Researches On Detecting And Tracking Dim Small Targets In Image Sequences

The detection and tracking of dim small targets in the optical and infrared images has been the subject of intense investigation for not more then two decades. The optical and infrared sensors are passive sensors, which are valued for their strong survival capacity in battlefields, but their maximum detection range is critical. The basic problem inherent to extent the detection range is the detection of small, low observable, moving targets in images and subsequent estimation of the target trajectories. The small spatial extent of these targets limits the information content of targets signature precluding the use of traditional pattern matching approaches, and the signal-to-noise ratio is sufficiently low that detection specifications cannot be met by an analysis of a single image frame. Most of approaches presented recently need high computation power are not Suit for real time implement. This dissertation address the problem of designing new efficient and effective image sequence processing schemes that will successfully detect and track small (point) targets with very low signal-to-noise ratio (SNR). After analyzing the difficulties of detection dim small targets, it points out that the small target signal energy should be integrated along its trajectory or in its extent. However, the position, size and velocity of an object in an image are initially unknown. These result in high computational requirements. An effective algorithm to detect dim small targets in images based on wavelet transform is presented here. The extent information is well be used to integrate the target抯 signal energy. Theoretical analysis and simulation results show that SNR can be successfully increased. A new dim point target detection algorithm based on Genetic algorithms (GAs) is proposed here. To a point target there is no extent information can be used. It is only can be detected by integrating signal energy along its path. How to find the objet path is a search problem. GAs is also a search problem. We induct GAs to the difficult problem of point targets detection. The code scheme and the operation of crossover, mutation and selection suitable for dim point targets detection are designed here. The simulation results show that it can detect and track targets computational efficiently with SNR<2. Eligible individual strings retained as a new genetic operation is proposed here. It avoids target tracks with very low SNR missing. It is in favor of GAs convergence. An algorithm, is also proposed for tracking low observable small (point) multi-targets. It combines multistage hypothesis tracking and intensity filtering to track moving multi- targets with SNR smaller than 2. At last, an algorithm, which combines Gas and truncated sequential probability ratio test, is presented here to detect and track point targets with low SNR. Another algorithm based on Wavelet transforms and Gas is proposed for dim small target detection.