Multi-sensor data fusion: An unscented least squares approach
Jemin George, Lance Kaplan · International Conference on Information Fusion · 2011
This manuscript provides an approach to solve the nonlinear least squares problem that arises in decentralized fusion. Even though almost all sensor noise can be modeled as additive noise, the additive nature of the measurement noise is lost when the signal is processed at the sensor node. The proposed approach employs the unscented transformation before the estimation problem at the central node is posed as a nonlinear least squares problem. Numerical simulations indicate that the proposed unscented transformation based approach yields desired results.