Multiplatform-multisensor tracking with surveillance radars

Modern tactical surveillance systems benefit from a network of distributed sensors that fuse multiplatform-multisensor data into a single integrated picture. Data fusion is complicated due to inconsistent dimensionality between sensors. For example, some radar systems provide range, bearing, and elevation measurements, while other systems provide two-dimensional measurements in range and bearing only. This paper presents a method for generating three dimensional track states and error covariance matrices from two dimensional tracks from two or more surveillance radars geographically separated in WGS-84 coordinates. Equations are developed for estimating the state and error covariance for the single sensor and multiplatform-multisensor cases. For surveillance radars with multiple tracks, track-to-track assignment is performed using the likelihood of the three dimensional track state for each candidate track-to-track association. Results of Monte Carlo simulations show that the new technique is a practical and efficient method that improves track accuracy, covariance consistency, and hence, the value of netting surveillance radars.