摘要
针对拓扑序列航迹相关法为适应较大系统误差而计算量过大的问题,首先证明了存在系统误差时,不同传感器的拓扑序列满足近似线性变换关系,然后引入空间点集的奇异值分解(SVD)匹配算法对拓扑序列进行直接计算,通过检验得到的线性变换参数来判定航迹的相关性.SVD算法是一种高效的航迹相关算法,尤其适用于远距离目标的拓扑序列近似.通过仿真,验证了SVD算法的有效性,不但计算时间降低了90%以上,而且避免了拓扑序列法在进行角度和径向距离步进匹配时步长选择的难题,提高了航迹的相关成功率.
To release the huge calculation load for the track correlation algorithm based on the topology sequence facing large sensor system errors,the approximate linear transformation relationship between topology sequences from different sensors is firstly discussed,where sensor system errors are considered.The SVD method for space points set matching problem is directly used to estimate the linear transformation.A decision for topology sequences matching is made after validating the linear parameters.The SVD based track correlation algorithm is efficient,especially for far-away targets.The efficiency of the SVD based algorithm is validated by simulation.Because of removing the dilemma of choosing the angle and range search step sizes for the basic topology sequence algorithm,not only is the computational time reduced about 90%,but also the successful rate of correlation is improved.
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2011年第2期180-186,共7页
Journal of Xidian University
基金
国家863计划高技术资助项目(2008AA01Z216)
国家973资助项目(2009CB3020402)
