Eric K. Jones

Curvature nonlinearity measure and filter divergence detector for nonlinear tracking problems

Several nonlinear filtering techniques are investigated for nonlinear tracking problems. Experimental results show that for a weakly nonlinear tracking problem, the extended Kalman filter and the unscented Kalman filter are good choices, while a particle filter should be used for problems with strong nonlinearity. To quantitatively determine the nonlinearity of a nonlinear tracking problem, we propose two types of measures: one is the differential geometry curvature measure and the other is based on the normalized innovation squared (NIS) of the Kalman filter. Simulation results show that both measures can effectively quantify the nonlinearity of the problem. The NIS is capable of detecting the filter divergence online. The curvature measure is more suitable for quantifying the nonlinearity of a tracking problem as determined via simulations.

Measures of Nonlinearity for Single Target Tracking Problems

The tracking of objects and phenomena exhibiting nonlinear motion is a topic that has application in many areas ranging from military surveillance to weather forecasting. Observed nonlinearities can come not only from the nonlinear dynamic motion of the object, but also from nonlinearities in the measurement model. Many techniques have been developed that attempt to deal with this issue, including the development of various types of filters, such as the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF), variants of the Kalman Filter (KF), as well as other filters such as the Particle Filter (PF). Determining the effectiveness of any of these techniques in nonlinear scenarios is not straightforward. Testing needs to be accomplished against scenarios whose degree of nonlinearity is known. This is necessary if reliable assessments of the effectiveness of nonlinear mitigation techniques are to be accomplished. In this effort, three techniques were investigated regarding their ability to provide useful measures of nonlinearity for representative scenarios. These techniques were the Parameter Effects Curvature (PEC), the Normalized Estimation Error Squared (NEES), and the Normalized Innovation Squared (NIS). Results indicated that the NEES was the most effective, although it does require truth values in its formulation.

Multi-scale decomposition tool for Content Based Image Retrieval

Content Based Image Retrieval (CBIR) is a technical area focused on answering “Who, What, Where and When,” questions associated with the imagery. A multi-scale feature extraction scheme based on wavelet and Contourlet transforms is proposed to reliably extract objects in images. First, we explore Contourlet transformation in association with Pulse Coupled Neural Network (PCNN) while the second technique is based on Rescaled Range (R/S) Analysis. Both methods provide flexible multi-resolution decomposition, directional feature extraction and are suitable for image fusion. The Contourlet transformation is conceptually similar to a wavelet transformation, but simpler, faster and less redundant. The R/S analysis, uses the range R of cumulative deviations from the mean divided by the standard deviation S, to calculate the scaling exponent, or a Hurst exponent, H. Following the original work of Hurst, the exponent H provides a quantitative measure of the persistence of similarities in a signal. For images, if information exhibits self-similarity and fractal correlation then H gives a measure of smoothness of the objects. The experimental results demonstrate that our proposed approach has promising applications for CBIR. We apply our multiscale decomposition approach to images with simple thresholding of wavelet/curvelet coefficients for visually sharper object outlines, salient extraction of object edges, and increased perceptual quality. We further explore these approaches to segment images and, the empirical results reported here are encouraging to determine who or what is in the image.

CADRE: continuous analysis and discovery from relational evidence

CADRE (continuous analysis and discovery from relational evidence) is a link detection system that takes in a threat pattern and partial evidence about threat cases and outputs threat hypotheses with inferred actors and events. CADRE uses a Prolog-based frame system to represent threat patterns and enforce temporal and equality constraints among pattern slots. Based on rules involving uniquely identifying slots in the pattern, CADRE triggers an initial set of threat hypotheses, and then refines these hypotheses by generating queries for unknown slots from constraints involving known slots. To evaluate hypotheses, CADRE scores each local hypothesis using a probabilistic model in order to create a consistent, high-value global hypothesis by pruning conflicting lower scoring hypotheses. In a program-wide first year evaluation using simulated threats, CADRE performed best overall among five participating link detection systems.

QuEST for Information Fusion in Multimedia Reports

Qualia-based Exploitation of Sensing Technology QuEST is an approach to create a cognitive exoskeleton to improve human-machine decision quality. In this paper, the authors present QuEST-motivated man-machine information fusion with an example for multimedia narratives. User-based situation awareness includes both elements of external sensory perception and internal cognitive explanation. The authors outline QuEST elements and tenets towards a reasoning approach that achieves human intelligence amplification IA in relation to data aggregation from machine artificial intelligence AI. In a use case example for multimedia exploitation, they showcase the need for enhanced understanding of the man mind-body cognition and the machine sensor-based reasoning for establishing a cohesive narrative of situational activities. QuEST tenets of structurally coherent, situated conceptualization, and simulated experience are utilized in organizing multimedia reports of Video Event Segmentation by Text VEST.

Activity recognition using Video Event Segmentation with Text (VEST)

Multi-Intelligence (multi-INT) data includes video, text, and signals that require analysis by operators. Analysis methods include information fusion approaches such as filtering, correlation, and association. In this paper, we discuss the Video Event Segmentation with Text (VEST) method, which provides event boundaries of an activity to compile related message and video clips for future interest. VEST infers meaningful activities by clustering multiple streams of time-sequenced multi-INT intelligence data and derived fusion products. We discuss exemplar results that segment raw full-motion video (FMV) data by using extracted commentary message timestamps, FMV metadata, and user-defined queries.

Hypothesis management for information fusion

The efficient management of large collections of fusion hypotheses presents a critical challenge for scaling high-level information fusion systems to solve large problems. We motivate this challenge in the context of two ALPHATECH research projects, and discuss several partial solutions. A recurring theme is the exploitation of space-efficient, factored representations of multiple hypotheses to enable efficient search for good hypotheses.

Failure prediction in ceramic composites using acoustic emission and digital image correlation

The objective of the work performed here was to develop a methodology for linking in-situ detection of localized matrix cracking to the final failure location in continuous fiber reinforced CMCs. First, the initiation and growth of matrix cracking are measured and triangulated via acoustic emission (AE) detection. High amplitude events at relatively low static loads can be associated with initiation of large matrix cracks. When there is a localization of high amplitude events, a measurable effect on the strain field can be observed. Full field surface strain measurements were obtained using digital image correlation (DIC). An analysis using the combination of the AE and DIC data was able to predict the final failure location.

Adaptive Filtering for Single Target Tracking

Many algorithms may be applied to solve the target tracking problem, including the Kalman Filter and different types of nonlinear filters, such as the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Particle Filter (PF). This paper describes an intelligent algorithm that was developed to elegantly select the appropriate filtering technique depending on the problem and the scenario, based upon a sliding window of the Normalized Innovation Squared (NIS). This technique shows promise for the single target, single radar tracking problem domain. Future work is planned to expand the use of this technique to multiple targets and multiple sensors.

Rapid knowledge base design via extension of mid-level knowledge components

The full potential of knowledge based systems will be realized only when users who are not knowledge engineers are able to develop and deploy the underlying knowledge bases. To this end we are developing a system for rapid knowledge base design that permits users to draw on an existing library of knowledge components and, through intuitive interfaces, shape them into an ontology for a domain and application of interest. The models created are supported by an expressive language that provides sophisticated constraint reasoning.