elements present a unified amplitude response (or appear as a point source). The main problem during acquisition is to reduce the viewing area to dimensions comparable to the target so that the target return will be distinguishable. This argument does not apply to an interferometer as target phase variations will still contribute to acquisition error, however the sophistication required to implement interferometers into a seeker design precludes their use for present day tracking schemes. Although acquisition problems can severely limit the response time of a seeker bearing vehicle or aircraft to an eminent attack threat, the inability of a radar seeker to operate in the presence of angle glint can render the seeker useless. A seeker\u27s design must often be a compromise between tracking accuracy, acquisition capability, and dynamic versatility, since the weight and cost penalties associated with multiple radar tracking modes within a single seeker are prohibitive. Therefore, since the literature abounds with acquisition theory and techniques, the main portion of this thesis consists of analyzing the post-acquisition seeker performance in the presence of angular glint or angle noise. The glint models used in this analysis represent models in use at the present time. In addition, models which were derived are compared to determine the degree of correlation which exists between measured target models and those models which are derived from their statistical characteristics. The method employed for the evaluation is general in nature so that the procedure could be used to evaluate any subsequent glint model derivations. In addition, some recent work is summarized which demonstrates the advantages of using swept frequency techniques to improve radar tracking performance in the presence of angular glint --Abstract, pages ii-iv

Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms: validation against collocated MODIS and CALIOP data

The objective of the National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2) mission is to retrieve the column-averaged carbon dioxide (CO₂) dry air mole fraction (XCO2) from satellite measurements of reflected sunlight in the near-infrared. These estimates can be biased by clouds and aerosols, i.e., contamination, within the instrument's field of view. Screening of the most contaminated soundings minimizes unnecessary calls to the computationally expensive Level 2 (L2) X_(CO₂) retrieval algorithm. Hence, robust cloud screening methods have been an important focus of the OCO-2 algorithm development team. Two distinct, computationally inexpensive cloud screening algorithms have been developed for this application. The A-Band Preprocessor (ABP) retrieves the surface pressure using measurements in the 0.76 µm O₂ A band, neglecting scattering by clouds and aerosols, which introduce photon path-length differences that can cause large deviations between the expected and retrieved surface pressure. The Iterative Maximum A Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) retrieves independent estimates of the CO₂ and H₂O column abundances using observations taken at 1.61 µm (weak CO₂ band) and 2.06 µm (strong CO₂ band), while neglecting atmospheric scattering. The CO₂ and H₂O column abundances retrieved in these two spectral regions differ significantly in the presence of cloud and scattering aerosols. The combination of these two algorithms, which are sensitive to different features in the spectra, provides the basis for cloud screening of the OCO-2 data set. To validate the OCO-2 cloud screening approach, collocated measurements from NASA's Moderate Resolution Imaging Spectrometer (MODIS), aboard the Aqua platform, were compared to results from the two OCO-2 cloud screening algorithms. With tuning of algorithmic threshold parameters that allows for processing of  ≃ 20–25 % of all OCO-2 soundings, agreement between the OCO-2 and MODIS cloud screening methods is found to be  ≃ 85 % over four 16-day orbit repeat cycles in both the winter (December) and spring (April–May) for OCO-2 nadir-land, glint-land and glint-water observations. No major, systematic, spatial or temporal dependencies were found, although slight differences in the seasonal data sets do exist and validation is more problematic with increasing solar zenith angle and when surfaces are covered in snow and ice and have complex topography. To further analyze the performance of the cloud screening algorithms, an initial comparison of OCO-2 observations was made to collocated measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). These comparisons highlight the strength of the OCO-2 cloud screening algorithms in identifying high, thin clouds but suggest some difficulty in identifying some clouds near the surface, even when the optical thicknesses are greater than 1

Robust adaptive beamforming for MIMO monopulse radar

Researchers have recently proposed a widely separated multiple-input multiple-output (MIMO) radar using monopulse angle estimation techniques for target tracking. The widely separated antennas provide improved tracking performance by mitigating complex target radar cross-section fades and angle scintillation. An adaptive array is necessary in this paradigm because the direct path from any transmitter could act as a jammer at a receiver. When the target-free covariance matrix is not available, it is critical to include robustness into the adaptive beamformer weights. This work explores methods of robust adaptive monopulse beamforming techniques for MIMO tracking radar