Research On Several Key Technical Problems In Surveillance Radar Netting

The intent of this dissertation is to investigate several key technical problems in surveillance radar netting.In the first chapter, there are brief reviews and comments of the research background as well as the present situation of the subject, and then the characteristic feature and key technical problems in radar netting are introduced.The second chapter studies surveillance radar netting and sensor management of data fusion. Firstly, there are deductions of target detection probability, false alarm probability and relation formula of radar-target range. Then the conceptions of station distribution efficiency and distribution principle are proposed and defined. Based on the conclusion we develop an optimization model of surveillance radar netting. Also, a real genetic algorithm based on multi species is proposed for the complicated surface optimization in radar netting model. This algorithm has merits of less computation, higher search veracity, rapider convergence, stronger ability against precocity, and can search multi global optimal points at the same time, and also suits for parallel optimal searching. By applying the station distrbution principle to point targets and targets with given RCS, simulation researches are performed on optimal deploy problems of ground-based surveillance radar net, which contains both depth deploying and front deploying radars with same power range. The simulation shows that this method is feasible and effective. Lastly, based on the radar netting model, simulation calculations are done for the radar choosing under the condition of given radar net cover region, radar station distribution and minimal target detection probability. On the aspect of data fusion management, firstly, a data fusion management network with complicated topological structure is proposed, and the key problems for the network realization are analyzed. Then data fusion subnet performance cost function is proposed and defined. On this basis optimization rule for data fusion subnet size choosing is proposed, and the simulation test is performed with satisfactory results.The third chapter is about characteristic features of data fusion in surveillance radar netting. First, there are rigorous equations for fusion weight calculation, data coordinate transformation and coordinate transformation in detection mean square error. Then considering the system error produced by radar station distribution, radar net system error-correction method based on LMS is proposed. To meet the requirement of target tracking in surveillance radar net, an improved adaptive kalman filtering in tracking calculation is also proposed, as well as an SWLMS filtering algorithm for varied applications. The validity of the presented algorithms is demonstrated with simulated data at last.The fourth chapter focuses on the target recognition in surveillance radar netting. Firstly, existence problems of target recognition based on low-resolution radar and prior target RCS curve are examined. Then on the basis of information intelligence quotient, we develop a new target recognition method, which matches the detective data and RCS sample curve though feasible probability. The validity of the presented method is demonstrated with simulated data at last.The fifth and last chapter concludes the current trends and future outlook in the subject. In particular, some significant and valuable problems of multisensor radar netting are pointed out for further research.