Aerial Target Anti-interference Recognition And Tracking System

For aerial vehicle, the aerial target identification has been a very important issue. The optical far-field detector and near-field detector have been working from the vehicle launching to capture the target. The interferences of the two stages are not the same. This issue is written mainly for the target identification issues from interferences of the third-generation infrared far-field detector and laser near-field imaging detector.For the third-generation infrared far-field detector, the interferences they face are the interferences thrown by the target in different directions. First, this issue implements a far-field detector signal simulator, using the data generated by computer simulation to simulate the detected infrared signals of targets and interferences and to provide the data for the recognition algorithm of the far-field detector. And then, this issue achieves the correct target identification on the hardware platform designed based on the whole process of the far-field detector working and the results of machine learning, by controlling of each state, combined with wave-gate technology, track memory and analysis, pulse analysis techniques. Experimental results show that the algorithm has good robustness.For the laser near-field imaging detector, the main interference it faces is cloud. Because acquiring images of cloud is so expensive, using the computer simulation to obtain the cloud images becomes necessary. This issue tries using the Monte Carlo simulation method to obtain the cloud echo images based on the cloud modeled, and tests the model through literature research and experiments. For the high noise and multi-profile images laser near-field imaging detector obtain, this issue presents a target recognition algorithm based on Zernike moments which are more stable characteristics comparing with point and line features. A hardware implementation of Zernike moments calculation is given, and also the process of the image scaling, an simple image segmentation and a multi-scale object recognition. The algorithm tests show that method in this issue can obtain not only a higher recognition rate, but also a lower error rate.Based on the comprehensive study of far-field detector and laser near-field imaging detector anti-interference target identification, which greatly improves the probability of the vehicle hitting the targets, this work has an important theoretical and practical value.