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DCM Research Resources, LLC
Germantown, MD, United States
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Su H.,Hohai University | Du Q.,Mississippi State University | Chen G.,DCM Research Resources, LLC | Du P.,Nanjing University
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2014

A particle swarm optimization (PSO)-based system is proposed to select bands and determine the optimal number of bands to be selected simultaneously, which is near-automatic with only a few data-independent parameters. The proposed system includes two particle swarms, i.e., the outer one for estimating the optimal number of bands and the inner one for the corresponding band selection. To avoid employing an actual classifier within PSO so as to greatly reduce computational cost, criterion functions that can gauge class separability are preferred; specifically, minimum estimated abundance covariance (MEAC) and Jeffreys-Matusita (JM) distance are adopted in this research. The experimental results show that the 2PSO-based algorithm outperforms the popular sequential forward selection (SFS) method and PSO with one particle swarm in band selection. © 2014 IEEE.

Agency: Department of Defense | Branch: Air Force | Program: STTR | Phase: Phase I | Award Amount: 100.00K | Year: 2010

In this proposal, DCM Research Resources (DCM), LLC, and Syracuse University propose a highly innovative distributed pattern detection and classification approach, called Compressive Sensing aided Sequential Pattern Detection and Classification (CSASPDC) in Distributed Sensor Network. Our goal is to develop sophisticated approaches that can effectively detect or classify very weak distributed patterns that are undetectable in the local signatures at individual nodes. At the mean time, any solution to pattern detection and classification needs to take into account the very limited energy and communication bandwidth. We propose a pattern detection/classification framework that combines sophisticated techniques in several areas, including compressive sensing, distributed detection, game theoretic sensor selection and management for detection/estimation, and sequential detection/classification, and secure cognitive radio, leveraging our previous experiences in these areas BENEFIT: The proposed compressive sensing aided sequential pattern detection and classification (CSASPDC) algorithm for distributed sensor network is very important in many military (DoD) applications including reconnaissance and surveillance, homeland security, etc. It can be directly used for developing of advanced mission planning and emergency preparedness decision support systems such as CB agent defense, Space Situational Awareness Fusion Intelligent Research Environment [SAFIRE] program, JSPOC Situational Awareness Response System (JSARS), BMDS system, Future Combat System (FCS), Joint Strike Fighter (JSF) program, and JSSEO program. During the Phase I, we will work closely with Lockheed Martin MS2, who is prime contractor on the Aegis weapon system, the Littoral Combat Ship, and C2 lead for the DDG-1000 program. We have developed a strong and realistic plan to transition our technology to their programs (support letter attached). In addition, DCM and Lockheed Martin are building a mentor-protégé program. We will leverage this relationship to identify the end customer, and work with these teams to transition our Phase 2 technology into their program. The DOD contact who knows the details of our work and who knows the above programs is Dr. Erik Blasch from AFRL. The market for military applications is quite large.Other potential commercial applications include air traffic control system, network security intrusion detection, the national weather service, physical security systems, law enforcement agency, emergency control center, border and coast patrol, pollution monitoring, remote sensing, robotics, medical applications, and global awareness. The size of this market is not small and hard to estimate. We expect the aggregate market size will be similar to that of military applications.

Shen D.,DCM Research Resources, LLC | Tian Z.,Michigan Technological University | Chen G.,DCM Research Resources, LLC | Pham K.,Air Force Research Lab | Blasch E.,Air Force Research Lab
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010

In this paper, we studied the performance metrics for evaluating Network-Centric Warfare (NCW) battlefield awareness. We developed a set of novel information awareness metrics to enable responsive situation assessment under mission-critical conditions. The awareness metrics model (AMM) reflects the global information values of event locations such as position, terrain information, dangerousness, survivability, cell difficulty, and mission importance. Based on the enhanced awareness model, we developed an in-network cooperative multi-sensor search and track (ICMS) algorithm by solving a unified optimization problem in which each cell is searched and all detected objects are tracked for at least a desired track-lifetime period. We utilize a track-lifetime surface metric to represent the spatial and temporal aspects of object movements over a region of interest that requires frequent sampling of the known and estimated object positions (track maintenance) as well as possible object arrivals (track initiation). To demonstrate the effectiveness of our approach, we implemented our ICMS algorithm in a numerical example and found that it is effective in the sense that most cells with high activity are well-searched. © 2010 Copyright SPIE - The International Society for Optical Engineering.

Chen H.,University of New Orleans | Shen D.,DCM Research Resources, LLC | Chen G.,DCM Research Resources, LLC | Blasch E.,Air Force Research Lab | Pham K.,Air Force Research Lab
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010

This paper is concerned with the nonlinear filtering problem for tracking a space object with possibly delayed measurements. In a distributed dynamic sensing environment, due to limited communication bandwidth and long distances between the earth and the satellites, it is possible for sensor reports to be delayed when the tracking filter receives them. Such delays can be complete (the full observation vector is delayed) or partial (part of the observation vector is delayed), and with deterministic or random time lag. We propose an approximate approach to incorporate delayed measurements without reprocessing the old measurements at the tracking filter. We describe the optimal and suboptimal algorithms for filter update with delayed measurements in an orbital trajectory estimation problem without clutter. Then we extend the work to a single object tracking under clutter where probabilistic data association filter (PDAF) is used to replace the recursive linear minimum means square error (LMMSE) filter and delayed measurements with arbitrary lags are be handled without reprocessing the old measurements. Finally, we demonstrate the proposed algorithms in realistic space object tracking scenarios using the NASA General Mission Analysis Tool (GMAT). © 2010 SPIE.

Chen G.,DCM Research Resources, LLC | Blasch E.,Air Force Research Lab | Shen D.,DCM Research Resources, LLC | Chen H.,University of New Orleans | Pham K.,Air Force Research Lab
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2010

In the modern networked battlefield, network centric warfare (NCW) scenarios need to interoperate between shared resources and data assets such as sensors, UAVs, satellites, ground vehicles, and command and control (C2/C4I) systems. By linking and fusing platform routing information, sensor exploitation results, and databases (e.g. Geospatial Information Systems [GIS]), the shared situation awareness and mission effectiveness will be improved. Within the information fusion community, various research efforts are looking at open standard approaches to composing the heterogeneous network components under one framework for future modeling and simulation applications. By utilizing the open source services oriented architecture (SOA) based sensor web services, and GIS visualization services, we propose a framework that ensures the fast prototyping of intelligence, surveillance, and reconnaissance (ISR) system simulations to determine an asset mix for a desired mission effectiveness, performance modeling for sensor management and prediction, and user testing of various scenarios. © 2010 SPIE.

Chen H.,University of New Orleans | Shen D.,DCM Research Resources, LLC | Chen G.,DCM Research Resources, LLC | Blasch E.P.,U.S. Air force | Pham K.,U.S. Air force
Proceedings of the 2010 American Control Conference, ACC 2010 | Year: 2010

This paper outlines a strategy for tracking evasive objects in discrete space using game theory to allocate sensor resources. One or more searchers have to allocate the effort among the discrete cells to maximize the object detection probability within a finite time horizon or minimize the expected search time to achieve the desired detection probability under a false alarm constraint.We review the standard formulations under a sequential decision setting for finding stationary objects. Then we consider both robust and optimal search strategies and extend the standard search problem to a two-person zero-sum search allocation game where the object wants to hide from the searcher and the object has incomplete information about the searcher's remaining search time. We discuss how the results affect the sensor management and mission planning for cooperative unmanned aerial vehicle (UAV) search tasks and provide simulation examples to show the effectiveness of the proposed method compared with random search strategy. © 2010 AACC.

Yang H.,Mississippi State University | Du Q.,Mississippi State University | Chen G.,DCM Research Resources, LLC
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2011

The high dimensionality of hyperspectral imagery challenges image processing and analysis. Band selection is a common technique for dimensionality reduction. When the desired object information is unknown, an unsupervised band selection approach is employed to select the most distinctive and informative bands. Although band selection can significantly alleviate the computational burden in the following data processing and analysis, the process itself may induce additional computation complexity, especially when the image spatial size is large; it may be time-consuming for unsupervised band selection methods that need to take all pixels into consideration. Parallel computing techniques are widely adopted to alleviate the computational burden and to achieve real-time processing of data with vast volume. In this paper, we propose parallel implementations via emerging general-purpose graphics processing units (GPUs) for band selection without changing band selection result. Its speedup performance is comparable to the cluster-based parallel implementation. We also propose an approach to using several selected pixels for unsupervised band selection and the number of pixels needed can be equal to the number of selected bands minus one. With whitened pixel signatures (not the original pixels), band selection performance can be comparable to or even better than that from using all the pixels. For this approach, parallel computing is implemented for pixel selection only, since computational complexity in band selection has been greatly reduced. © 2011 IEEE.

Liu K.,Mississippi State University | Ma B.,Mississippi State University | Du Q.,Mississippi State University | Chen G.,DCM Research Resources, LLC
Journal of Applied Remote Sensing | Year: 2012

In our previous work, we proposed a joint optical flow and principal component analysis (PCA) approach to improve the performance of optical flow based detection, where PCA is applied on the calculated two-dimensional optical flow image, and motion detection is accomplished by a metric derived from the two eigenvalues. To reduce the computational time when processing airborne videos, parallel computing using graphic processing unit (GPU) is implemented on NVIDIA GeForce GTX480. Experimental results demonstrate that our approach can efficiently improve detection performance even with dynamic background, and processing time can be greatly reduced with parallel computing on GPU. © 2012 Society of Photo-Optical Instrumentation Engineers.

Yang H.,Mississippi State University | Du Q.,DCM Research Resources, LLC | Chen G.,DCM Research Resources, LLC
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | Year: 2012

A particle swarm optimization (PSO)-based dimensionality reduction approach is proposed to use a simple searching criterion function, called minimum estimated abundance covariance (MEAC), requiring class signatures only. It has low computational cost, and the selected bands are independent of the detector or classifiers used in the following data analysis step. With such an efficient criterion, PSO can find a global optimal solution much more efficiently, compared with other frequently used searching strategies. Its performance is evaluated by support vector machine (SVM)-based classification for urban land cover mapping. In our experiments, SVM classification accuracy using PSO-selected bands is greatly higher than using all of the original bands or dimensionality-reduced data from principal component analysis (PCA) or linear discriminant analysis (LDA). In addition, the improvement on SVM accuracy can bring out even more significant improvement in classifier fusion. © 2012 IEEE.

Agency: Department of Defense | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 748.75K | Year: 2010

In phase 1, we have developed a holistic approach to systematically employ distributed sensor management techniques for large scale networks, with technical innovations on network utility optimization, efficient distributed computational methods, and robust and scalable information propagation. To attain predictive and responsive space situation awareness (SSA), the DCM team design, Awareness-based Compressed Data Collection and Dynamic Resource Management (ACDC-DRM), jointly addresses compressive and active sensing with network resource management in a unified manner, which represents an emerging data collection paradigm that is important, and indispensable in many cases, to the success of resource-constrained, large-scale sensor networks monitoring dynamic and/or localized phenomena. Our phase 1 work consists of: 1) compressive and active sensing algorithms for efficient data acquisition and reconstruction of a large-scale complex target-field, 2) in-network cooperative multi-sensor searching and tracking algorithm with information-based awareness metrics; and 3) game-theoretic dynamic sensor resource allocation approach for intelligent targets. In addition, a prototype based on open-source software has been implemented to illustrate the algorithms. In Phase II, we plan to coordinate with government POCs, academic researchers and industrial partners on research and development, as well as updating various open-standard database collection routines. We will also refine the key algorithms in our ACDC-DRM design, extend the system capability using theoretical performance guidelines quantified under various operating conditions, and develop an executive prototype for realistic network scenarios. BENEFIT: The first potential commercialization application is JSTAR and SAFIRE program. The second potential application is other DoD application such as ARL. The third potential application is AEGIS program and other programs where LM is the Prime Contractor. Lockheed Martin MS2 is prime contractor on the Aegis weapon system, the Littoral Combat Ship, and C2 lead for the DDG-1000 program. We have developed a strong and realistic plan to transition our technology to their programs. During the first stage of Phase II, LM MS2 will study and quantify through simulation and analysis how the ACDC-DRM can enhance the performance of their network centric platform. Assuming a successful Phase II, during the Phase III LM MS2 will build on the results of the Phase II work to implement and test the technology in real-world systems. Beyond the AEGIS first application, the innovations we are developing will improve situation awareness, planning, decision support for many military applications and we will aggressively pursue these other applications. As a metric of success, the technology is also applicable to commercial systems. Our target application will focus on disaster management, intelligent air traffic control system, and network defense.

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