Integrated Monostatic and Bistatic mmWave Sensing

TL;DR

This paper explores the integration of monostatic and bistatic mmWave sensing modalities in 5G scenarios, using advanced filtering and fusion techniques to enhance sensing performance.

Contribution

It introduces a novel method for combining monostatic and bistatic sensing data using extended Kalman-Poisson multi-Bernoulli filters and periodic fusion.

Findings

Improved sensing accuracy through modality integration

Effective fusion of user states and maps from two sensing modalities

Enhanced performance in 5G mmWave sensing scenarios

Abstract

Millimeter-wave (mmWave) signals provide attractive opportunities for sensing due to their inherent geometrical connections to physical propagation channels. Two common modalities used in mmWave sensing are monostatic and bistatic sensing, which are usually considered separately. By integrating these two modalities, information can be shared between them, leading to improved sensing performance. In this paper, we investigate the integration of monostatic and bistatic sensing in a 5G mmWave scenario, implement the extended Kalman-Poisson multi-Bernoulli sequential filters to solve the sensing problems, and propose a method to periodically fuse user states and maps from two sensing modalities.