Most of the literature pertaining to target tracking assumes that the sensor data are corrupted by measurement noises that are zero mean (i.e., unbiased) and with known variances (accuracies). However in real tracking systems, measurements from sensors exhibit, typically, biases. For angle-only sensors, imperfect registration leads to Line Of Sight (LOS) measurement biases in azimuth and elevation. In this project we propose a new methodology that uses an exoatmospheric target of opportunity seen in a satellites borne sensor's field of view to estimate the sensor's biases simultaneously with the state of the target. The first step is to formulate a general bias model for synchronized optical sensors; then we use a Maximum Likelihood (ML) approach that leads to a nonlinear least-squares estimation problem for simultaneous estimation of the 3D Cartesian position and velocity components of the target of opportunity and the angle measurement biases of the sensors (two in the present study). Each satellite is equipped with an IR sensor that provides LOS measurements (azimuth and elevation) to the target. The measurements provided by these sensors are assumed to be noisy and biased but perfectly associated, i.e., it is known perfectly that they belong to the same target. The sensor bias and the target state estimates, obtained via Iterative Least Squares (ILS), are shown, by the simulation, to be unbiased.