ML estimation of transition probabilities for an unknown maneuvering emitter tracking
ML estimation of transition probabilities for an unknown maneuvering emitter tracking
复制标题
未知机动发射器跟踪的转移概率的 ML 估计
DOI：
10.1016/j.sigpro.2014.11.004
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发表时间：
2015
影响因子：
4.4
通讯作者：
Ge Quanbo
中科院分区：
文献类型：
--
作者：
Luo Xiaomei;Jiu Bo;Chen Shaodong;Ge Quanbo
研究了利用到达时间差(TDOA)和到达频差(FDOA)测量的无线传感器网络跟踪未知机动辐射源的问题。针对机动辐射源跟踪中的参数更新问题，提出了多模型交互结合带相关噪声的平方根立方卡尔曼滤波(IMM-SCKF-CN)算法。该跟踪框架的关键是马尔可夫转移概率矩阵(TPM)，它控制机动目标的多个动态运动模型之间的跳跃。然而，在实践中，TPM是未知的，必须进行估计。本文考虑了TPM的最大似然估计，提出了一种基于改进的加权解析中心割平面法的递推算法。与一些批量最大似然估计方法相比，所得到的递推最大似然估计方法具有更低的每样本复杂度。仿真结果表明了该方法的有效性，大大提高了跟踪性能。
We consider the problem of an unknown maneuvering emitter tracking by a wireless sensor network with time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements. Interacting multiple models combined with square-root cubature Kalman filter with correlated noises (IMM-SCKF-CN) is proposed to update the parameters in the maneuvering emitter tracking. Essential to this tracking framework is the Markov transition probability matrix (TPM) which governs the jumps between multiple dynamic motion models for the maneuvering target. However, in practice, the TPM is unknown and has to be estimated. In this paper, we consider the maximum likelihood (ML) estimation of the TPM and propose a recursive algorithm based on theimproved weightedanalytical center cutting plane method (ACCPM). Compared with some batch ML methods, the resulting recursive ML estimation method has a much lower per sample complexity. Simulation results show the efficacy of the proposed method with greatly improved tracking performance.