Walter S. Kuklinski

Track maintenance and positional estimation via ground moving target indicator and geolocation data fusion

In modern tactical environments, information from a variety of sensors may be simultaneously utilized to improve target detection and tracking procedures. Towards this goal, two data fusion algorithms are developed that implement such processing. First, a GMTI (ground moving target indicator)/geolocation algorithm is derived to assist with track maintenance during periods of GMTI data blackout. Next, a geolocation estimator is presented which fuses time difference of arrival (TDOA), frequency difference of arrival (FDOA), and angle of arrival (AOA) measurements. These algorithms lead to improved positional estimation, track accuracy, and track maintenance. They also potentially reduce the number of platforms required to successfully locate and track targets.

3D SAR imaging using a hybrid decomposition superresolution technique

A technique to form super-resolved 3D Synthetic Aperture Radar (SAR) images from a limited number of elevation passes is presented in this paper. This technique models the environment as containing a finite number of isotropically radiating, frequency independent point scatterers in Additive White Gaussian Noise (AWGN), and applies a hybrid super-resolution method that yields the Maximum Likelihood (ML) estimates of scatterer strengths and resolves their locations in the data deficient dimension well beyond the Fourier resolution limit.

An Innovative method to determine multi-system performance for the detection of clandestine tunnels

The threat posed by underground clandestine tunnels has been a growing concern for law enforcement and national security. Cross-border tunnels have been used by smugglers with the intention of avoiding border security for trafficking people, drugs, firearms, and other illegal materials. The ability to detect these tunnels is vital to achieving effective border control. This paper describes the development of an innovative method to model and assess the performance of various sensor systems in the geological region of their intended use, and to determine the best sensing modalities and equipment to operate in that region. The method includes: 1) Investigation and characterization of the regional representative geologic and geophysical properties of the shallow subsurface soil and environmental conditions along the southern US border; 2) Sensor performance modeling and simulation studies for various sensor systems components/configurations, tunnel characteristics, surface and subsurface environmental and soil conditions; and 3) Validation and verification of the performance via tunnel detection testbed development and demonstration. The results of these combined efforts will be used to develop and implement an integrated sensor performance characterization suite to assist in identification of the most suitable methods and/or equipment to detect tunnels in a variety of locales. A case study illustrating our approach applied to an area along the southern border using available field data to characterize the sensor performance indicates the methodology can yield accurate predictions of sensor performance in various geologies and indigenous environmental noise. For the simulations to be useful, more work is planned to improve the accuracy of the sensor models, the precision of the geophysical databases, and to overcome the long execution times required for the models to run.

Application of Novel Software Algorithms to Spectral-Domain Optical Coherence Tomography for Automated Detection of Diabetic Retinopathy.

BACKGROUND AND OBJECTIVE To present novel software algorithms applied to spectral-domain optical coherence tomography (SD-OCT) for automated detection of diabetic retinopathy (DR). PATIENTS AND METHODS Thirty-one diabetic patients (44 eyes) and 18 healthy, nondiabetic controls (20 eyes) who underwent volumetric SD-OCT imaging and fundus photography were retrospectively identified. A retina specialist independently graded DR stage. Trained automated software generated a retinal thickness score signifying macular edema and a cluster score signifying microaneurysms and/or hard exudates for each volumetric SD-OCT. RESULTS Of 44 diabetic eyes, 38 had DR and six eyes did not have DR. Leave-one-out cross-validation using a linear discriminant at missed detection/false alarm ratio of 3.00 computed software sensitivity and specificity of 92% and 69%, respectively, for DR detection when compared to clinical assessment. CONCLUSION Novel software algorithms applied to commercially available SD-OCT can successfully detect DR and may have potential as a viable screening tool for DR in future. [Ophthalmic Surg Lasers Imaging Retina. 2016;47:410-417.].

Random set tracking and entropy based control applied to distributed sensor networks

This paper describes an integrated approach to sensor fusion and resource management applicable to sensor networks. The sensor fusion and tracking algorithm is based on the theory of random sets. Tracking is herein considered to be the estimation of parameters in a state space such that for a given target certain components, e.g., position and velocity, are time varying and other components, e.g., identifying features, are stationary. The fusion algorithm provides at each time step the posterior probability density function, known as the global density, on the state space, and the control algorithm identifies the set of sensors that should be used at the next time step in order to minimize, subject to constraints, an approximation of the expected entropy of the global density. The random set approach to target tracking models association ambiguity by statistically weighing all possible hypotheses and associations. Computational complexity is managed by approximating the posterior Global Density using a Gaussian mixture density and using an approach based on the Kulbach-Leibler metric to limit the number of components in the Gaussian mixture representation. A closed form approximation of the expected entropy of the global density, expressed as a Gaussian mixture density, at the next time step for a given set of proposed measurements is developed. Optimal sensor selection involves a search over subsets of sensors, and the computational complexity of this search is managed by employing the Mobius transformation. Field and simulated data from a sensor network comprised of multiple range radars, and acoustic arrays, that measure angle of arrival, are used to demonstrate the approach to sensor fusion and resource management.

Target recognition using HRR profile-based incoherent SAR (InSAR) image formation

Feature-aided target verification is a challenging field of research, with the potential to yield significant increases in the confidence of re-established target tracks after kinematic confusion events. Using appropriate control algorithms airborne multi-mode radars can acquire a library of HRR (High Range Resolution) profiles for targets as they are tracked. When a kinematic confusion event occurs, such as a vehicle dropping below MDV (Minimum Detectable Velocity) for some period of time, or two target tracks crossing, it is necessary to utilize feature-aided tracking methods to correctly associate post-confusion tracks with pre-confusion tracks. Many current HRR profile target recognition methods focus on statistical characteristics of either individual profiles or sets of profiles taken over limited viewing angles. These methods have not proven to be very effective when the pre- and post- confusion libraries do not overlap in azimuth angle. To address this issue we propose a new approach to target recognition from HRR profiles. We present an algorithm that generates 2-D imagery of targets from the pre- and post-confusion libraries. These images are subsequently used as the input to a target recognition/classifier process. Since, center-aligned HRR Profiles, while ideal for processing, are not easily computed in field systems, as they require the airborne platforms center of rotation to line up with the geometric center of the moving target (this is impossible when multiple targets are being tracked), our algorithm is designed to work with HRR profiles that are aligned to the leading edge (the first detection above a threshold, commonly referred to as Edge-Aligned HRR profiles). Our simulated results demonstrate the effectiveness of this method for classifying target vehicles based on simulations using both overlapping and non-overlapping HRR profile sets. The algorithm was tested on several test cases using an input set of .28 m resolution XPATCH generated HRR profiles of 20 test vehicles (civilian and military) at various elevation angles.