Sensor Fusion and Resource Management in MIMO-OFDM Joint Sensing and Communication
Elia Favarelli, Elisabetta Matricardi, Lorenzo Pucci, Wen Xu, Enrico, Paolini, and Andrea Giorgetti

TL;DR
This paper presents an integrated MIMO-OFDM system that combines sensing and communication through innovative data fusion, resource management, and tracking algorithms, enhancing target detection and network efficiency in urban scenarios.
Contribution
It introduces a novel data fusion technique, a resource management framework, and a comprehensive channel model for joint sensing and communication in MIMO-OFDM networks.
Findings
Improved target localization accuracy using the proposed fusion and tracking methods.
Enhanced system performance with optimized resource allocation strategies.
Effective management of network overhead through excision filtering.
Abstract
This study explores the promising potential of integrating sensing capabilities into multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM)-based networks through innovative multi-sensor fusion techniques, tracking algorithms, and resource management. A novel data fusion technique is proposed within the MIMO-OFDM system, which promotes cooperative sensing among monostatic joint sensing and communication (JSC) base stations by sharing range-angle maps with a central fusion center. To manage data sharing and control network overhead introduced by cooperation, an excision filter is introduced at each base station. After data fusion, the framework employs a three-step clustering procedure combined with a tracking algorithm to effectively handle point-like and extended targets. Delving into the sensing/communication trade-off, resources such as transmit…
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Taxonomy
TopicsIndoor and Outdoor Localization Technologies · Distributed Sensor Networks and Detection Algorithms
MethodsBalanced Selection
