基于马尔科夫键蒙特卡洛抽样的最大似然时差-频差联合估计算法

该文针对无源定位中参考信号真实值未知的时差-频差联合估计问题，构建了一种新的时差-频差最大似然估计模型，并采用马尔科夫链蒙特卡洛(MCMC)方法求解似然函数的全局极大值，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界，并通过仿真实验表明，算法在不同信噪比条件下的估计精度优于CAF算法和EM算法，且计算复杂度较低。

Maximum Likelihood TDOA-FDOA Estimator Using Markov Chain Monte Carlo Sampling

This paper investigates 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, and Markov Chain Monte Carlo (MCMC) method is applied to finding the global maximum of likelihood function by generating the realizations of TDOA and FDOA. Unlike the Cross Ambiguity Function (CAF) algorithm or the Expectation Maximization (EM) algorithm, the proposed algorithm can also 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 for different SNR conditions with higher accuracy and lower computational complexity.