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您的检索式：作者名="Gengxin Ning"
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| 1 | Acoustic Velocity-Independent 2-D DOA Estimation for Underwater Application显示文摘In this paper,an acoustic velocityindependent two-dimensional direction of arrival(2-D DOA)estimation for underwater application is presented to eliminate the effect of the inaccurate acoustic velocity estimation.According to the geometric relationship between the linear arrays,the proposed method employs the cross correlation matrix(CCM)of the data received by three crossed linear arrays to remove the acoustic velocity factor.The simulation results demonstrate that the proposed method is not susceptible to the acoustic velocity.For a single source,the proposed method outperforms the conventional method in all conditions.For multiple sources,there is a little performance degradation for the proposed method compared with the conventional method.However,the proposed method displays a better performance than the conventional method in situations where the signal to noise ratio(SNR)is extremely low or the acoustic velocity estimation error is non-negligible.Furthermore,the computational complexity of the proposed method is lower than that of the conventional method using the same amount of sensors in total,while the performance is still acceptable. | Gengxin Ning Zhenfeng Liao Xiaopeng Li Cui Yang | 2022 | Journal of Communications and Information Networks2022，7，4： | 1 |
| 2 | 1D Acoustic Velocity-Independent DOA Estimation Method with Arbitrary Arrays显示文摘The performance of conventional direction of arrival(DOA)method is greatly affected by the uncertainty of wave velocity in underwater environment.To solve this problem,an acoustic velocity-independent method is proposed to estimate the underwater DOA using two arbitrary intersecting uniform linear arrays in this study.By introducing the additional array compared to the conventional DOA methods,the proposed algorithm can make its performance independent of the acoustic velocity through the geometric relationship between those two arrays.The simulation results demonstrate that the proposed method is more accurate and robust than other methods in an unknown sound velocity. | Gengxin Ning Liang Xie Xiaopeng Li Jun Zhang | 2020 | Journal of Communications and Information Networks2020，5，2： | 1 |
| 3 | Semantics Analytics of Origin-Destination Flows from Crowd Sensed Big Data显示文摘Monitoring,understanding and predicting Origin-destination(OD)flows in a city is an important problem for city planning and human activity.Taxi-GPS traces,acted as one kind of typical crowd sensed data,it can be used to mine the semantics of OD flows.In this paper,we firstly construct and analyze a complex network of OD flows based on large-scale GPS taxi traces of a city in China.The spatiotemporal analysis for the OD flows complex network showed that there were distinctive patterns in OD flows.Then based on a novel complex network model,a semantics mining method of OD flows is proposed through compounding Points of Interests(POI)network and public transport network to the OD flows network.The propose method would offer a novel way to predict the location characteristic and future traffic conditions accurately. | Ning Cao Shengfang Li Keyong Shen Sheng Bin Gengxin Sun Dongjie Zhu Xiuli Han Guangsheng Cao Abraham Campbell | 2019 | Computers, Materials & Continua2019，，7： | 0 |
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