Dynamic agent classification and tracking using an ad hoc mobile acoustic sensor network

David Friedlander, Christopher Griffin, Noah Jacobson, Shashi Phoha, Richard R. Brooks

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


Autonomous networks of sensor platforms can be designed to interact in dynamic and noisy environments to determine the occurrence of specified transient events that define the dynamic process of interest. For example, a sensor network may be used for battlefield surveillance with the purpose of detecting, identifying, and tracking enemy activity. When the number of nodes is large, human oversight and control of low-level operations is not feasible. Coordination and self-organization of multiple autonomous nodes is necessary to maintain connectivity and sensor coverage and to combine information for better understanding the dynamics of the environment. Resource conservation requires adaptive clustering in the vicinity of the event. This paper presents methods for dynamic distributed signal processing using an ad hoc mobile network of microsensors to detect, identify, and track targets in noisy environments. They seamlessly integrate data from fixed and mobile platforms and dynamically organize platforms into clusters to process local data along the trajectory of the targets. Local analysis of sensor data is used to determine a set of target attribute values and classify the target. Sensor data from a field test in the Marine base at Twentynine Palms, Calif, was analyzed using the techniques described in this paper. The results were compared to "ground truth" data obtained from GPS receivers on the vehicles.

Original languageEnglish (US)
Pages (from-to)371-377
Number of pages7
JournalEurasip Journal on Applied Signal Processing
Issue number4
StatePublished - Mar 15 2003

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Hardware and Architecture
  • Electrical and Electronic Engineering


Dive into the research topics of 'Dynamic agent classification and tracking using an ad hoc mobile acoustic sensor network'. Together they form a unique fingerprint.

Cite this