Least Squares Estimation and Cramer-Rao Type Lower Bounds for Relative Sensor Registration Process

An important prerequisite for successful multisensor integration is that the data from the reporting sensors are transformed to a common reference frame free of systematic or registration bias errors. If not properly corrected, the registration errors can seriously degrade the global surveillance system performance by increasing tracking errors and even introducing ghost tracks. The relative sensor registration (or grid-locking) process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. In this paper, we consider all registration errors involved in the grid-locking problem, i.e., attitude, measurement, and position biases. A linear least squares (LS) estimator of these bias terms is derived and its statistical performance compared to the hybrid Cramer-Rao lower bound (HCRLB) as a function of sensor locations, sensors number, and accuracy of sensor measurements.