Contact-Free Multi-Target Tracking Using Distributed Massive MIMO-OFDM Communication System: Prototype and Analysis

TL;DR

This paper presents a contact-free multi-target tracking system using a distributed massive MIMO-OFDM communication setup, combining CSI calibration, Bayesian compressive sensing, and probabilistic filtering for real-time multi-person tracking with high accuracy.

Contribution

The paper introduces a novel contact-free multi-target tracking method leveraging distributed massive MIMO-OFDM systems, with a new CBCS algorithm and real-time tracking capabilities.

Findings

Achieved 12.7 cm accuracy for single-person tracking

Achieved 45.7 cm accuracy for multi-person tracking

Demonstrated real-time multi-target tracking in practical scenarios

Abstract

Wireless-based human activity recognition has become an essential technology that enables contact-free human-machine and human-environment interactions. In this paper, we consider contact-free multi-target tracking (MTT) based on available communication systems. A radar-like prototype is built upon a sub-6 GHz distributed massive multiple-input and multiple-output (MIMO) orthogonal frequency-division multiplexing communication system. Specifically, the raw channel state information (CSI) is calibrated in the frequency and antenna domain before being used for tracking. Then the targeted CSIs reflected or scattered from the moving pedestrians are extracted. To evade the complex association problem of distributed massive MIMO-based MTT, we propose to use a complex Bayesian compressive sensing (CBCS) algorithm to estimate the targets' locations based on the extracted target-of-interest CSI signal directly. The estimated locations from CBCS are fed to a Gaussian mixture probability hypothesis density filter for tracking. A multi-pedestrian tracking experiment is conducted in a room with size of 6.5 m$\times$10 m to evaluate the performance of the proposed algorithm. According to experimental results, we achieve 75th and 95th percentile accuracy of 12.7 cm and 18.2 cm for single-person tracking and 28.9 cm and 45.7 cm for multi-person tracking, respectively. Furthermore, the proposed algorithm achieves the tracking purposes in real-time, which is promising for practical MTT use cases.