A least square algorithm with covariance weighting for computing the translational and rotational errors between two radar sites

Sharing of radar track data between sites greatly enhances the radar coverage at very little cost in assets. By linking all the radars in a battlefield or a task force, a complete picture can be realized that supports faster response time and is robust against jamming, ECM, and DECM. In order for these benefits to be realized a good algorithm must transform tracks between sites without introducing large errors. This article describes a novel least square algorithm with covariance weighting (LSC) for computing translational and rotational (gridlock) errors between two radar sites that share common tracks. Simulations show that this technique reduces the gridlock errors by a factor of six over least squares algorithms with no covariance weighting.<>