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Multiple vehicle signals separation based on particle filtering in wireless sensor network

机译:无线传感器网络中基于粒子滤波的多车辆信号分离

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摘要

A novel statistical method based on particle filtering is presented for multiple vehicle acoustic signals separation problem in wireless sensor network. The particle filtering method is able to deal with non-Gaussian and nonlinear models and non-stationary sources. Using some instantaneously mixed observations of several real-world vehicle acoustic signals, the proposed statistical method is compared with a conventional non-stationary Blind Source Separation algorithm and attractive simulation results are achieved. Moreover, considering the natural convenience to transmit particles between sensor nodes, the algorithm based on particle filtering is believed to have potential to enable the task of multiple vehicles recognition collaboratively performed by sensor nodes in distributed wireless sensor network.
机译:针对无线传感器网络中多个车辆声信号分离问题,提出了一种基于粒子滤波的统计方法。粒子滤波方法能够处理非高斯和非线性模型以及非平稳源。利用对几种现实世界车辆声信号的瞬时混合观测,将所提出的统计方法与常规的非平稳盲源分离算法进行比较,并获得了令人满意的仿真结果。此外,考虑到在传感器节点之间传输粒子的自然便利性,基于粒子滤波的算法被认为具有实现由分布式无线传感器网络中的传感器节点协同执行的多个车辆识别任务的潜力。

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