In the study of passive location based on the Time Difference of Arrival (TDOA),the cross-correlation algorithm has been widely used to estimate time delay.The computational complexity of cross-correlation algorithm is large,and the algorithm complexity is O(N2).With further research,some scholars have proposed a cross-correlation algorithm based on Fourier transform.By using the FFT algorithm,the processing speed has been greatly improved and the algorithm complexity reduced to O(N·log N),from square level down to sub-linear level.In some real-time occasions,especially when the sampling point is quite large,the traditional FFT algorithm is still difficult to meet the requirements.By introducing the mapping and reconstruction of sparse signals,the calculation of cross-correlation is optimized by using the Sparse Fourier Transform (SFT) algorithm,which can further improve the speed of time delay estimation.The algorithm complexity reduces to O(N),from sub-linear level down to linear level.The measured data shows that accuracy can meet the requirement of engineering application,and it provides a technical reference for real-time passive TDOA location.%在基于到达时间差(TDOA)的无源定位研究中,利用互相关算法估计站间时延被广泛采用.相关算法计算量较大,算法复杂度为O(N2).随着研究深入,有学者提出了基于傅里叶变换的互相关算法,由于使用快速傅里叶变换(FFT)算法,处理速度得到较大提升,算法复杂度由平方级降低至亚线性级,即O(N·log N).在一些实时性较强的环境,当采样点数较大时,传统FFT算法仍很难满足要求.本文借鉴稀疏傅里叶变换(SFT)算法,引入稀疏信号的映射和重构思想,优化互相关计算过程,以进一步提高时延估计的速度,改进算法的算法复杂度为O(N),从亚线性级降低至线性级.实测数据表明,测量精度满足工程应用的要求,可为实时性较高的无源时差定位提供技术参考.
展开▼
