双基地SLAM mmWave无线电中计算效率高的EK-PMBM滤波器
A Computationally Efficient EK-PMBM Filter for Bistatic mmWave Radio SLAM
摘要：
毫米波（mmWave）信号由于其与传播环境和传播信道的固有几何连接而可用于同时定位和映射（SLAM）。为了解决SLAM问题，现有的方法依赖于基于西格玛点或粒子的近似，导致高计算复杂度，妨碍实时执行。基于泊松多伯努利混合滤波器（Poisson multi-Bernoulli mixture，PMBM），提出了一种新的低复杂度SLAM滤波器。它利用扩展卡尔曼（EK）的一阶泰勒级数为基础的高斯近似的滤波分布，并应用面向跟踪的边缘多伯努利/泊松（TOMB/P）算法来近似产生的PMBM作为一个泊松多伯努利（PMB）。滤波器可以考虑无线电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，改进的SLAM滤波器在保持良好的映射性能和用户状态估计性能的同时，大大降低了计算量。
英文摘要：
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.
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