Accession Number:



Multimodal Sensing and Information Integration for Multiple Object Tracking

Descriptive Note:

[Technical Report, Final Report]

Corporate Author:

Arizona State University

Report Date:


Pagination or Media Count:



The funded research designed methods to improve multiple object tracking using measurements from multimodal systems. The methods combine sequential Bayesian filtering to estimate the time-varying parameters of physics-based models and Bayesian nonparametric modeling to infer and learn information directly from the measurements. Integrating these methods resulted in robust learning and increased performance when compared to current state-of-the-art methodologies. Multiple challenging scenarios were considered time-varying number of moving objects, unknown measurement-to-object associations, time-varying environmental conditions, and multiple statistically-dependent measurements.


Subject Categories:

  • Target Direction, Range and Position Finding
  • Statistics and Probability

Distribution Statement:

[A, Approved For Public Release]