Moving Target Detection

Moving target detection has both a long history and novel approaches. In face of different objects, there are both theoretical study for a guide and application with particularities. Baesd on the theory that moving target detectability can be enhanced by joint space-time processing with the application of frequency features and targets' characteristics, we focus our attention on particularies of sonars' target detection using underwater acoustic techniques and vehicle detection using computer vision techniques.With the development of reducing noise technology and the advent of quieter targets, sonar faces up to the challenge of low signal-to-noise ratio detection while high detection performance is demanded by weapons's increasing range.Moving target detection is two of first and last of sonar' four main tasks: detection, loacalization, classification and tracking/motion analysis of targets, which is the base of other two functions. It is not only tightly related to transimission channel (environment) information but also the characteristics of targets.In the study of environment model-based processing,the thesis presents a signal enhancement method based on combination of the ocean acoustic propagation, measurement system and noise models.Furthermore,a signal enhancement algorithm is developed on the basis of the model-based identifer (MBID) implemented by the the augmented Gauss-Markov process and corrsponding extended Kalman filter. The experiment shows MBID can produce the enhanced pressure field at hydrophone array, provide modal domain representation of pressure (modal functions and horizontal wavenumbers) and target's bearing (plane waves), and have good adaptive ability and robustness against mismatch.In post processing after signal enhancement, the feature enhancement using signal micro-structure characterizing target features is exploited to improve target detectability. In view of the signal micro-structure knowledge, more attention is paid on analyzing the time-frequency and distribution characteristics of underwater acoustic signals. To detect and track weak signals, feature extraction is studied on the base of feature anlysis, and a Viterbi spectrum line tracker with double thresholds is exploited. The simulation shows that feature enhancement and extraction based on the signal micro-structure characterization are efficient.The algorinthms of the signal micro-structure extraction as a key part of feature enhancement are mapped into hardware platform to build a real-time target detection system by developing high speed and parallel processing ability of DSP chips of TMS320C40. That the system can stably track targets at lake test shows the signal micro-structure extraction is effective and can be implemented for engineering application.In contrast to the development of freeway, the existing intelligent transportation system (ITS) lags behind for its image acquiring and processing with low efficiency and low acuracy vehicle detection. The improvement of ITS depends not only on the development of hardware platform, but also fast and reliable algorithms for detectingvehicles. The thesis is to exploit key vehicle detection agorithms, and design a real-time vehicle detection system through the detection algorithm mapping.The exiting ITS has a low detection and high false alarm rates due to:l)less information of gray images for detection;2) lack background model adaptive to real background change; 3)existence of shadows. In color space, the thesis proposes a detection method for high speed vehicles with two background models, one statistical background model and another deterministic adaptive background model. After the underlying physics of shadows and their characteristics are studied, two shadow detection approaches are exploited, one blue wave band information approach and another deterministic non-modeled approach. The experimnet shows that it is effficient to detect high speed vehicles by means of background models and shadows can be correctly detected with shadow detection algo...