Target Tracking using Cognitive Radar and Foveal Nodes

The adaptive configuration of nodes in a sensor network optimizes the use of scarce network resources to improve target tracking performance. Moreover, the effective fusion of measurements from heterogeneous sensing nodes provides diversity in information on the state of the target for additional performance gains. However, the joint configuration of the heterogeneous parameters of nodes and the fusion of heterogeneous measurements for sequential estimation are challenging tasks. To tackle these challenges, a sequential Monte Carlo method is presented in this work that adaptively configures heterogeneous sets of sensing nodes and fuses heterogeneous data for target tracking. A simulation experiment is provided as an example application of the method to demonstrate the configuration of cognitive foveal and radar nodes and the fusion of heterogeneous data for accurate and efficient single target tracking.