Research On Target Recognition Algorithm For Laser Imaging Radar

With the development of laser technology, laser imaging radar was gradually gained wide application in complicated battlefield of modern warfare. The target recognition technology for laser imaging radar has become a hot issue at home and abroad. This dissertation researches on the target recognition technology for laser imaging radar range image, which includes the noise reduction, the image segmentation, and the target classification and recognition. The main contributions of the dissertation are demonstrated as follows:In the introduction, firstly, the research background and significance of this dissertation are set forth. Then the research state and problem of laser imaging radar target recognition are summarized. Finally, the main work of this paper is introduced.In chapter 2, the noise reduction of range image is studied. Based on the formation mechanism and noise characteristic of rang anomalies in range image, three types of noise reduction algorithm is proposed. (1) A range anomalous suppression algorithm based on differential sorting and adaptive median filter is proposed. Firstly, the sorting and difference are carried out for pixels with big filtering window. Then, the pixels corresponding to the maximum continuous difference value below threshold are regarded as normal range values. Finally, a self-adaptive median filtering is carried out for range anomalies. (2) A range anomalies reduction algorithm based on parameter optimization adaptive median filter is proposed. The filtering value is achieved by calculating the difference between present pixel and median and comparing the difference with two thresholds. (3) A range anomalies reduction algorithm of laser imaging radar range image based on surrounding criterion is proposed. Firstly, the noise is detected based on surrounding criterion. Then using a combining median filter with weighted mean filter, the noise reduction is carried out on range image. The experimental results show that the range anomalies noises can be efficiently suppressed based on these three algorithms and the detail of object in range image is well protected.In chapter 3, the image segmentation of range image is studied. According to the functional limitations of classical edge detection operators in detecting the edge of range image and segmenting the range image, two improved algorithm are proposed. (1) An edge detection method of depth region based on Canny operator is proposed. Firstly, the initial segmentation is carried out by using sort differential operator. Then, the edge detection and correction is carried out based on Canny operator. Finally, the contour edge of object is extracted completely. (2) An image segmentation method based on edge control region growth is proposed. Firstly, the initial pixel of edge region growth is automatically selected, and then the region growth process is dominated based on object contour edge. Finally, the image segmentation of integral target region is realized. Experimental results show that combining these two methods can gain good image segment effect of laser imaging radar range image.In chapter 4, the target classification and recognition of range image is studied. According to two technical approaches (image features and model matching), some recognition algorithms of range image are proposed. Based on image features, four recognition algorithms of range image are proposed. (1) A recognition algorithm of range image based on singular value feature is proposed. According to the relationship between recognition rate and the number of singular value feature, important features are obtained. Then, target recognition is carried out using the support vector machines of optimal parameter. (2) A recognition algorithm of range image based on wavelet transform is proposed. Firstly, two dimensional wavelet transform of range image is carried out. Then features are extracted from approximate part and detail part. Finally the target is identified using the support vector machines of optimal parameter. (3) A recognition algorithm based on moment feature is proposed. Firstly, Hu moment and Zernike moment features of range images are extracted separately. Then the target is identified using the RBF neural network. (4) A recognition algorithm based on texture feature is proposed. Thirteen texture features based on Gray Level Co-occurrence Matrix are extracted from range image. Then the target is identified using the RBF neural network. According to model matching principle, a recognition algorithms of range image is proposed, including three major steps: rectangle estimation, model matching and similarity measure. (1) A SLEC rectangle estimation algorithm is proposed. Firstly, a binary image is got by rasterizing point cloud and the internal holes are filled through morphological closing operation. Then, the edge is extracted based on Sobel operator and angle parameters are got by Hough transform. Finally, the target direction and size estimation are got by searching the smallest rectangle with constraint of the boundary line angle. (2) A modified ICP algorithm is proposed. The standard ICP algorithm only matches point to point, while the modified ICP algorithm is expanded to matching point and surface. The accurate matching is carried out a rigid body transformation which searches the minimum euclidean distance between the point cloud and CAD models. (3) To measure the similarity between target point cloud and CAD models, a measurement algorithm is proposed which using the normalized euclidean distance as similarity measure. The target recognition is based on this similarity measure. Experimental results show that these recognition algorithms have good recognition results.Finally, the innovation of this paper is summarized. The last chapter also discusses the future work to be further researched.