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Extended target detection in the presence of K-distributed clutter has gained a special interest in recent years. Highresolution radars allow a target to be found in several resolution cells. Therefore, the detection rate and the false alarm rate of an extended target should be analyzed by these cells jointly rather than in one single resolution cell. A detector in which all the cells whose magnitude affected by one target are considered jointly is present. The performance and optimal parameters of the detector are analyzed in detail. Meanwhile, the large amount of calculation caused by enormous raw data is also considered. Then, an efficient method based on region growing algorithm and contour tracking algorithm is proposed. Only part of the resolution cells is scanned once with the proposed method, while all the cells are scanned at least one times in the existing methods. Therefore, considerable calculations are saved. Furthermore, the proposed method and several existing methods are performed with real data and simulated data, and the results show that the proposed model is practical and efficient to detect the extended targets in K-distributed clutters.

In recent years, multi-extended-targets detection in sea clutter has gained a special interest. Dynamic programming based track-before-detect (DP-TBD) algorithm is used to detect extended targets in video data of high resolution radars. Two innovations are presented in this work. First one is a novel partition method to cluster targets into well separate groups for the problem of high-dimensional maximisation. Second one is a novel merit function specifically designed for extended targets for the problem of target extended. Both the principle of this novel DP-TBD method specifically for extended targets and its detail implementation are presented. Then, both the real data and simulated data are performed. The comparison of the results shows that compared with particle filter based track before detect algorithm, the proposed method obtains better performance in detection rate, position error and false alarm rate. Meanwhile, far less calculation is spent with the novel partition method. Therefore, it can safely conclude that the proposed method is very practical under various sea conditions in the real world.

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With the deterioration of ecological environment, sustainable supply chain management has become an important means of enterprise performance evaluation. During the implementation of a sustainable supply chain management (SSCM), Chinese enterprises are faced with domestic and overseas institutional pressures, such as laws, regulations, and agenda, etc. Then, whether the dual institutional pressure has a promoting role for Chinese manufacturing enterprises in implementing the SSCM and whether the sustainable supply chain practices (SSCP) can promote the output of enterprise performance, have become a topic worthy of study. Hypothesis on the relationship between the institutional pressure of sustainable supply chain and economic, social, and environmental performances is innovatively raised in this paper and a theoretical model is built. Besides, a fitting test is conducted to a full model by using a structural equation model. An optimal model is eventually obtained after repeated modifications to the initial model by means of goodness of fit and causal path coefficient, thereby it is verified in this paper that the institutional pressure has a significantly positive impact on the SSCP; a conclusion is drawn that the impact of the SSCP on the economic, environmental, and social performances shows different characteristics, which has an important theoretical guiding role in promoting the SSCP.