Research On Image Fusion For Autonomous Control Of Attack Unmanned Aerial Vehicles (AUAV)

With the development of modern defense techniques, the abilities of autonomous control of attack unmanned aerial vehicles (AUAV), such as cruise missile, attack unmanned plane, urgently need to be improved, which can help to enhance the intelligence of AUAV. In order to realize the autonomous control of AUAV, the situation awareness based on image fusion is the first step. Because of the high complexity of battle environment, how to design an effective method of image fusion has become a problem with both great significant theoretical value and great practical value, which can improve the abilities of target recognition and threat elusion, and enhance the intelligent degree of AUAV.Image fusion can deal with the images and information in multiple dimensions, and form a result for decision making. Aiming to overcome the limitations of traditional image fusion that cannot realize the optimal fusion because of the mode of the fusion before the evaluation, the multi-objective optimization image fusion methods where the evaluation before the fusion are proposed and designed in this thesis. In this architecture, image fusion is divided into preprocessing, fusion and target location to realize the optimal image fusion. The main work and the creative contribution of this thesis are as follows:1) The system structure of autonomous control of AUAV based on image fusion is built. First the concept of autonomous control is defined and the autonomy level is classified; then the structure, components, functions of autonomous control system of AUAV are analyzed, the system flow of autonomous control based on image fusion, which are divided into three parts, i.e. situation awareness, dynamic replanning, and mission conduction, where situation awareness includes image preprocessing, image registration, image fusion, image recognition and target location, and dynamic replanning includes strategy selection and real-time route planning.2) The methods of image fusion preprocessing based on DWT (Discrete Wavelet Transform) and FDCT (Fast Discrete Curvelet Transform) are proposed. Aiming at the shortcomings of current image denoising methods that can't realize the best denoising effect, the image denoising method based on multi-objective optimization is researched, the multiple criteria for image denoising are presented, the methods of image denoising based on DWT and Fast DCT are proposed. The experimental results show the two method can realize the Pareto optimal denoising effect, but the two methods have their advantages themselves, the speed of DWT methods is higher, while the effect of FDCT methods is better. On condition that the imaging environment of AUAV is invariable, the thresholds of denoising can be set a fixed value, which can meet the requirement of applications, and give the effective solutions in time. 3) A multi-objective optimization image fusion is proposed. The effective evaluation metrics is proposed from the view of image quality, the relation of the fused image to source images, and the relation of the fused image to the standard image. New conditional mutual information is proposed, which can avoid the information overloaded. The evaluation metrics are selected and designed according to the metric relativity and a certain selection rules. The multiple metrics are the optimization objectives, and the fusion parameters are selected as the decision variables and optimized by a multi-objective optimization algorithm. The fusion models in space domain and DWT domain are uniformed and simplified, two fusion rules are designed, finally the experiments for multi-focus image fusion, blind image fusion, multi-resolution image fusion, and color image fusion are conducted, and the methods and evaluation metrics of image fusion is designed according the characteristics of different images. Experimental results indicate that the fusion method based on multi-objective optimization is suitable for many types of pixel-level image fusion and can realize the Pareto optimal image fusion.4) The method of optimizing the decomposition levels for wavelet-based image fusion is presented and the selection of wavelet bases is analyzed. In order to realize the optimal wavelet-based image fusion, the decomposition levels and wavelet base selection are both important. A multi-objective optimization approach to determine the optimal number of decomposition levels is presented. In the experiments it is found that the optimal decomposition level is not a fixed value, but rather, changes with the characteristics of the original images. In general, fusion of images with larger resolution requires a higher number of decomposition levels. The experimental results also show that using the multi-objective optimization can effectively obtain the optimal number of decomposition levels make each metric be maximized. For there exist too many wavelet bases, the selection criteria of wavelet bases are given. In order to make more meaningful results, simplify the computation, and avoid the reconstruct distortion, the "Haar" wavelet is selected the wavelet base.5) A method of target recognition and tracking based on deformable templates in a fused image is proposed. First the advantage and challenges of deformable templates in target recognition and tracking are analyzed. Because the deformable template has formalized description parameters, it can define different targets in all kinds of shapes. Then different target templates are designed, and the energy functions are defined, the template matching is converted into single objective problem. The target recognition based on the deformable template is presented, which can reach the target matching through optimizing the energy functions, and the different methods of target location are analyzed. The target tracking based on the deformable template is also researched, and experimental results show that the method based on the deformable template can search, recognize, locate, and track the target quickly. 6) A novel multi-objective constriction particle swarm optimization (MOCPSO) is presented. Using different multi-objective optimization algorithms as reference, MOCPSO is proposed, which not only uses mutation operator to avoid earlier convergence, but also uses a new crowding operator to improve the distribution of nondominated solutions along the Pareto front, and uses the uniform design to obtain the optimal parameter combination. The sound evaluation criteria for multi-objective optimization algorithm are given, and some typical test functions are introduced. Experimental results show that MOCPSO has faster convergent speed and better search capacity than other multi-objective particle swarm optimization algorithms, especially when there are more than two objectives. MOCPSO is suitable to solve different multi-objective optimization problems.