This paper focuses on the adaptive detection of range and Doppler dual-spread targets in non-homogeneous and nonGaussian sea clutter.The sea clutter from two polarimetric channels is modeled as a compound-Gaussian mod...This paper focuses on the adaptive detection of range and Doppler dual-spread targets in non-homogeneous and nonGaussian sea clutter.The sea clutter from two polarimetric channels is modeled as a compound-Gaussian model with different parameters,and the target is modeled as a subspace rangespread target model.The persymmetric structure is used to model the clutter covariance matrix,in order to reduce the reliance on secondary data of the designed detectors.Three adaptive polarimetric persymmetric detectors are designed based on the generalized likelihood ratio test(GLRT),Rao test,and Wald test.All the proposed detectors have constant falsealarm rate property with respect to the clutter texture,the speckle covariance matrix.Experimental results on simulated and measured data show that three adaptive detectors outperform the competitors in different clutter environments,and the proposed GLRT detector has the best detection performance under different parameters.展开更多
Integrated sensing and communication(ISAC)is regarded as a pivotal technology for 6G communication.In this paper,we employ Kullback-Leibler divergence(KLD)as the unified performance metric for ISAC systems and investi...Integrated sensing and communication(ISAC)is regarded as a pivotal technology for 6G communication.In this paper,we employ Kullback-Leibler divergence(KLD)as the unified performance metric for ISAC systems and investigate constellation and beamforming design in the presence of clutters.In particular,the constellation design problem is solved via the successive convex approximation(SCA)technique,and the optimal beamforming in terms of sensing KLD is proven to be equivalent to maximizing the signal-to-interference-plus-noise ratio(SINR)of echo signals.Numerical results demonstrate the tradeoff between sensing and communication performance under different parameter setups.Additionally,the beampattern generated by the proposed algorithm achieves significant clutter suppression and higher SINR of echo signals compared with the conventional scheme.展开更多
The optimal selection of radar clutter model is the premise of target detection,tracking,recognition,and cognitive waveform design in clutter background.Clutter characterization models are usually derived by mathemati...The optimal selection of radar clutter model is the premise of target detection,tracking,recognition,and cognitive waveform design in clutter background.Clutter characterization models are usually derived by mathematical simplification or empirical data fitting.However,the lack of standard model labels is a challenge in the optimal selection process.To solve this problem,a general three-level evaluation system for the model selection performance is proposed,including model selection accuracy index based on simulation data,fit goodness indexs based on the optimally selected model,and evaluation index based on the supporting performance to its third-party.The three-level evaluation system can more comprehensively and accurately describe the selection performance of the radar clutter model in different ways,and can be popularized and applied to the evaluation of other similar characterization model selection.展开更多
To address the problem that dynamic wind turbine clutter(WTC)significantly degrades the performance of weather radar,a WTC mitigation algorithm using morphological component analysis(MCA)with group sparsity is studied...To address the problem that dynamic wind turbine clutter(WTC)significantly degrades the performance of weather radar,a WTC mitigation algorithm using morphological component analysis(MCA)with group sparsity is studied in this paper.The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo.After that,the MCA algorithm is applied and the window used in the short-time Fourier transform(STFT)is optimized to lessen the spectrum leakage of WTC.Finally,the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution,thus contributing to better estimation performance of weather signals.The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.展开更多
以无人机为代表的低慢小(Low,Slow and Small Targets,LSS)目标的检测在雷达探测中因杂波干扰而面临巨大挑战。为了解决低慢小目标杂波抑制问题,本文提出了一种将鲸鱼优化算法(Whale Optimization Algorithm,WOA)与变分模态分解(Variati...以无人机为代表的低慢小(Low,Slow and Small Targets,LSS)目标的检测在雷达探测中因杂波干扰而面临巨大挑战。为了解决低慢小目标杂波抑制问题,本文提出了一种将鲸鱼优化算法(Whale Optimization Algorithm,WOA)与变分模态分解(Variational Mode Decomposition,VMD)相结合的方法,该算法用WOA优化VMD的分解参数,以实现最佳的模态分离效果,有效分离出目标信号与杂波信号。实验结果表明,WOA-VMD方法在复杂环境下能够显著提升低慢小目标的检测概率,计算简单且误差较小,可以对多个目标以及不同多普勒频率大小的目标进行处理,优于传统的杂波抑制方法。展开更多
基金supported by the National Natural Science Foundation of China(62371382,62071346)the Science,Technology&Innovation Project of Xiong’an New Area(2022XAGG0181)the Special Funds for Creative Research(2022C61540)。
文摘This paper focuses on the adaptive detection of range and Doppler dual-spread targets in non-homogeneous and nonGaussian sea clutter.The sea clutter from two polarimetric channels is modeled as a compound-Gaussian model with different parameters,and the target is modeled as a subspace rangespread target model.The persymmetric structure is used to model the clutter covariance matrix,in order to reduce the reliance on secondary data of the designed detectors.Three adaptive polarimetric persymmetric detectors are designed based on the generalized likelihood ratio test(GLRT),Rao test,and Wald test.All the proposed detectors have constant falsealarm rate property with respect to the clutter texture,the speckle covariance matrix.Experimental results on simulated and measured data show that three adaptive detectors outperform the competitors in different clutter environments,and the proposed GLRT detector has the best detection performance under different parameters.
基金supported in part by National Key R&D Program of China under Grant No.2021YFB2900200in part by National Natural Science Foundation of China under Grant Nos.U20B2039 and 62301032in part by China Postdoctoral Science Foundation under Grant No.2023TQ0028.
文摘Integrated sensing and communication(ISAC)is regarded as a pivotal technology for 6G communication.In this paper,we employ Kullback-Leibler divergence(KLD)as the unified performance metric for ISAC systems and investigate constellation and beamforming design in the presence of clutters.In particular,the constellation design problem is solved via the successive convex approximation(SCA)technique,and the optimal beamforming in terms of sensing KLD is proven to be equivalent to maximizing the signal-to-interference-plus-noise ratio(SINR)of echo signals.Numerical results demonstrate the tradeoff between sensing and communication performance under different parameter setups.Additionally,the beampattern generated by the proposed algorithm achieves significant clutter suppression and higher SINR of echo signals compared with the conventional scheme.
基金the National Natural Science Foundation of China(6187138461921001).
文摘The optimal selection of radar clutter model is the premise of target detection,tracking,recognition,and cognitive waveform design in clutter background.Clutter characterization models are usually derived by mathematical simplification or empirical data fitting.However,the lack of standard model labels is a challenge in the optimal selection process.To solve this problem,a general three-level evaluation system for the model selection performance is proposed,including model selection accuracy index based on simulation data,fit goodness indexs based on the optimally selected model,and evaluation index based on the supporting performance to its third-party.The three-level evaluation system can more comprehensively and accurately describe the selection performance of the radar clutter model in different ways,and can be popularized and applied to the evaluation of other similar characterization model selection.
文摘To address the problem that dynamic wind turbine clutter(WTC)significantly degrades the performance of weather radar,a WTC mitigation algorithm using morphological component analysis(MCA)with group sparsity is studied in this paper.The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo.After that,the MCA algorithm is applied and the window used in the short-time Fourier transform(STFT)is optimized to lessen the spectrum leakage of WTC.Finally,the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution,thus contributing to better estimation performance of weather signals.The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.
文摘以无人机为代表的低慢小(Low,Slow and Small Targets,LSS)目标的检测在雷达探测中因杂波干扰而面临巨大挑战。为了解决低慢小目标杂波抑制问题,本文提出了一种将鲸鱼优化算法(Whale Optimization Algorithm,WOA)与变分模态分解(Variational Mode Decomposition,VMD)相结合的方法,该算法用WOA优化VMD的分解参数,以实现最佳的模态分离效果,有效分离出目标信号与杂波信号。实验结果表明,WOA-VMD方法在复杂环境下能够显著提升低慢小目标的检测概率,计算简单且误差较小,可以对多个目标以及不同多普勒频率大小的目标进行处理,优于传统的杂波抑制方法。
