To solve the jamming decision problem of the non-cooperative multifunctional radar in a complex electromagnetic environment, this paper proposes a radar cognitive jamming decision method based on an incomplete information game. First, the characteristics of current multifunctional radars are analyzed, and a radar jamming decision model is established based on an incomplete information game; then, after quantifying the gains of both sides by considering the jamming effect and radar anti-jamming capability, a jamming strategy selection algorithm is designed for unknown operating mode radars, and the magnitude of the jamming effect is used as the criterion for strategy seeking. Finally, the proposed method is scientifically verified utilizing simulation experiments. Compared with traditional radar jamming decision methods, this method proposed in this paper is more suitable for practical operation in terms of model design and gain quantification, and the increase in the number of radar operating modes has no impact on the accuracy of a strategy seeking; the output of jamming strategy is a pure strategy, which is accessible and apprehensible for equipment operators.
In order to achieve fast and accurate target segmentation of ship images, a segmentation method based on fuzzy entropy and salient region extraction is proposed. Firstly, the multi-level fuzzy entropy and differential evolution is applied to obtain image segmentation result quickly. Then, for obtaining the seed points, a saliency detection method based on dual pyramids and feature fusion is used, and the target core region is generated by morphological open operation using reconstruction and region maximum. Finally, the image segmentation results are binarized in each layer and combined, and the region block is selected with the largest overlap on the target core region for the target segmentation results. The experimental results show that the new method can realize the fast and accurate segmentation of ship target images under various complex scenes.
The image segmentation method based on 1D histogram and the optimal objective function is an important threshold segmentation method, but if it is applied to the infrared image segmentation directly, its ability for the suppression of the background noise is weak. In this paper, the 2D Maximum inter-class variance method is applied to infrared image segmentation, which improves the image segmentation effect obviously, but it takes a long time to calculate. Therefore, an improved Particle Swarm Optimization (PSO) algorithm is introduced to speed up the algorithm, which improves the real-time performance of the algorithm. The experimental results show that the new method has not only good segmentation effect, but also high computational efficiency, and it is a fast infrared image segmentation method.
In order to realize fast target detection under complex image scene, a novel method is proposed based on supervised saliency map and efficient subwindow search. Supervised saliency map generation mainly includes: (1) the original image is segmented by different parameters to obtain multi-segmentation results; (2) regional feature is mapped for salient value by random forest regressor; (3) obtain saliency map by fusing multi-level segmentation results. Efficient subwindow search method is implemented by transforming salient target detection as maximum saliency density, and using branch and bound algorithm to localize the maximum saliency density in global optimum. The experimental results show that the new method can not only detect salient region, but also recognize this region in some extent.
The future naval battle will take place in a complex electromagnetic environment. Therefore, seizing the electromagnetic superiority has become the major actions of the navy. Radar reconnaissance equipment is an important part of the system to obtain and master battlefield electromagnetic radiation source information. Azimuth measurement function is one of the main function radar reconnaissance equipments. Whether the accuracy of direction finding meets the requirements, determines the vessels successful or not active jamming, passive jamming, guided missile attack and other combat missions, having a direct bearing on the vessels combat capabilities . How to test the performance of radar reconnaissance equipment, while affecting the task as little as possible is a problem. This paper, based on radar signal simulator and GPS positioning equipment, researches and experiments on one new method, which povides the azimuth benchmark required by the direction-finding precision test anytime anywhere, for the ships at jetty to test radar reconnaissance equipment performance in direction-finding. It provides a powerful means for the naval radar reconnaissance equipments daily maintenance and repair work[1].
In order to segment the target well and suppress background noises effectively, an infrared image segmentation method based on spatial coherence histogram and maximum entropy is proposed. First, spatial coherence histogram is presented by weighting the importance of the different position of these pixels with the same gray-level, which is obtained by computing their local density. Then, after enhancing the image by spatial coherence histogram, 1D maximum entropy method is used to segment the image. The novel method can not only get better segmentation results, but also have a faster computation time than traditional 2D histogram-based segmentation methods.
Image registration is a difficult problem when dealing with images captured by different sensors, for instance, Visible and IR sensors, due to their texture, gray not matched and feature inconsistent. Since the edges of the objects present in the images are preserved in most cases, so, in this paper, a new contour-based image registration algorithm is proposed. First, an edge detection method based on multi-scale and multi-direction morphology is proposed for extracting contour. Then the matched contour pairs are found according to the following shape attributes, such as the first and second invariant moments, etc. Next, within these matched contours, several control-point pairs are selected, which has the characteristics of 1) even distribution 2) a suitable number of points 3) locally maximum curvature. In addition, the sea-sky-line is extracted in advance, which determines the region of image registration and reduces the computation time. The performance of our algorithm is demonstrated by estimating the registration accuracy and evaluating the fusion effects of the visual and IR images of ship target.
IR and visible sensors are very common sensors adopted in the region of military image fusion, however, since there are less correlation and lack of consistent features between their acquired images, it is very difficult to achieve automatic registration of IR and visible images. In this paper, optoelectronic imaging anti-ship missile is taken as research object, and based on the analysis of its seeker's imaging process, we proposed a new automatic registration algorithm based on sensor parameters and image information. The basic idea of our algorithm is that decomposing the transform model, and simplifying it step by step. For example, the transform of IR and visible image registration is affine. By adjusting sensor parameters, the affine transform can be simplified to rigid transform through eliminating the scaling change between images, and by finding out the centroid of ship target's contour we can further eliminate the translational change between them. After image registration is achieved, the registration effect is assessed by judging whether the sea-sky-lines of the two registered images are in the same position. The final simulation experiments convince us that our algorithm has better performance on solving the difficult registration problem of small target images with different sensors.
Since the conventional denoising algorithms have not considered the influence of certain concrete detector, they are not very effective to remove various noises contained in the low signal-to-noise ration infrared image. In this paper, a new thinking for infrared image denoising is proposed, which is based on the noise analyses of detector with an example of L model infrared multi-element detector. According to the noise analyses of this detector, the emphasis is placed on how to filter white noise and fractal noise in the preprocessing phase. Wavelet analysis is a good tool for analyzing 1/f process. 1/f process can be viewed as white noise approximately since its wavelet coefficients are stationary and uncorrelated. So if wavelet transform is adopted, the problem of removing white noise and fraction noise is simplified as the only one problem, i.e., removing white noise. To address this problem, a new wavelet domain adaptive wiener filtering algorithm is presented. From the viewpoint of quantitative and qualitative analyses, the filtering effect of our method is compared with those of traditional median filter, mean filter and wavelet thresholding algorithm in detail. The results show that our method can reduce various noises effectively and raise the ratio of signal-to-noise evidently.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.