Robust Estimation for Ship-Borne Radar Detecting Biases

An important prerequisite for successful multi-radar integration is that the data from the reporting radars are transformed to a common reference frame which is free of systematic or registration errors. According to the ship-borne radar data processing, the types of bias are divided into four main categories: radar measurement biases, ship-position biases, attitude biases, and baseline transform biases. In this chapter, we present an algorithm which uses detecting data for estimation of equivalent biases. Our approach is unique for two reasons. First, we explicitly avoid the individual biases and instead use equivalent biases modeling the four main class biases, which leads to a highly nonlinear bias model that contains 12 unknown parameters. Then, we use the singular value decomposition (SVD) within least-squares estimator to automatically handle the issue of parameter observability. Finally, according to two different simulation scenes, we demonstrate that our algorithm can improve track accuracy, especially for ship-borne radar.