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基于馬爾科夫鍵蒙特卡洛抽樣的最大似然時差-頻差聯(lián)合估計算法

趙擁軍,  趙勇勝,  趙闖

趙擁軍, 趙勇勝, 趙闖. 基于馬爾科夫鍵蒙特卡洛抽樣的最大似然時差-頻差聯(lián)合估計算法[J]. 電子與信息學報, 2016, 38(11): 2745-2752. doi: 10.11999/JEIT160050
引用本文: 趙擁軍, 趙勇勝, 趙闖. 基于馬爾科夫鍵蒙特卡洛抽樣的最大似然時差-頻差聯(lián)合估計算法[J]. 電子與信息學報, 2016, 38(11): 2745-2752. doi: 10.11999/JEIT160050
ZHAO Yongjun, ZHAO Yongsheng, ZHAO Chuang. Maximum Likelihood TDOA-FDOA Estimator Using Markov Chain Monte Carlo Sampling[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2745-2752. doi: 10.11999/JEIT160050
Citation: ZHAO Yongjun, ZHAO Yongsheng, ZHAO Chuang. Maximum Likelihood TDOA-FDOA Estimator Using Markov Chain Monte Carlo Sampling[J]. Journal of Electronics & Information Technology, 2016, 38(11): 2745-2752. doi: 10.11999/JEIT160050

基于馬爾科夫鍵蒙特卡洛抽樣的最大似然時差-頻差聯(lián)合估計算法

doi: 10.11999/JEIT160050
基金項目: 

國家自然科學基金(61401469, 41301481, 61501513)，國家高技術(shù)研究發(fā)展計劃(2012AA7031015)

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

Funds: 

The National Natural Science Foundation of China (61401469, 41301481, 61501513), The National High Technology Research and Development Program of China (2012AA7031015)

  • 摘要: 該文針對無源定位中參考信號真實值未知的時差-頻差聯(lián)合估計問題，構(gòu)建了一種新的時差-頻差最大似然估計模型，并采用馬爾科夫鏈蒙特卡洛(MCMC)方法求解似然函數(shù)的全局極大值，得到時差-頻差聯(lián)合估計。算法通過生成時差-頻差樣本，并統(tǒng)計樣本均值得到估計值，克服了傳統(tǒng)互模糊函數(shù)(CAF)算法只能得到時域和頻域采樣間隔整數(shù)倍估計值的問題，且不存在期望最大化(EM)等迭代算法的初值依賴和收斂問題。推導了時差-頻差聯(lián)合估計的克拉美羅界，并通過仿真實驗表明，算法在不同信噪比條件下的估計精度優(yōu)于CAF算法和EM算法，且計算復(fù)雜度較低。
    Abstract: 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.
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出版歷程
  • 收稿日期:  2016-01-13
  • 修回日期:  2016-06-08
  • 刊出日期:  2016-11-19

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