A two-stage optimization approach to the asynchronous multi-sensor registration problem

An important step in multi-sensor data fusion is sensor registration, namely, to estimate sensors' range and azimuth biases from their asynchronous measurements. Assuming the target moves in a straight line with an unknown constant velocity, we propose a two-stage nonlinear least square (LS) approach to this problem. More specifically, in stage I, each sensor first estimates its own range bias individually, and then in stage II, all sensors jointly estimate their azimuth biases. We show that both of the nonconvex LS problems can be solved to global optimality under mild conditions. Simulation results show that the root mean square error (RMSE) of the proposed approach is quite close to the Cramér-Rao lower bound (CRLB) when the level of the measurement noise is small.