Research On Algorithms Of Laser Detection And Tracking System

With the rapid development of military technology in modern warfare,more and more unmanned aerial vehicles and precision-guided weapons are used in military operations.Therefore,the study of detection and tracking systems for enemy objects has important practical significance.The object tracking and detection recognition technology in the laser detection system is the core data processing technology of the system.In this thesis,the algorithm of laser detection and tracking system is studied,aiming to improve the accuracy and efficiency of object detection and tracking,this thesis also builds a software terminal of laser detection system to realize the human-computer interaction function of the system.Firstly,the basic structure of the laser detection and tracking system is built,and the working principle of the system is introduced and analyzed.Then the tracking algorithm of the system is studied,the Kalman filter algorithm is selected to predict the object tracking,and the system model and filter model are established.On this basis,several common object dynamic models are analyzed.Considering the characteristics of laser detection and tracking system,the Constant Velocity dynamic model and Constant Acceleration dynamic model of the object under this system are established.At the same time,in the two scenarios where the object in the system is approximately uniformly moving and approximately uniformly accelerating,simulation experiments are respectively performed on the Kalman filter algorithm of the Constant Velocity dynamic model and the Constant Acceleration dynamic model.The analysis shows that the Kalman filtering algorithm of the Constant Acceleration dynamic model can accurately filter and track when the target is moving at approximately uniform speed and uniform acceleration,and can be better adapted to the actual engineering application of the laser detection system,so the Kalman filtering algorithm based on Constant Acceleration model is used to further improve the tracking effect of the system.Aiming at the problems of the traditional laser detection system,such as slow object detection speed in the initial stage,easy to lose the object due to the influence of obstacles in the environment,and unable to effectively identify the authenticity of the object.This thesis proposes a solution to assist the laser detection system to detect and identify the object by using image object detection algorithm.The image object detection algorithm based on deep learning is effectively applied to the laser detection system,so as to solve the above problems and improve the detection and tracking efficiency and reliability of the system.At the same time,considering the practical application of the laser detection and tracking system,in most of the images captured by the system from the air,the size of the object is small,but many object detection algorithms cannot identify small objects.This thesis solves the problem well by using the depth feature aggregation pyramid object detection network,and verifies the effectiveness of the algorithm through experiments.The thesis also designs the implementation scheme of the system,using Python language and Py Charm tool to develop a laser detection tracking system's software terminal,the user information in the system is stored through the SQLite database,which improves the performance of data management of the system.The software terminal mainly includes login self-test module,image detection module,object reconnaissance system module and data processing module.By using unit testing and integration testing methods to test each module of the terminal,it is verified that the functions of the software system are normal,and it has good robustness and reliability,and it can realize real-time processing of data and intuitive display of status information.The software terminal is simple and efficient in operation.Users can easily monitor and deal with the object in real time through the system software terminal,which improves the human-computer interaction experience of the software terminal and meets the requirements of practical application of the new intelligent laser detection system.