<title>Comparison of registration error correction techniques for air surveillance radar network</title>

Abstract

This paper considers the effectiveness of different registration algorithms in an air surveillance radar tracking environment. The evaluation is based on real data collected from an operating radar network in Canada. The existing real time quality control (RTQC) routine in the network requires targets to be on both sides of the line between the radars; this type of distribution is usually not available in the area with low air traffic density. The least squares (LS) algorithm which solves for the least squares biases is an alternative to handle this problem. Two algorithms that are considered as being more rigorous in the literature are the maximum likelihood (ML) and generalized least squares (GLS). The ML algorithm is a solution of the registration problem that uses a calculated covariance matrix of the measurement noise. The GLS algorithm is a variation of the ML algorithm where only the variances of the measurement noise are used. Based on the real data analysis, the track separation after registration are identical for the LS and ML algorithm, with the GLS being close most of the time. The result of the RTQC algorithm is close when the radar returns are split evenly between the two sides of the line between the radars. If the data in unevenly distributed, then the RTQC algorithm provides less accurate estimates. Comparing their computational complexities, the RTQC and LS algorithm are very efficient while the ML and GLS algorithm are about hundred times slower in terms of computational speed.