在高移动性场景中,利用正交时频空(Orthogonal Time Frequency Space,OTFS)调制实现通感一体化(Integrated Sensing and Communication,ISAC)技术具有显著优势。然而,OTFS-ISAC的波形设计及其信号处理极具挑战因而成为了悬而未决的难题...在高移动性场景中,利用正交时频空(Orthogonal Time Frequency Space,OTFS)调制实现通感一体化(Integrated Sensing and Communication,ISAC)技术具有显著优势。然而,OTFS-ISAC的波形设计及其信号处理极具挑战因而成为了悬而未决的难题。针对OTFS-ISAC感知接收处理时性能受限的问题,提出了基于最小均方误差(Minimum Mean Square Error,MMSE)和基于正交匹配追踪(Orthogonal Matching Pursuit,OMP)的感知信号处理算法。利用MMSE准则最小化均方误差进行感知,根据残差与原子相关性最大的准则迭代获得雷达感知信道的稀疏逼近元。仿真结果表明,相较于已有研究,所提方法不仅能准确感知目标,还获得了显著的感知性能增益,验证了所提方法的正确性与有效性。展开更多
自适应压缩感知与处理方法(Adaptive Compressive Sensing and Processing,ACSP)能够减少计算负荷,但现有的基于自适应压缩感知与处理的雷达目标跟踪方法仅限于单目标的跟踪,针对该问题,提出将自适应压缩感知用于雷达多目标追踪。通过...自适应压缩感知与处理方法(Adaptive Compressive Sensing and Processing,ACSP)能够减少计算负荷,但现有的基于自适应压缩感知与处理的雷达目标跟踪方法仅限于单目标的跟踪,针对该问题,提出将自适应压缩感知用于雷达多目标追踪。通过对回波进行稀疏表示,设计改进字典(稀疏变换矩阵)。在测量过程中,采用自适应权重替代随机高斯矩阵,构造和配置感知矩阵,基于压缩感知采样的接收数据来建立测量模型。由于测量与目标状态的非线性关系,采用结合联合概率数据关联方法的似然粒子滤波器对目标状态实时顺序估计,从而克服了多目标跟踪中的数据关联问题。理论仿真实验结果表明,改进的自适应压缩感知与处理方法实现了对多目标跟踪。展开更多
The use of underwater acoustic data has rapidly expanded with the application of multichannel, large-aperture underwater detection arrays. This study presents an underwater acoustic data compression method that is bas...The use of underwater acoustic data has rapidly expanded with the application of multichannel, large-aperture underwater detection arrays. This study presents an underwater acoustic data compression method that is based on compressed sensing. Underwater acoustic signals are transformed into the sparse domain for data storage at a receiving terminal, and the improved orthogonal matching pursuit(IOMP) algorithm is used to reconstruct the original underwater acoustic signals at a data processing terminal. When an increase in sidelobe level occasionally causes a direction of arrival estimation error, the proposed compression method can achieve a 10 times stronger compression for narrowband signals and a 5 times stronger compression for wideband signals than the orthogonal matching pursuit(OMP) algorithm. The IOMP algorithm also reduces the computing time by about 20% more than the original OMP algorithm. The simulation and experimental results are discussed.展开更多
文摘在高移动性场景中,利用正交时频空(Orthogonal Time Frequency Space,OTFS)调制实现通感一体化(Integrated Sensing and Communication,ISAC)技术具有显著优势。然而,OTFS-ISAC的波形设计及其信号处理极具挑战因而成为了悬而未决的难题。针对OTFS-ISAC感知接收处理时性能受限的问题,提出了基于最小均方误差(Minimum Mean Square Error,MMSE)和基于正交匹配追踪(Orthogonal Matching Pursuit,OMP)的感知信号处理算法。利用MMSE准则最小化均方误差进行感知,根据残差与原子相关性最大的准则迭代获得雷达感知信道的稀疏逼近元。仿真结果表明,相较于已有研究,所提方法不仅能准确感知目标,还获得了显著的感知性能增益,验证了所提方法的正确性与有效性。
文摘自适应压缩感知与处理方法(Adaptive Compressive Sensing and Processing,ACSP)能够减少计算负荷,但现有的基于自适应压缩感知与处理的雷达目标跟踪方法仅限于单目标的跟踪,针对该问题,提出将自适应压缩感知用于雷达多目标追踪。通过对回波进行稀疏表示,设计改进字典(稀疏变换矩阵)。在测量过程中,采用自适应权重替代随机高斯矩阵,构造和配置感知矩阵,基于压缩感知采样的接收数据来建立测量模型。由于测量与目标状态的非线性关系,采用结合联合概率数据关联方法的似然粒子滤波器对目标状态实时顺序估计,从而克服了多目标跟踪中的数据关联问题。理论仿真实验结果表明,改进的自适应压缩感知与处理方法实现了对多目标跟踪。
基金Project(11174235)supported by the National Natural Science Foundation of ChinaProject(3102014JC02010301)supported by the Fundamental Research Funds for the Central Universities,China
文摘The use of underwater acoustic data has rapidly expanded with the application of multichannel, large-aperture underwater detection arrays. This study presents an underwater acoustic data compression method that is based on compressed sensing. Underwater acoustic signals are transformed into the sparse domain for data storage at a receiving terminal, and the improved orthogonal matching pursuit(IOMP) algorithm is used to reconstruct the original underwater acoustic signals at a data processing terminal. When an increase in sidelobe level occasionally causes a direction of arrival estimation error, the proposed compression method can achieve a 10 times stronger compression for narrowband signals and a 5 times stronger compression for wideband signals than the orthogonal matching pursuit(OMP) algorithm. The IOMP algorithm also reduces the computing time by about 20% more than the original OMP algorithm. The simulation and experimental results are discussed.
