Joint source localization and sensor position refinement for sensor networks

Modern localization systems/platforms such as sensor networks often experience uncertainty in the sensor positions. Improving the sensor positions is necessary in order to achieve better localization performance. This paper proposes a joint estimator for locating multiple unknown sources and refining the sensor positions using TOA measurements. Rather than resorting to the traditional iterative nonlinear least-squares approach that requires careful initializations, the proposed estimator is algebraic and computationally attractive. The small noise analysis shows that the proposed estimator is able to attain the CRLB performance for both the unknown sources and the sensor positions. Simulations support the efficiency of the proposed estimator.