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Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Long-time coherent integration is one of the important techniques to improve radar detection performance. The range migration (RM), range curvation (RC), Doppler frequency migration (DFM) and Doppler frequency curvation (DFC) induced by the movement of high-speed maneuvering target during the longtime coherent integration would bring about serious integration signal-to-noise ratio (SNR) loss, which may furthermore cause the target detection performance of the radar sharp deterioration in the background of noise. In order to solve the problem, the paper proposes a parameterized long-time integration algorithm for high-speed maneuvering targets. The method applies keystone transform (KT) to eliminate linear RM firstly. Then coherently integrated modified cubic phase function (CIMCPF) and coherency integrated modified high-order ambiguous function (CIMHAF) methods are utilized respectively to estimate the first-order and second-order accelerations. Thanks to the use of signal coherent property, the method is still effective at low signal-to-noise ratio. Furthermore, since there is no brute-force search process, the computational complexity of the method is low. Simulation results and analysis demonstrate the excellent performance of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to solve the problems of halo artefacts and unfavourable visual perception in the fused images, this paper proposes an image fusion algorithm based on gradient-domain guided filtering and an improved pulse-coupled neural network (PCNN). First, an image fusion model is constructed using the image features of image structure, sharpness and contrast saliency. Secondly, the initial decision map is optimised by inter-pixel correlation using gradient-domain guided filtering instead of the traditional optimisation method. Then, the optimised decision map is used as external input to stimulate the improved PCNN model to obtain the fusion weight map. Finally, the source image and the fusion weight map are weighted to obtain the final Finally, the source image and the fusion weight map are weighted to obtain the final fused image. The experimental results show that this method can better preserve the image edge, texture and detail information, avoid halo artefacts on the target edge, and facilitate visual observation. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To solve the problem of multiple unmanned aerial vehicle (UAV) collaborative logistics task allocation in urban areas, a collaborative logistics task allocation model for multiple UAVs is constructed with the objective function of minimizing economic cost, time penalty and safety risk by comprehensively considering the factors of different UAVs' performance, logistics timeliness and flight reliability. Because of the large scale and high complexity of task allocation, an improved quantum particle swarm optimization algorithm is designed to solve the problem. Firstly, in order to enhance the ergodicity and diversity of particles, particle initialization is carried out by homogenization cascade Logistic mapping. Then, in order to improve the search ability of particles and speed up the running efficiency of the algorithm, particle mutation method based on Gaussian distribution is introduced. Finally, in order to improve the efficiency of the algorithm, adaptive inertia weight method is used to assign different values to each particle. Simulation results show that the constructed model can achieve multi-objective optimization of task allocation. It is close to the reality of UAV logistics distribution in urban areas. Compared with the traditional quantum particle swarm optimization algorithm and the genetic algorithm, the task allocation cost of the improved algorithm is reduced by 5.9% and 6.3% respectively. Furthermore, the parameter weight setting is also analyzed in this paper. When the weight coefficients of the three sub objective functions are 0.225, 0.275 and 0.500 respectively and the population size is 150, the task allocation scheme planned by this algorithm is optimal. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)