基于 Dirichlet 过程的无线电同步定位与地图构建方法
Dirichlet process approach for radio-based simultaneous localization and mapping
AI总结：
本文提出基于 Dirichlet 过程的 5G mmWave 无线电 SLAM 方法，通过出生点聚类识别反射体并适应虚警与信号数变化，以轻微性能损失换取显著计算复杂度改善。
摘要：
由于 5G 毫米波（mmWave）的发展，空间信道参数正变得具有高度可分辨性，从而能够实现精确的车辆定位与地图构建。我们提出一种利用 Dirichlet 过程（DP）的无线电同步定位与地图构建（SLAM）新方法。DP 既能够估计簇的数量，也能够进行聚类；当此类 5G 信号被各种物体反射并被接收时，它可以通过对信号进行分类来识别反射体的位置。我们利用车辆接收到的 5G mmWave 信号测量值生成出生点，并通过对随时间生成的出生点进行聚类来对物体分类。每次使用 DP 聚类方法时，即使在测量中存在虚警且接收信号的基数发生变化的挑战性情况下，我们也能够绘制环境中的地标。仿真结果证明了所提方案的性能。通过将结果与基于 Rao-Blackwellized 概率假设密度滤波器的 SLAM 进行比较，我们确认 SLAM 性能略有下降，但由此验证了该方法在计算复杂度方面具有显著收益。
英文摘要：
Due to 5G millimeter wave (mmWave), spatial channel parameters are becoming highly resolvable, enabling accurate vehicle localization and mapping. We propose a novel method of radio simultaneous localization and mapping (SLAM) with the Dirichlet process (DP). The DP, which can estimate the number of clusters as well as clustering, is capable of identifying the locations of reflectors by classifying signals when such 5G signals are reflected and received from various objects. We generate birth points using the measurements from 5G mmWave signals received by the vehicle and classify objects by clustering birth points generated over time. Each time we use the DP clustering method, we can map landmarks in the environment in challenging situations where false alarms exist in the measurements and change the cardinality of received signals. Simulation results demonstrate the performance of the proposed scheme. By comparing the results with the SLAM based on the Rao-Blackwellized probability hypothesis density filter, we confirm a slight drop in SLAM performance, but as a result, we validate that it has a significant gain in computational complexity.
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