One-dimensional Distance As Target Recognition And Data Fusion Algorithm

Since High Resolution Range Profiles (HRRP) could perfectly reveal the distribution detail of target in radial distance, recently, more attention is paid on HRRP. Meanwhile, data fusion of multi-sensors could improve the capabilities of systems in detection, recognition, classification and decision-making based on the complementary of various sensors. Thus, it also becomes a key research issue and widely applied.This thesis studies not only the technology of recognition of radar targets based on HRRP, but also data fusion of multi-sensors. The main points and the creativeness are shown as following:1,Target recognition HRRPFirstly, the scattering point model of HRRP is presented, which proves that HRRP could be taken as an effective feature vector to use in recognition of radar targets. Then, a solution of detection and data selection of targets is provided for the HRRP acquired in practical environment which is heavily effected by the complex radar clutter. Meanwhile, through detection of the HRRP, multi-targets are distinguished and false targets are filtered out, and multiple range-cells of targets are accurately extracted. Use a new sliding window detection algorithm to extract precise objectives. Furthermore, four common features of targets detection for HRRP are analyzed and identified by BP Neural Net.2,D-S algorithmAn optimization algorithm to solve the multi-sensors conflict problem of DS evidence in target identification system is presented. It utilizes the features of the sensor to estimate the confidence function. It adopts the confidence function to redistribution the conflict to make the algorithm more reasonable. Finally, simulation experiments results show that the new fusion algorithm can get a better fusion result in highly conflict situation.Finally, Summaries are made and the priorities and direction of future research are prospected.