Millimeter wave (mmWave) signals are useful for simultaneous localization and mapping (SLAM), due to their inherent geometric connection to the propagation environment and the propagation channel. To solve the SLAM problem, existing approaches rely on sigma-point or particle-based approximations, leading to high computational complexity, precluding real-time execution. We propose a novel low-complexity SLAM filter, based on the Poisson multi-Bernoulli mixture (PMBM) filter. It utilizes the extended Kalman (EK) first-order Taylor series based Gaussian approximation of the filtering distribution, and applies the track-oriented marginal multi-Bernoulli/Poisson (TOMB/P) algorithm to approximate the resulting PMBM as a Poisson multi-Bernoulli (PMB). The filter can account for different landmark types in radio SLAM and multiple data association hypotheses. Hence, it has an adjustable complexity/performance trade-off. Simulation results show that the developed SLAM filter can greatly reduce the computational cost, while it keeps the good performance of mapping and user state estimation.

中文翻译：

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用于双基地毫米波无线电 SLAM 的计算高效 EK-PMBM 滤波器


毫米波 (mmWave) 信号由于其与传播环境和传播通道固有的几何联系，可用于同步定位和地图绘制 (SLAM)。为了解决 SLAM 问题，现有方法依赖于西格玛点或基于粒子的近似，导致计算复杂度较高，妨碍实时执行。我们提出了一种基于泊松多伯努利混合 (PMBM) 滤波器的新型低复杂度 SLAM 滤波器。它利用基于扩展卡尔曼（EK）一阶泰勒级数的滤波分布高斯近似，并应用面向轨迹的边缘多伯努利/泊松（TOMB/P）算法将所得PMBM近似为泊松多伯努利（项目管理委员会）。该过滤器可以考虑无线电 SLAM 中的不同地标类型和多个数据关联假设。因此，它具有可调整的复杂性/性能权衡。仿真结果表明，所开发的SLAM滤波器可以大大降低计算成本，同时保持良好的建图和用户状态估计性能。