Collaborative Sensor Registration without a Priori Association

Sensor registration is an important prerequisite for successful multi-sensor data fusion. In this paper, we consider a cooperative sensor registration scenario that the Precise Location Messages (PLM) can be received by the tracker periodically from cooperative targets through wireless data link and utilized to estimate sensor systematic biases. A 2-D sensor registration algorithm is presented to jointly estimate the site location bias and the measurement bias without a priori knowledge of track-to-track association. At first, a point set is obtained by mapping all possible pairs of sensor and PLM measurements. Next, a credit function is defined as the arithmetic mean of the likelihood function of these points. Since the registration biases can be estimated by finding the maximum credit point, a two-step searching algorithm is proposed to jointly estimate location and measurement biases. Its statistical performance is compared to the hybrid Cramér-Rao lower bound (HCRLB).