Multi-source Radar Data Fusion via Support Vector Regression
Zhanchun Gao, Yu Xiang · 2019
Since the measurement error of surveillance sensors such as radar differs each other in the detection of the same target, it's necessary to fuse the multi-source radar data to estimate the true location of target and reduce the measurement error of radar. The key is to establish nonlinear regression model since the uncertainty of measurement error. In this paper, the Support Vector Regression(SVR) methodology was adopted to estimate the true location of target based upon the measurement results of multi-source radar. We uniquely identify a region by a sequence of radar id which means a target can be detected in this area by radars with id listed in the sequence. Different regression model was established in different region which are independent of each other. Since the coordinate system used by radar data and ADSB data is different, we mapped all the data into the same two-dimensional Cartesian coordinate system. In the same region, two regression models were established to estimate the values of aircraft on the x-axis and the y-axis. After we predict the x and y coordinates of the target, we convert the coordinates back to the WGS84 format.