粒子滤波在辐射源检测中的收敛结果
Particle Filtering Convergence Results for Radiation Source Detection
摘要：
最近的研究表明，在一定的假设下，粒子滤波方法的弱收敛性-分布收敛。然而，粒子滤波方法的一些应用，如辐射源定位问题，可以被证明具有以下意义上的扩展收敛。使用统计独立的测量和测量过程中，不依赖于状态空间的假设，我们证明了收敛的后验和粒子滤波算法的近似真实狄拉克分布特征的源位置。我们设计了一个采样重要性呼吸（SIR）过滤器检测和定位辐射源使用的传感器网络。为了数值评估这种粒子滤波器的有效性，我们采用它来解决源定位问题，从智能辐射传感器系统（IRSS）的测试和数值模拟的城市域数据，使用射线跟踪模型的实验开放领域的数据集。然后，我们证明了状态后验概率密度函数的估计的理论收敛性。
英文摘要：
Recent research has shown a weak convergence - convergence in distribution - of particle filtering methods under certain assumptions. However, some applications of particle filtering methods, such as radiation source localization problems, can be shown to have an extended convergence in the following sense. Using the assumptions of statistically independent measurements and a measurement process that does not depend on the state space, we prove the convergence of the posterior and its approximation by the particle filtering algorithm to the true Dirac distribution characterizing the source location. We design a Sampling Importance Resampling (SIR) filter to detect and locate a radiation source using a network of sensors. To numerically assess the effectiveness of this particle filter we employ it to solve a source localization problem using both experimental open field data sets from Intelligent Radiation Sensor Systems (IRSS) tests and numerically simulated urban domain data using a ray-tracing model. We then prove the theoretical convergence of the estimate to the state posterior probability density function.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
