Multi modal, multi sensor fusion for detection, classification and tracking of humans/vehicles
ARL Award DAAD19-01-02-0008, MS-07-08A, 2006-2007
Decentralized data fusion for multi-sensor multi-mode multi-target detection, classification, and tracking
ARL Award DAAD19-01-02-0008, MS-06-01, 2005-2006
Acoustic sensor network design for position estimation project developed a theory to predict the localization performance of randomly distributed sensor networks consisting of various sensor modalities when only a constant active subset of sensors that minimize localization error is used for estimation. The characteristics of the modalities include measurement type (bearing or range) and error, sensor reliability, FOV, sensing range, and mobility. The project showed that the localization performance of a sensor network is a function of a weighted sum of the total number of each sensor modality; this weighted sum is independent of how the sensor management strategy chooses the active sensors. The project also combined the utility objective with other objectives, such as lifetime, coverage and reliability to determine the best mix of sensors for an optimal sensor network design: the Pareto efficient frontier.
Joint acoustic-video fingerprinting of vehicles project addressed vehicle classification and mensuration problems using acoustic and video sensors. In the resulting publications, we showed how to estimate a vehicle’s speed, width, and length by jointly estimating its acoustic wave-pattern using a single passive acoustic sensor that records the vehicle’s drive-by noise. We also showed joint fusion results with video using vehicle classification and mensuration based on silhouette extraction and wheel detection. Vehicle speed estimation and classification results are provided using field data.
Joint acoustic-video object tracking project resulted in a Bayesian framework for target tracking using multi modal information. The project developed a joint acoustic video algorithm using particle filters for detecting and tracking multiple vehicles and also successfully extended the Bayesian framework for joint acoustic radar and radar seismic algorithms.