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arXiv 2409.20552eess.SP
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基于直接多径的同步定位与建图

Direct Multipath-Based SLAM

Mingchao Liang, Erik Leitinger, Florian Meyer

AI总结:

本文提出一种直接以接收无线电信号为输入的基于多径的SLAM方法,通过因子图和置信传播避免信道估计预处理,在SISO场景下相比传统方法显著提升了定位与建图精度。

摘要:

在未来的无线网络中,移动智能体位置和传播环境信息的可用性能够催生新的服务,并提升通信的吞吐量和鲁棒性。基于多径的同步定位与建图(SLAM)旨在通过利用接收无线电信号中局部几何结构与多径分量(MPCs)之间的关系,估计智能体的位置以及环境中的反射特征。现有的基于多径的SLAM方法使用信道估计器对接收无线电信号进行预处理。信道估计器通过为每个MPC提取一组色散参数来降低数据速率,这些参数随后被用作SLAM的测量值。较低的数据速率有利于基于多径SLAM的贝叶斯估计。然而,由于信号带宽限制了有限的分辨能力,信道估计容易出错,MPC参数可能被错误提取,从而导致SLAM性能下降。我们提出了一种直接使用接收无线电信号作为输入的基于多径的SLAM方法。我们引入了一种新的统计模型,该模型可以有效地用因子图表示。该因子图是开发一种高效的置信传播(BP)方法的起点,该方法用于基于多径的SLAM,避免了信道估计器的数据预处理。基于具有挑战性的单输入单输出(SISO)场景中的合成数据和真实数据的数值结果表明,所提出的方法在定位和建图精度方面优于传统方法。

英文摘要:

In future wireless networks, the availability of information on the position of mobile agents and the propagation environment can enable new services and increase the throughput and robustness of communications. Multipath-based simultaneous localization and mapping (SLAM) aims at estimating the position of agents and reflecting features in the environment by exploiting the relationship between the local geometry and multipath components (MPCs) in received radio signals. Existing multipath-based SLAM methods preprocess received radio signals using a channel estimator. The channel estimator lowers the data rate by extracting a set of dispersion parameters for each MPC. These parameters are then used as measurements for SLAM. Bayesian estimation for multipath-based SLAM is facilitated by the lower data rate. However, due to finite resolution capabilities limited by signal bandwidth, channel estimation is prone to errors and MPC parameters may be extracted incorrectly and lead to a reduced SLAM performance. We propose a multipath-based SLAM approach that directly uses received radio signals as inputs. A new statistical model that can effectively be represented by a factor graph is introduced. The factor graph is the starting point for the development of an efficient belief propagation (BP) method for multipath-based SLAM that avoids data preprocessing by a channel estimator. Numerical results based on synthetic and real data in challenging single-input, single-output (SISO) scenarios demonstrate that the proposed method outperforms conventional methods in terms of localization and mapping accuracy.

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。

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