To solve the problem of multiple moving sources passive location, a novel blind source separa- tion （BSS） algorithm based on the muhiset canonical correlation analysis （MCCA） is presented by exploiting the differe...To solve the problem of multiple moving sources passive location, a novel blind source separa- tion （BSS） algorithm based on the muhiset canonical correlation analysis （MCCA） is presented by exploiting the different temporal structure of uncorrelated source signals first, and then on the basis of this algorithm, a novel multiple moving sources passive location method is proposed using time difference of arrival （TDOA） and frequency difference of arrival （FDOA） measurements. The key technique of this location method is TDOA and FDOA joint estimation, which is based on BSS. By blindly separating mixed signals from multiple moving sources, the multiple sources location problem can be translated to each source location in turn, and the effect of interference and noise can also he removed. The simulation results illustrate that the performance of the MCCA algorithm is very good with relatively light computation burden, and the location algorithm is relatively simple and effective.展开更多
基金Supported by the National High Technology Research and Development Program of China(No.2009AAJ116,2009AAJ208,2010AA7010422)the National Science Foundation for Post-Doctoral Scientists of China(No.20080431379,200902671)the Hubei Natural Science Foundation(No.2009CDB031)
文摘To solve the problem of multiple moving sources passive location, a novel blind source separa- tion （BSS） algorithm based on the muhiset canonical correlation analysis （MCCA） is presented by exploiting the different temporal structure of uncorrelated source signals first, and then on the basis of this algorithm, a novel multiple moving sources passive location method is proposed using time difference of arrival （TDOA） and frequency difference of arrival （FDOA） measurements. The key technique of this location method is TDOA and FDOA joint estimation, which is based on BSS. By blindly separating mixed signals from multiple moving sources, the multiple sources location problem can be translated to each source location in turn, and the effect of interference and noise can also he removed. The simulation results illustrate that the performance of the MCCA algorithm is very good with relatively light computation burden, and the location algorithm is relatively simple and effective.
文摘混合元胞自动机(Mixed-cell cellular automata,MCCA)模型改进了传统的元胞自动机(Cellular automata,CA)模型,基于现实复杂土地结构引入混合元胞,实现了从定性、静态模拟到定量、动态模拟的跨越。本文首先探究MCCA模型在黑河中游甘临高地区(甘州区、临泽县和高台县)的适用性;之后分别采用多目标线性规划(Multiple-objective programming,MOP)模型、普通线性回归模型预测得到2035年可持续发展(Sustainable development,SUD)情景、基本发展(Basic development,BAD)情景中不同地类面积数值,然后将面积输入MCCA模型中进行不同情景的土地利用空间结构可视化,并开展对比研究。结果表明:各项精度评价指标均表明MCCA模型的模拟精度较高,Kappa系数、混合元胞质量系数(Mixed-cell figure of merit,mcFoM)和平均相对熵(Relative entropy,RE)分别为0.886、0.261和0.508,优于基于纯净元胞的斑块生成土地利用变化模拟(Patch-generating land use simulation model,PLUS)模型,因此MCCA模型适用于研究区土地利用结构模拟。2035年SUD情景中林地范围明显高于BAD情景,生态效益较BAD情景增速快,建设用地和耕地适度扩张,综合效益增速较快。该结果表明耦合MOP和MCCA模型模拟的土地利用优化配置方案能够更好地协调经济与环境的关系,既有利于经济快速发展,又能保护生态环境和维持社会稳定。
