Adaptive Data Fusion for Wireless Localization in Harsh Environments
Adaptive Data Fusion for Wireless Localization in Harsh Environments
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DOI：
10.1109/tsp.2012.2183126
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发表时间：
2012-04
影响因子：
5.4
通讯作者：
J. Prieto;S. Mazuelas;A. Bahillo;P. Fernández;R. Lorenzo;E. Abril
中科院分区：
文献类型：
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作者：
J. Prieto;S. Mazuelas;A. Bahillo;P. Fernández;R. Lorenzo;E. Abril
无线信道在恶劣环境中的动态和不可预测特性导致了定位系统的性能不佳。传统的实现依赖于由可处理性要求驱动的不切实际的假设，例如线性模型或高斯误差。在本文中，我们提出了一种基于确定似然函数的定位系统数据融合框架，该似然函数表示测量与距离之间的关系。在该框架中，这样的可能性被动态地适应于传播条件。粒子滤波(PF)的后续使用导致了自适应似然粒子(ALPA)滤波，其解决了测量随时间的非线性和非高斯行为。ALPA滤波器的性能通过使用无线局域网(WLAN)设备收集的接收信号强度(RSS)和到达时间(TOA)测量来量化。我们将所获得的精度与传统实现的精度以及后验Cramér-Rao下界(PCRLB)进行了比较。实验和仿真结果表明，所提出的ALPA滤波器显著改善了传统方法的精度，获得了接近PCRLB值的误差。
The dynamic and unpredictable characteristics of wireless channels in harsh environments have resulted in a poor performance of localization systems. Conventional implementations rely on unrealistic assumptions driven by tractability requirements, such as linear models or Gaussian errors. In this paper, we present a framework for data fusion in localization systems based on determining likelihood functions that represent the relationship between measurements and distances. In this framework, such likelihoods are dynamically adapted to the propagation conditions. The subsequent usage of a particle filter (PF) leads to an adaptive likelihood particle (ALPA) filter that addresses the nonlinear and non-Gaussian behavior of measurements over time. The ALPA filter's performance is quantified by using received-signal-strength (RSS) and time-of-arrival (TOA) measurements collected with wireless local area network (WLAN) devices. We compare the accuracy obtained to the accuracy of conventional implementations and to the posterior Cramér-Rao lower bound (PCRLB). Both empirical and simulation results show that the proposed ALPA filter significantly improves the accuracy of conventional approaches, obtaining an error close to the PCRLB.