Model of multi-sensor data fusion and trajectory prediction based on echo state network

Radar receives more and more attention as an important means of access to information, and multi-sensor data fusion and track prediction become a new discipline. Compared with single radar, multi-radar system has the advantage of improving reliability of the system, enhancing the system when coverage, etc. However, this technology faces new problems when utilizing information in the complex environment. In this paper, we used the nearest data association algorithm (NNDA) to extract tracks from multifarious radar data and three spline interpolation method to make different measuring data track registration to the unity of time axis. Through the same period of fuzzy track correlation, we realized the same target track of extraction, calculated the relative radar accuracy by using the least square fitting, and applied radar tracking precision to be the integration of the weighted average method through the fusion of track-to-track reference. Finally, based on the knowledge of neural network technology, we used echo state network (ESN) to predict data, as ESN network training algorithm is very good in effectively solving nonlinear dynamic system specifically of uncertainty model. Through simulation test, we concluded that the multi-sensor data fusion and trajectory prediction model precision accuracy is 43.18 m for uniform motion model; for a sudden turn or variable targets, precision accuracy is 122.7m; for complex moving targets, precision accuracy is 165.3m.