Maximum likelihood matched field source range and depth localization with a single acoustic vector sensor

Motivated by the problem of localizing a source in range and depth in an ocean waveguide using a single acoustic vector sensor with pressure, horizontal, and vertical particle velocity channels, we derive and examine the optimum maximum likelihood-based matched field processors when the signal waveform emitted by the source is unknown. In our derivations, the ocean waveguide parameters are assumed to be known so that pressure and particle velocity replicas can be calculated and that the ambient noise is Gaussian distributed. Two different signal cases are considered: (1) the signal emitted by the source is unknown but deterministic and (2) the source signal is a Gaussian stationary random process with an unknown power spectrum. These two assumptions lead to estimator architectures with totally different invariance properties to signal and noise levels that we will discuss. We also present some propagation-model-based simulation examples that are compared to the Cramer–Rao lower bounds.