Robust direct position determination for chirp signal-based underwater acoustic sensor networks

In this study, we develop robust direct position determination (DPD) algorithms for chirp signal-based underwater acoustic sensor networks (UASNs) by harnessing parameterized time–frequency transform (PTFT) and the proposed frequency-aware clustering scheme, under low signal-to-noise ratio (SNR) and heterogeneous array scenarios. Matched kernel functions of PTFT are conceived based on the known parameters of chirp signals to aggregate signal energy in the time–frequency domain. The closed-form expression of the proposed PTFT-based array data model is derived and the feasibility of the PTFT-based array data model for a cluster-based partially coherent processing scheme is analyzed. To accommodate array heterogeneity, we conceive a frequency-aware clustering scheme to handle different operating frequencies among arrays. Upon exploiting maximum likelihood (ML) and multiple signal classification (MUSIC) frameworks, the cost functions of DPD are derived by combining the PTFT-based array data model with the frequency-aware clustering scheme. Simulation results validate that our proposed scheme can improve the accuracy and resolution of positioning at low SNRs in both homogeneous and heterogeneous UASNs. Furthermore, the SWellEx-96 experimental data is leveraged to characterize the effectiveness of our solutions under practical scenarios.