Agile Sensing Research

 

 

Sensors are ubiquitous in today’s technology products and systems. From power plants to medical devices, navigation to safety, sensors are increasingly important in many aspects of our daily lives. Spurred by advancing device technologies, highly advanced, agile sensors are emerging as a next-generation technology for many applications, such as surveillance, medical imaging and structural health monitoring. Profs. Cochran, Papandreou-Suppappola and Morrell founded the laboratory for agile sensing research and have been working to integrate sensing and processing with a special emphasis on new algorithms that exploit the agility of emerging sensor systems. The lab has received significant research funding from several DoD agencies including two Multi-Disciplinary University Research Initiatives (MURI), which are providing $7M in research funding over five years. The faculty’s research on these projects entails collaboration with Raytheon, AFRL, NRL, NASA, Princeton, Purdue, Harvard, the University of Maryland and the University of Melbourne. Other noteworthy collaborative activities in the sensing area include multimodal sensing with AME and analysis of ion-channel sensor signals.

 

 

Capabilities

  • Myopic and non-myopic sensor scheduling algorithms.

  • Algorithms for waveform design and clutter suppression for agile sensing.

  • Multi-channel detection and estimation algorithms.

  • Real-time sensing using acoustic, radar, imager sensors.

  • Structural health monitoring sensing and processing.

 

Publications

  • K. Ghartey, A. Papandreou-Suppappola and D. Cochran, “Applications of time-frequency techniques in multi-channel signal detection,'' IEEE Transactions on Signal Processing, Volume 54, Issue 9, Page 3353-3362, September 2006.

  • S. Sira, A. Papandreou-Suppappola and D. Morrell, “Dynamic configuration of time-varying waveforms for agile sensing and tracking in clutter,'' IEEE Transactions on Signal Processing, Volume, Issue, Pagein print.

  • A. Chhetri, D. Morrell and A. Papandreou-Suppappola, “On the use of binary programming for sensor scheduling,'' IEEE Transactions on Signal Processing, Volume, Issue, Page in print.

  • A. Chhetri, D. Morrell and A. Papandreou-Suppappola, “Sensor resource allocation for tracking using outer approximation'', IEEE Signal Processing Letters, Volume 14, Issue, Page March 2007.

  • A. Chhetri, D. Morrell, A. Papandreou-Suppappola, C. Chakrabarti, A. Spanias, J. Zhang, "A Unified Bayesian Decision Theory Perspective to Sensor Networks," Proc. 2005 IEEE Mediterrean Conference on Control and Automation, Volume, Issue, Page 598 - 603, Month 2005.

  • V. Berisha, H. Kwon, A. Spanias, "Real-Time Collaborative Monitoring in Wireless Sensor Networks," Proc. IEEE ICASSP 2006, Volume, Issue, Page III-1120 - III-1123, Toulouse, May 2006.

  • S. P. Sira, D. Cochran, A. Papandreou-Suppappola, D. Morrell, W. Moran, S. D. Howard, and R. Calderbank, “Adaptive waveform design for improved detection of low-RCS targets in heavy sea clutter,’’ submitted to the IEEE Journal on Special Topics in Signal Processing, Volume, Issues, Page, Month 2006.

 

Grants

  • AFOSR: “MURI: A Multidisciplinary Approach to Health Monitoring and Materials Damage Prognosis for Metallic Aerospace,'' May 2006-June 2011, $2M, A. Papandreou-Suppappola and D. Cochran.

  • AFOSR: “MURI: Adaptive Waveform Design for Full Spectral Dominance,'' July 1, 2005-June 30, 2010, $1.12M, A. Papandreou-Suppappola and D. Morrell.

  • DARPA: “Waveforms for Active Sensing,'' Jan. 2006- July 2007, $200k, A. Papandreou-Suppappola.

  • DARPA: “Integrated Sensing and Processing, Phase I & II'' (Raytheon subcontract), July 2002 – March 2007, $450k, D. Morrell and A. Papandreou-Suppappola.

  • DARPA: “Sensor Topology for Minimalist Planning’’ Oct. 2006 – Sept. 2010, $600K, D. Cochran.

 

Awards

  • IEEE Phoenix Section Outstanding Faculty for Research award for research contributions in Sensor Signal Processing (D. Morrell and A. Papandreou-Suppappola, 2003), Secretary of Defense Medal for Exemplary Public Service (D. Cochran, 2005).

 

Website

 

Curriculum

  • Modeling and Performance Analysis (EEE555), Filtering of Stochastic Processes (EEE581), Time-frequency Signal Processing (EEE505).

 

Past Doctoral Graduates

  • H. Shen

  • Y. Jiang

  • A. Chhetri

  • D. Sinno

  • S. Han

  • A. Clausen

  • M. Brack

 

Facilities

  • The LASR computing cluster is comprised of a set of Intel x86 based machines running Linux and sharing resources on the network. Presently, the cluster has five 3 GHz Intel Pentium D processor dual-core machines, two 3 GHz Intel Pentium 4 processor single-core machines, each with 2 GB RAM, and two new computers with 3.73 GHz Intel Xeon HT dual-core processors and 4GB RAM each. Facilities provided by the cluster include secure remote login, file transfer, application launch, and real-time status monitoring ability. The cluster is equipped with an implementation of Message Passing Interface (MPI), a standard for parallel computing which provides a set of libraries which can be used with C or FORTRAN for parallel programming.