Object Trajectory Estimation with Multi-Band Wi-Fi Neural Dynamic Fusion

Sorachi Kato, Pu Wang, Toshiaki Koike-Akino, Takuya Fujihashi, Hassan Mansour, Petros Boufounos

Abstract: In contrast to existing multi-band Wi-Fi fusion in a frame-to-frame basis for simple classification, this paper considers asynchronous sequence-to-sequence fusion between sub-7GHz channel state information (CSI) and 60GHz beam SNR for more challenging downstream tasks such as continuous regression. To handle the timing disparity between the two channel measurements, we extend our recently proposed dual-decoder neural dynamic (DDND) framework with latent ordinary differential equations (ODEs), align the distinct latent dynamic states at the same time instances, and introduce a post-ODE fusion framework. The resulting neural dynamic fusion (NDF) framework is trained in an end-to-end fashion with a modified variational autoencoder loss function. Evaluation over a newly collected in-house multi-band Wi-Fi dataset shows the advantage of the proposed NDF method over frame-based and DDND methods.