Resampling algorithms for particle filters: A computational complexity perspective
Resampling algorithms for particle filters: A computational complexity perspective
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DOI：
10.1155/s1110865704405149
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发表时间：
2004-11-01
期刊：
影响因子：
--
通讯作者：
Hong, SJ
中科院分区：
文献类型：
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作者：
Bolic, M;Djuric, PM;Hong, SJ
描述了最新发展的适合实时实现的粒子滤波重采样算法，并对其进行了分析。新算法通过减少运算次数和存储器访问等常见问题，降低了硬件和DSP实现的复杂性。此外，通过在时间上将重采样步骤与其他粒子滤波步骤重叠，该算法允许使用更高的采样频率。由于重采样不依赖于任何特定应用程序，因此该分析适用于使用重采样的所有类型的粒子过滤器。将该算法应用于无线通信中的纯方位跟踪和联合检测与估计，对算法的性能进行了评估。我们已经证明了所提出的算法在不降低性能的情况下降低了复杂度。
Newly developed resampling algorithms for particle filters suitable for real-time implementation are described and their analysis is presented. The new algorithms reduce the complexity of both hardware and DSP realization through addressing common issues such as decreasing the number of operations and memory access. Moreover, the algorithms allow for use of higher sampling frequencies by overlapping in time the resampling step with the other particle filtering steps. Since resampling is not dependent on any particular application, the analysis is appropriate for all types of particle filters that use resampling. The performance of the algorithms is evaluated on particle filters applied to bearings-only tracking and joint detection and estimation in wireless communications. We have demonstrated that the proposed algorithms reduce the complexity without performance degradation.