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波导环境多目标时延-多普勒解卷成像
引用本文:曾小辉,赵航芳,宫先仪,李春晓.波导环境多目标时延-多普勒解卷成像[J].声学学报,2016,41(3):353-361.
作者姓名:曾小辉  赵航芳  宫先仪  李春晓
作者单位:1 浙江大学信息与电子工程学院 杭州 310027;
基金项目:国家自然科学基金(61379008,61101231)和浙江省科技厅公益性项目(2013C31003)资助
摘    要:有源声呐感兴趣的参量是目标距离和径向速度,它们无法直接观测得到,需要通过估计而获得。利用波导多路径环境多目标时延-多普勒模型,可以导出采样互模糊度函数均值是发射信号自模糊度函数与广义目标反射性密度函数的两维卷积,其中广义目标反射性密度函数为信道扩展函数与目标反射性密度函数的两维卷积。依据信息理论最小Csiszar鉴别准则,可导出R-L (Richardson-Lucy)迭代解卷算法,对采样互模糊度函数均值进行两维迭代解卷积,消除发射信号和信道引入的模糊,序贯地实现时延-多普勒两维像的估计,进而获得多目标的时延和多普勒参量估计。仿真结果和海上实验数据分析验证了R-L解卷算法的可行性和有效性,较之常规的匹配滤波和维纳滤波算法,R-L算法有效地提高了时延和多普勒估计的分辨力和精度。 

收稿时间:2014-11-11

Delay-Doppler deconvolution image formation for multiple targets in waveguide environment
Institution:1 College of Information Science and Electronic Engineering, Zhejiang University Hangzhou 310027;2 Hangzhou Applied Acoustics Research Institute Hangzhou 310023;3 College of Mechanical Engineering, Zhejiang University of Technology Hangzhou 310014
Abstract:The ranges and radial velocities of targets are interesting parameters in the active sonar, which can't be observed directly but rather estimated. Firstly, by making use of the delay-Doppler model of multi-targets in waveguide multipath environment, one finds that sample cross-ambiguity function is a two-dimensional (2D) convolution of the autoambiguity function of the transmitted signal with the generalized target reflectivity density, which is a 2D convolution of the spread function of channel with the reflectivity density as well. Secondly, from the perspective of information theory, an iterative deconvolution algorithm named R-L (Richardson-Lucy) is derived based on minimum Csiszar discrimination criterion. Finally, the blurs caused by both of the transmitted signal and channel are removed by 2D deconvolution of the expectation of sample cross-ambiguity function, 2D image and then parameters of time-delay and Doppler is estimated sequentially. Results of both numerical simulation and sea experimental data processing verify the feasibility and effectiveness of R-L deconvolution algorithm with improving the resolution and accuracy of the estimations, when compared to the classical match filtering and Wiener filtering. 
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