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首页> 外文会议>Bayesian Inference and Maximum Entropy Methods in Science and Engineering >Sequential MCMC for Spatial Signal Separation and Restoration From An Array of Sensors
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Sequential MCMC for Spatial Signal Separation and Restoration From An Array of Sensors

机译：用于从一系列传感器中分离和恢复空间信号的顺序MCMC

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摘要

This paper addresses the implementation of sequential Markov Chain Monte Carlo (MCMC) estimation, also known as particle filtering, to signal separation and restoration problems, using a passive array of sensors. This proposed method offers significant advantages: 1) the signals mixed at the array can be well-separated in space and restored in an online fashion, 2) the assumption of a stationary environment over the interval can be relaxed, 3) the estimated joint posterior distribution of all the unknown parameters can be used for statistical inference, and 4) the method can also be used to dynamically detect the number of signals throughout the observation period. The signals used in the simulation were mixed by a highly-nonlinear but structured steering-vector matrix. Simulation results demonstrated the effectiveness of the method in such a way that the true and restored signals were clearly separated and restored by the sequential MCMC method.
机译：本文介绍了使用无源传感器阵列实现顺序马尔可夫链蒙特卡罗（MCMC）估计（也称为粒子滤波）的实现，以解决信号分离和恢复问题。该方法具有显着的优点：1）在阵列上混合的信号可以在空间上很好地分离并以在线方式恢复； 2）可以放松整个区间的固定环境； 3）估计后关节所有未知参数的分布可用于统计推断，并且4）该方法还可用于在整个观察周期内动态检测信号数量。仿真中使用的信号由高度非线性但结构化的转向矢量矩阵混合。仿真结果证明了该方法的有效性，通过顺序MCMC方法可以清楚地分离和还原真实信号和还原信号。

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