Distributed acoustic signature identification using incremental adaptive networks

Abstract

Considerable amount of information about objects is obtained from their acoustic signature. Since the acoustic signal is physically propagated over a geographical region, any fixed point observer records data with a limited quality. Moving sources also decreases the generality of single point modeling. In this paper, we propose employing distributed incremental adaptive networks for the aim of acoustic signature identification, as a time-varying autoregressive (TVAR) stochastic process. The distributed adaptive sensor network considers spatial and temporal challenges simultaneously and provides real-time estimations. By formulating the problem under non-stationary conditions, we proceed to show the superiority of the proposed incremental adaptive algorithm compared to the classical single point observations methods. To practically prove this merit, the proposed algorithms were implemented on and evaluated using a real sensor network dataset recorded from moving vehicles for the first time in the adaptive networks field; this is a substantial real-world validation test. The experimental results well support the claim and demonstrate the excellence and competence of distributed incremental adaptive networks for this case.