Multi-Base Station Cooperative Sensing with AI-Aided Tracking
- URL: http://arxiv.org/abs/2310.20403v1
- Date: Tue, 31 Oct 2023 12:27:48 GMT
- Title: Multi-Base Station Cooperative Sensing with AI-Aided Tracking
- Authors: Elia Favarelli, Elisabetta Matricardi, Lorenzo Pucci, Enrico Paolini,
Wen Xu, Andrea Giorgetti
- Abstract summary: We investigate a joint sensing and communication network consisting of multiple base stations (BSs) that cooperate through a fusion center (FC)
We show that our framework could provide remarkable sensing performance, achieving an optimal sub-pattern assignment (OSPA) less than 60 cm.
- Score: 10.400855135405251
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this work, we investigate the performance of a joint sensing and
communication (JSC) network consisting of multiple base stations (BSs) that
cooperate through a fusion center (FC) to exchange information about the sensed
environment while concurrently establishing communication links with a set of
user equipments (UEs). Each BS within the network operates as a monostatic
radar system, enabling comprehensive scanning of the monitored area and
generating range-angle maps that provide information regarding the position of
a group of heterogeneous objects. The acquired maps are subsequently fused in
the FC. Then, a convolutional neural network (CNN) is employed to infer the
category of the targets, e.g., pedestrians or vehicles, and such information is
exploited by an adaptive clustering algorithm to group the detections
originating from the same target more effectively. Finally, two multi-target
tracking algorithms, the probability hypothesis density (PHD) filter and
multi-Bernoulli mixture (MBM) filter, are applied to estimate the state of the
targets. Numerical results demonstrated that our framework could provide
remarkable sensing performance, achieving an optimal sub-pattern assignment
(OSPA) less than 60 cm, while keeping communication services to UEs with a
reduction of the communication capacity in the order of 10% to 20%. The impact
of the number of BSs engaged in sensing is also examined, and we show that in
the specific case study, 3 BSs ensure a localization error below 1 m.
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