Research On Detection For Small Dim Targets And Tracking For Multi-sensor Data Fusion

With the continuous appearances of complex multisensor large-scale systems, multisensor data fusion technique has attracted comprehensive attention. Studied with quantities of manpower and material resources in many countries, multisensor data fusion technique has been a very active field of processing multiple source information effectively. In western developed countries, some military data fusion systems have been researched and exploited. The study of data fusion in our country is still at a relatively low level, especially in complex environment, some correlative techniques in data fusion lack veracity and adaptiveness. Three important problems in date fusion, multisensor tagert detection, target tracking and recognition, are discussed in this dissertation. For multiple sensors multiple targets detection and identification environment, dim small targets are always in complex background, and the multiple information association, fusion, and tracking is on the premise of low SNR, dim small targets detection. The better results of dim small targets detection for each sensor of system, the better ability of fusion and tracking. Thus before multiple sensors multiple targets tracking and fusion, the detection methods for low SNR, dim small target in the complex background is researched first in this paper.The main research and innovation contents are as following:Firstly, target detection in infrared image is reseached. With the problem of the small target detection in low SNR and sky-sea background conditions, the paper proposed a method which based on the adaptive filter with Gauss function.This method is used in the sub-image which was obtained by wavelet analysis in the infrared image in order to restrain the noise. According to the trait of the wavelet analysis, we multiply the coefficient of the LL sub-image,HL sub-image and LH sub-image as the new LL sub-image, then use this coefficient by conversed wavelet transform. The results in the aspects of the mean, the standard deviation and the SNR of the image were better. Then, we found the threshold adaptively to segment the target in the image according to the SNR value. The simulation experiment results show the method is prone to realization in engineering and satisfies the meet of real time.Secondly, for multi-sensor multi-target tracking, data association is one of important problems, and is the precondition of data fusion. Two kinds of data association conditions - homogeneous sensors and heterogeneous sensors are reseaerched in this thesis. Consindered of data association for homogeneous sensors, the multiple dimensions assignment algorithm is used. This algorithm is NP-hard for S≥3, so it is transformed to a combination optimization problem and the Particle Swarm Optimization algorithm is used to solve this. Moreover, the measurements are restranited, then the solution can be found faster and more effective, and then data association problem is solved; Consindered of data association for heterogeneous sensors, a data association method of using two kinds of information from radar and ESM is proposed. It is based on the Fuzzy C-means algorithm, and dymatice information and characteristic information are used at the same time to cluster, so that the data association is enhanced.Thirdly, the problem of target track fusion was researched. A fuzzy theory based adaptive data fusion algorithm is used. The feature extraction was made for the filter data from two sensors and then the fuzzy illation was done under certain subordinate functions and rules, thereby a coefficient was got respinse to the practical conditions, and fusion results enhance the precision.Finally, the core problem in using fuzzy integral for decision fusion is to determine the fuzzy densities. According to shortage of the algorithm through that the fuzzy densities are determined only by aprior static information, a method of determining fuzzy densities adaptively is presented, which uses the apriori static information of the training samples and supporting reliability of each sensor's information. The simulation experiment results show that due to real-time update of fuzzy densities through the method of determining fuzzy densities adaptively using supporting reliability of each sensor's information, the effect of inaccurate information on fusion is depressed, and the reliability and robustness of multi-sensor system are enhanced.