Dual On Board2D Radar And ESM Data Fusion Technology Reaseach

Modern Warfare will be faced with challenges such as the harshelectromagnetic environment and complex interference, densely distributed targetsand low altitude penetration, electromagnetic stealth, and so on. Therefore, relyingon a single sensor alone is difficult to achieve the target state estimation. Afterdecades of data fusion technology development, the sensor has no longer beenlimited to a fixed platform. As part of the system of modern warfare, Because of itshigh positioning accuracy, achieving precision strikes, and so on, Missile-borneradar has become an important research topic at home and abroad. This article willsystematically conduct the study of multiple targets data fusion techniques by2Dradar and ESM on the dual motion platform. In order to solve the nonlinear situationsuch as densely distributed targets, relative position rapidly changing betweentargets and platform. To solve the above problem, this article will do research on thetrack initiation, data association and track fusion.First discuss the problem of track initiation. Before formation of the track, thetargets are far from the sensor. It’s hard to obtain the target state estimation. SoBased on Fuzzy Closeness of Track Initiation Method is proposed. For2D radartrack initiation, track initiation method based on the angle and distancemeasurements is taken into account. For the ESM, track initiation method based onthe angle and attribute information is adopted. After the introduction of fuzzycomprehensive function model construction, target detection model constructionand fuzzy closeness calculation. Then the method is simulated and analyzed. Theresults show that the method has a good track initiation effect.Secondly, discuss the problem of data association problem. Data association isone of the most important technologies in the data fusion technology. It is dividedinto trace points and the track association, the track and the track association. Afteranalyzing problems of the points and the track association, for the points and thetrack association, this paper proposes a full-near-neighbor point and trackcorrelation algorithm. For the2D radar, use angle and distance measurements to thepoint and the track correlation method. For the ESM, adopt point-based integratedinformation to the point and the track correlation method. After discussing the fuzzyfactor set construction, membership function selecting and fuzzy factorsdetermining, This paper also proposes fuzzy track correlation algorithm. Thesimulation results show that this method has a high correlation accuracy.Finally, discuss the track fusion problem. Bring Kalman filter to solve linear state estimation theory, and for the nonlinear state estimation use UKF and SRF.With the background of the subject, conduct simulation to compare the performanceof filters. The results showed that the UKF is more effective to solve the stateestimation problem for the subject of this paper. This article also do research on thecentralized state estimation for dual-platform2D radar, the centralized stateestimation for dual-platform ESM and the heterogeneous sensor state estimation fordual-platform distributed problem. Finally conduct simulations about dual-platform2D and ESM for multi-target track fusion technology. The simulation’s results showthat, when the targets are densely distributed and the relative motion betweentargets and platform changes quickly, this method can let the targets be welltracked.