Research On Infrared Dim And Small Target Detection In Clutter Environment

Infrared Surveillance Systems (IRSS) are electro-optical detectors which detect, recognise and track targets by receiving their Infrared emission passively. The IRSS which, either working alone or as part of radar surveillance suite, search and scout military targets, can provide target information to fire control system of weapon platforms. In general, IRSS has passive detection and invisibility, high resolving ability, fine anti-jamming ability, small size, lightweight and high maneuverability. For these advantages, IRSS bcomes an important component of the nation defense system, and obtains wide attention and energetic cooperation in recent years.Infrared dim and small target detection and tracking in clutter environment is a key technique in IRSS. Efficient target detection and tracking algorithms for low signal-to-nose ratio (SNR) environment can result in longer alarm range of IRSS. It is of great significant to increase response time of fire control systems and survival probability. But when target is far away from sensors, the target is point-like and the image SNR is very low. The target signal amplitude is weak relative to the noise amplitude and lacks distance information. Single-frame target detection is unable to obtain the desired detection probability and false alarm rate because there are short of information about shape, size, texture and other features of targets. Because of target's regular and continuous movement, multi-frame target detection in image sequences which employing temporal information to confirm real targets becomes an effective solution.To improve detection ability of IRSS in available devices, in-depth and comprehensive research work has been done on dim and small target detection and tracking in an imager based on staring Infrared focal plane array (IRFPA) in this dissertation. Small target's imaging process in staring IRFPA is discussed, and then the mathematical models are presented. The dim and small target's statistical characters including its size, shape, gray level, contrast and local correlativity between target and its ground are analyzed. Based on works above, a series of algorithms are present to detect dim and small targets based on scanning staring IRFPA, include: algorithm of Infrared image preprocessing based on finite adaptive neighborhoods to enhance dim targets and restrain noise, algorithm of background prediction based on mathematical morphology, algorithm of line-target constant false alarm ratio (CFAR) detection. To reduce further false alarm rate in single-frame target detection, a target re-affirm technique based on target local intensity distribution function matching is presented. Detection method based on multi-frame tracking is another advanced approach to high detective probability in clutter environment. After analyzing current track before detect (TBD) techniques, an optimized dim target detecting technique based dynamic programming is presented. The designed algorithm put forward a novel stage benefit function and publishment function based on target's inter-frame relativity and consistency. This paper designs a multiple hypotheses tracking scheme for low altitude IRSS. The presented multitarget tracking (MTT) method adopts Kalman filter to predict new position of target, and a complete algorithm framework is brought forward for track intiation, tarck maintaining and track ending. In track maintaining, target's position, mean gray level, size and local SNR are taken into count, and a new data association technique based on multiple target characters is investigated. An emulator of dim and small targets detection and tracking is designed to confirm validity of methods. Results show that algorihms of target detection and tracking in IR image sequences have high clutter suppression ability, anti-target-missing ability and significant real-time processing ability.Based on results of theoretic analysis and algorithm simulation above, an efficient and real-time infrared digital signal processing model for low altitude IRSS is developed. Testing shows that algorithms in this dissertation are efficient and practical.