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In this paper, we propose using distributed diffusion adaptive networks for acoustic signature identification, as a timevarying autoregressive (TVAR) stochastic model. A distributed adaptive sensor network considers spatio-temporal challenges simultaneously. To analyze diffusion networks under TVAR modeling problem circumstances, we investigate and elaborate on their performance under non-stationary conditions. Different versions of diffusion networks are then theoretically compared under the problem conditions. Furthermore, their superiority to single point observations is shown. Finally, the proposed algorithms are implemented on a raw and real sensor network dataset recorded from moving vehicles. The experimental results well support the theoretical findings, and demonstrate the excellence and efficacy of distributed diffusion adaptive networks for this case.

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