电离层中释放的金属蒸气产生人工等离子体云团,其可显著改变无线电波传播。本文利用几何绕射理论(geometrical theory of diffraction, GTD)和有限元法(finite element method, FEM)相结合的方法,给出了经由天线、人工等离子云团和无人...电离层中释放的金属蒸气产生人工等离子体云团,其可显著改变无线电波传播。本文利用几何绕射理论(geometrical theory of diffraction, GTD)和有限元法(finite element method, FEM)相结合的方法,给出了经由天线、人工等离子云团和无人机(unmanned aerial vehicle, UAV)群组成的传播链路中信号强度计算方法。利用30~70 MHz甚高频(very high frequency, VHF)信号研究人工等离子体云团与UAV群的复合散射特性,得出如下结论:接收功率随着信号频率增加呈下降趋势;当机群由N架UAV构成时,阵因子迭加使机群雷达散射截面(radar cross section, RCS)出现一定的起伏,同相迭加时,接收功率可比单个UAV高约20lg N dB;利用人工等离子体云团散射可实现VHF频段用于对米级尺度RCS目标进行超视距探测,有助于解决紧急情况下电离层扰动对高频探测的不利影响。展开更多
针对雷达主瓣干扰抑制问题,提出一种基于信干噪比(signal to interference plus noise ratio,SINR)最大化的盲提取主瓣干扰抑制方法。与盲分离不同,盲提取能够从多路混合信号中提取出感兴趣的一路分量,这更适合在多信源多通道的复杂电...针对雷达主瓣干扰抑制问题,提出一种基于信干噪比(signal to interference plus noise ratio,SINR)最大化的盲提取主瓣干扰抑制方法。与盲分离不同,盲提取能够从多路混合信号中提取出感兴趣的一路分量,这更适合在多信源多通道的复杂电磁环境下进行干扰抑制。该方法在混合信号距离域建立SINR最大化的优化模型,采用粒子群优化(particle swarm optimization,PSO)算法进行求解并提取出目标回波信号实现主瓣干扰抑制。经仿真测试,该方法相较于传统的盲分离干扰抑制方法,提升了干扰抑制效果;无需信源数目估计,对通道数目要求更低,在欠定场景中依然适用;减小了计算复杂度,更适用于复杂电磁环境。展开更多
As the amount of data produced by ground penetrating radar （GPR） for roots is large, the transmission and the storage of data consumes great resources. To alleviate this problem, we propose here a root imaging algor...As the amount of data produced by ground penetrating radar （GPR） for roots is large, the transmission and the storage of data consumes great resources. To alleviate this problem, we propose here a root imaging algorithm using chaotic particle swarm optimal （CPSO） compressed sensing based on GPR data according to the sparsity of root space. Radar data are decomposed, observed, measured and represented in sparse manner, so roots image can be reconstructed with limited data. Firstly, radar signal measurement and sparse representation are implemented, and the solution space is established by wavelet basis and Gauss random matrix; secondly, the matching function is considered as the fitness function, and the best fitness value is found by a PSO algorithm; then, a chaotic search was used to obtain the global optimal operator; finally, the root image is reconstructed by the optimal operators. A-scan data, B-scan data, and complex data from American GSSI GPR is used, respectively, in the experimental test. For B-scan data, the computation time was reduced 60 % and PSNR was improved 5.539 dB; for actual root data imaging, the reconstruction PSNR was 26.300 dB, and total computation time was only 67.210 s. The CPSO-OMP algorithm overcomes the problem of local optimum trapping and comprehensively enhances the precision during reconstruction.展开更多
A novel adaptive sampling interval algorithm for multitarget tracking is presented. This algorithm which is based on interacting multiple models incorporates the grey relational grade （GRG） into the particle swarm o...A novel adaptive sampling interval algorithm for multitarget tracking is presented. This algorithm which is based on interacting multiple models incorporates the grey relational grade （GRG） into the particle swarm optimization （PSO）. Firstly, the desired tracking accuracy is set for each target. Secondly, sampling intervals are selected as particles, and then the advantage of the GRG is taken as the measurement function for resource management. Meanwhile, the fitness value of the PSO is used to measure the difference between desired tracking accuracy and estimated tracking accuracy. Finally, it is suggested that the radar should track the target whose prediction value of the next sampling interval is the smallest. Simulations show that the proposed method improves both the tracking accuracy and tracking efficiency of the phased-array radar.展开更多
文摘电离层中释放的金属蒸气产生人工等离子体云团,其可显著改变无线电波传播。本文利用几何绕射理论(geometrical theory of diffraction, GTD)和有限元法(finite element method, FEM)相结合的方法,给出了经由天线、人工等离子云团和无人机(unmanned aerial vehicle, UAV)群组成的传播链路中信号强度计算方法。利用30~70 MHz甚高频(very high frequency, VHF)信号研究人工等离子体云团与UAV群的复合散射特性,得出如下结论:接收功率随着信号频率增加呈下降趋势;当机群由N架UAV构成时,阵因子迭加使机群雷达散射截面(radar cross section, RCS)出现一定的起伏,同相迭加时,接收功率可比单个UAV高约20lg N dB;利用人工等离子体云团散射可实现VHF频段用于对米级尺度RCS目标进行超视距探测,有助于解决紧急情况下电离层扰动对高频探测的不利影响。
文摘针对雷达主瓣干扰抑制问题,提出一种基于信干噪比(signal to interference plus noise ratio,SINR)最大化的盲提取主瓣干扰抑制方法。与盲分离不同,盲提取能够从多路混合信号中提取出感兴趣的一路分量,这更适合在多信源多通道的复杂电磁环境下进行干扰抑制。该方法在混合信号距离域建立SINR最大化的优化模型,采用粒子群优化(particle swarm optimization,PSO)算法进行求解并提取出目标回波信号实现主瓣干扰抑制。经仿真测试,该方法相较于传统的盲分离干扰抑制方法,提升了干扰抑制效果;无需信源数目估计,对通道数目要求更低,在欠定场景中依然适用;减小了计算复杂度,更适用于复杂电磁环境。
基金supported by the Fundamental Research Funds for the Central Universities(DL13BB21)the Natural Science Foundation of Heilongjiang Province(C2015054)+1 种基金Heilongjiang Province Technology Foundation for Selected Osverseas ChineseNatural Science Foundation of Heilongjiang Province(F2015036)
文摘As the amount of data produced by ground penetrating radar （GPR） for roots is large, the transmission and the storage of data consumes great resources. To alleviate this problem, we propose here a root imaging algorithm using chaotic particle swarm optimal （CPSO） compressed sensing based on GPR data according to the sparsity of root space. Radar data are decomposed, observed, measured and represented in sparse manner, so roots image can be reconstructed with limited data. Firstly, radar signal measurement and sparse representation are implemented, and the solution space is established by wavelet basis and Gauss random matrix; secondly, the matching function is considered as the fitness function, and the best fitness value is found by a PSO algorithm; then, a chaotic search was used to obtain the global optimal operator; finally, the root image is reconstructed by the optimal operators. A-scan data, B-scan data, and complex data from American GSSI GPR is used, respectively, in the experimental test. For B-scan data, the computation time was reduced 60 % and PSNR was improved 5.539 dB; for actual root data imaging, the reconstruction PSNR was 26.300 dB, and total computation time was only 67.210 s. The CPSO-OMP algorithm overcomes the problem of local optimum trapping and comprehensively enhances the precision during reconstruction.
基金supported by the Pre-research Fund (N0901-041)the Funding of Jiangsu Innovation Program for Graduate Education(CX09B 081Z CX10B 110Z)
文摘A novel adaptive sampling interval algorithm for multitarget tracking is presented. This algorithm which is based on interacting multiple models incorporates the grey relational grade （GRG） into the particle swarm optimization （PSO）. Firstly, the desired tracking accuracy is set for each target. Secondly, sampling intervals are selected as particles, and then the advantage of the GRG is taken as the measurement function for resource management. Meanwhile, the fitness value of the PSO is used to measure the difference between desired tracking accuracy and estimated tracking accuracy. Finally, it is suggested that the radar should track the target whose prediction value of the next sampling interval is the smallest. Simulations show that the proposed method improves both the tracking accuracy and tracking efficiency of the phased-array radar.
