Studies On Missile Guidance Using Fly's Ommateum Technology

With the development of war, the requirement on information processing techniques for precision guidance is becoming higher and higher. ATR has become very important, this technique imitate human visual system. So this paper does certain researches on ommateum and some ATR techniques are proposed in this paper. As a real visual system existing in nature, ommateum not only have the same function as other visual systems, but also have its special feature. So research in this paper on ommateum contain two points, first researching ommateum like other normal visual system, considering contents including preprocessing of IR images, target image segmentation, feature extraction and target classification; second researching ommateum's special feature. Based on researching on ommateum"s special feature, a multi-mode guidance method is proposed in this paper. The main achievements of this paper are as follows:1 , The preprocessing of IR image: first introducing traditional methods on preprocessing of IR image, second emphasizing on introducing and summarizing lateral inhibition theory and application that predecessors had researched. This contains lateral inhibition theory's mathematics model, stability criterion, time domain and frequency domain feature, the function of enhancing the image's contrast and the object's frame and other functions applying in image segmentation.2 , Segmentation of the IR image: First systemic introducing image segmentation's model and classification, second emphasizing on researching image segmentation using best entropy or fuzzy entropy based on genetic algorithm . last a method for multi-object image segmentation avoiding searching for threshold is proposed, that is simple, stability and has certain self-adaptive and robust.3, Feature extraction: first introducing traditional feature extraction methods, then multi-channels feature extraction methods is proposed. One method is multichannels based on. frequency using wavelet theory, another is multi-channels based on gray. The first method is based multi-scale decompression using wavelet theory, it extract gray feature from each channel to form feature vector, it can distinguishobjects, but this method not have invariant property when object rotate. So the second method is proposed, it has not the same shortcoming, the method is based gray multi-channel. In this method, gray is partitioned into gray regions and feature is extracted in each regions and features form vector.4, IR object's classification and recognition: because of various factors in the process of feature extraction, the feature extracted is polluted certainly by noise, that the feature has certain uncertainty. This paper emphasizes on researching how to classify object based on feature vector having uncertainty. First researching on D-S evidence theory, .Combination Rules of Evidence Theory, Yager combination rules and this theory's shortcoming. Then a new method for object classification based on multi-feature is proposed, in this method basic probability assignment and combination rule is resolved. When the evidences have conflicts, D-S combination and Yager combination can't resolve this thing, the new method can resolve this conflict question.5 , Multi-mode guidance: Firstly, a idea about visual system is proposed based on researching on ommateum's structure and function. The idea is that: one ommateum can get object 3-D information in the space, not using two ommateum. Based on this point, a multi-mode guidance method is proposed, this method contains two point-detectors and one image detector. One point-detector imitates facet in ommateum, image detector imitates one whole ommateum, and it's visual function. Object's 3-D information is got using two point-detectors. In the last, the object detection question about multi-sensor's data fusion is researched.