利用重要性采样的时差-频差联合估计算法

TDOA-FDOA joint estimation using importance sampling method

针对无源定位中参考信号真实值未知的时差(TDOA)-频差(FDOA)联合估计问题,构建了一种新的时差-频差最大似然(ML)估计模型,并采用重要性采样(IS)方法求解似然函数极大值,得到时差-频差联合估计。算法通过生成时差-频差样本,并统计样本加权均值得到估计值,克服了传统互模糊函数(CAF)算法只能得到时域和频域采样间隔整数倍估计值的问题,且不存在期望最大化(EM)等迭代算法的初值依赖和收敛问题。推导了时差-频差联合估计的克拉美罗下界(CRLB),并通过仿真实验表明,算法的计算复杂度适中,估计精度优于CAF算法和EM算法,在不同信噪比条件下估计误差接近CRLB。

To solve the joint estimation of time difference of arrival（TDOA）and frequency difference of arrival（FDOA）in passive location system,where the true value of the reference signal is unknown,a novel maximum likelihood（ML）estimator of TDOA and FDOA is constructed.Then importance sampling（IS）method is applied to find the maximum of likelihood function by generating the samples of TDOA and FDOA.Unlike the cross ambiguity function（CAF）algorithm or the expectation maximization（EM）algorithm,the proposed algorithm can estimate the TDOA and FDOA of non-integer multiple of the sampling interval and has no dependence on the initial estimate.The Cramer Rao lower bound（CRLB）is also derived.Simulation results show that the proposed algorithm outperforms the CAF and EM algorithm with higher accuracy and moderate computational complexity,and approaches the CRLB for different SNR conditions.