Dynamic Data Driven Sensor Network Selection and Tracking

Abstract

Abstract The deployment of networks of sensors and development of pertinent information processing techniques can facilitate the requirement of situational awareness present in many defense/surveillance systems. Sensors allow the collection and distributed processing of information in a variety of environments whose structure is not known and is dynamically changing with time. A distributed dynamic data driven (DDDAS-based) framework is developed in this paper to address distributed multi-threat tracking under limited sensor resources. The acquired sensor data will be used to control the sensing part of the sensor network, and utilize only the sensing devices that acquire good quality measurements about the present targets. The DDDAS-based concept will be utilized to enable efficient sensor activation of only those parts of the network located close to a target/object. A novel combination of stochastic filtering techniques, drift homotopy and sparsity-inducing canonical correlation analysis (S-CCA) is utilized to dynamically identify the target-informative sensors and utilize them to perform improved drift-based particle filtering techniques that will allow robust, stable and accurate distributed tracking of multiple objects. Numerical tests demonstrate the effectiveness of the novel framework.