Kolba M.P.,Signal Innovations Group, Inc. |
Scott W.R.,Georgia Institute of Technology |
Collins L.M.,Duke University
IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans | Year: 2011
A framework is presented for information-theoretic sensor management for the detection of static targets. The sensor manager searches for N targets within a cell grid using a suite of M sensor platforms. Each sensor platform may contain one or more sensing modalities, and each of these modalities has known probabilities of detection and false alarm and also has an associated cost of use. Additional information such as motion constraints on the sensors and the prior distribution of the targets in space is incorporated. The sensor manager then directs the movement of the sensors through the grid by maximizing the expected information gain that will be obtained with each new sensor observation. Key modeling questions are addressed, including the selection of an appropriate information measure and the joint or independent management of the sensors. Through a number of simulations, the performance of the sensor manager is compared to the performance of a blind sweep procedure, a random search procedure, and an alternative information-theoretic sensor manager. The intelligent sensor management procedure is demonstrated to achieve a superior performance compared to all of the other three techniques. A specific application area for which the sensor management problem is becoming more critical is landmine detection; thus, the performance of the sensor manager is also analyzed using real data from three different landmine detection sensing modalities, and the proposed sensor management technique is again demonstrated to be superior compared to more simplistic approaches. © 2006 IEEE. Source
Cevher V.,University of Maryland University College |
Indyk P.,University of Warsaw |
Carin L.,Signal Innovations Group, Inc. |
Baraniuk R.,Rice University
IEEE Signal Processing Magazine | Year: 2010
Many applications in digital signal processing, machine learning, and communications feature a linear regression problem in which unknown data points, hidden variables, or code words are projected into a lower dimensional space via © 2006 IEEE. Source
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 97.31K | Year: 2011
The proposed Phase I program will develop a Bayesian framework for modeling and predicting track uncertainty and performance as a function of OCs and integrate this framework with algorithms for tracking vehicles in WAMI. The proposed Performance and Uncertainty Modeling and Prediction for Tracking (PUMP-T) framework is comprised of the following key elements: (1) explicit extraction of operating conditions (OCs), (2) calculation of information-theoretic metrics for quantifying uncertainty in track posterior probability density functions (PDFs), (3) sparse Bayesian regression algorithms for modeling track uncertainty as a function of OCs, (4) Bayesian semi-supervised regression/classification algorithms for modeling track performance as a function of OCs, (5) an offline process for training the uncertainty and performance models, and (6) an online process for predicting track uncertainty and performance and directly validating the models in the context of the observed data. The underlying prediction models driving the PUMP-T framework will be founded on posterior PDFs over track states inferred within a rigorous Bayesian framework. In Phase I, the PUMP-T framework will be designed to be agnostic to the specific tracking algorithm employed such that alternate tracking algorithms can be integrated and evaluated within the PUMP-T framework. BENEFIT: While the PUMP-T framework will be leveraged for video tracking applications in the proposed program, the framework easily generalizes to other exploitation algorithms (e.g. detection and classification) and other sensor modes. Medical device manufacturers are developing cutting edge sensors leading to revolutionary advances in diagnosis of a variety of diseases, especially cancer. These devices have the ability to gather a significant amount of data, much more than a physician or technician can handle alone. Therefore, automated processing, including rigorous understanding of uncertainty and performance predictions, is necessary for realizing the full potential of these devices. While the video exploitation software leveraged under this SBIR offer significant impact on a broad spectrum of DoD and IC applications today and in the future, the exploitation software also offers significant benefit to several existing and new commercial markets, including municipal public safety, traffic analysis and flow optimization, mining of video for targeted advertising, corporate security, and mining of surveillance cameras for retail applications. SIG has had discussions with companies and venture capital interests that participate in such markets and believes that the exploitation products of this SBIR would benefit these video content extraction markets.
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 499.99K | Year: 2014
Signal Innovations Group (SIG) proposes a Phase II program that addresses three primary capabilities for applications involving high-dimensional observed data: reversible nonlinear data dimensionality reduction, static and dynamic synthesis of data in the observed space based on a low-dimensional latent space, and intuitive and meaningful human interaction with the data in both the observed and latent spaces. Each of these three capabilities will be addressed in the Phase II Base effort through the development of models and algorithms, visualization techniques, and a prototype user interface. Additionally, proposed Phase II Options 1 and 2 will focus on maturing and optimizing the technology and software, ultimately leading to transition and integration with a targeted program of record.
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase II | Award Amount: 1.05M | Year: 2013
ABSTRACT: This program will develop salient feature analysis and saliency cueing capabilities for SAR-based exploitation and demonstrate this technology with operationally relevant data and classifiers. This saliency technology will immediately enable the development of exploitable, robust, compact feature databases for sustainable radar CID. This new SAR-based saliency capability will complement existing HRR-based saliency technology by providing either independent or joint discovery of sparse sets of robust, exploitable features across these two common radar sensing modalities, in support of air-to-air or air-to-ground moving and stationary CID missions. This framework provides a balance between a compact target representation and target generalization across in-class variations. The underlying probabilistic model captures the uncertainty inherent to the training process due to limited or noisy data, and propagates these probabilities in a mathematically rigorous manner to downstream processes. The proposed work for this program will focus on near-term capabilities of sustainable radar CID database development and will lay the fundamental groundwork for potential fusion of radar and EO features based upon saliency and target geometry. BENEFIT: Phase II will result in a combined salient feature analysis, saliency cueing, and classification performance analysis capability for both HRR and SAR radar modalities integrated into POSSIBLE. This capability will enable the automatic identification of compact exploitable radar signature features over fewer target aspect states for more efficient and sustainable CID databases. While the salient feature analysis framework will be leveraged on the proposed program for classification with radar sensor modalities, the framework easily generalizes to other data modalities and exploitation applications, such as detection, recognition, identification, and general data categorization. Medical device manufacturers are developing cutting-edge sensors and equipment that is leading to revolutionary advances in non-invasive diagnosis of a variety of diseases, especially cancer. These devices have the ability to gather a significant amount of data, much more than a physician or technician can handle alone. Additionally, commercial satellite imagery providers are developing sensors that collect extremely high-resolution imagery. While more pixels generally provide more information, large amounts of data also lead to increased burden on image analysis, storage, and query. Therefore, automated processing, including rigorous understanding of salient features, is necessary for realizing the full potential of these new medical devices and satellite imagery. SIG is actively engaged with sensor and data providers in each of these industries and will leverage these relationships to commercialize the products of this Phase II SBIR.