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In this paper, we assess the capability of underwater hydrophone (UH) arrays to locate and track manoeuvring targets. A UH array is a horizontal line array of omnidirectional pressure sensors that is deployed on the seabed. The measurements at each UH array are then affected by two idiosyncrasies, termed "source direction ambiguity" and "coning error". In this paper, the posterior Cramer-Rao lower bound(PCRLB) is used as the measure of system performance, providing a bound on the optimal achievable accuracy of target state estimation. We demonstrate the impact of the measurement idiosyncrasies on the PCRLB, with the bound shown to be greater (poorer performance) than when using standard bearings-only sensors. We also include clutter (i.e. we allow each measurement to be either target generated or a false positive), as well as both state-dependent measurement errors and a state-dependent probability of detection. Building on previous work, we show that the measurement origin uncertainty can again be expressed as an information reduction factor (IRF), with this IRF now shown to be a function of both the target range and orientation in relation to each UH array. We consider simulated scenarios that contain features characteristic of recent sea trials conducted by QinetiQ Ltd. The two key features of the trial scenarios is that we have very sparse prior knowledge, and each target has the potential to perform a series of manoeuvres. We use a recent PCRLB formulation for tracking manoeuvring targets that approximates the potentially multi-modal target distribution using a best-fitting Gaussian distribution. We present simulation results for multi-sensor scenarios, demonstrating that this is indeed a difficult tracking problem. Tracking is particularly difficult when the target crosses the line of the UH arrays, making triangulation difficult; and when the target is in the "end-fire" of at least one UH array. It is also difficult to detect and triangulate distant targets. Future work will investigate the tightness of the PCRLB when compared with the performance of state-of-the-art tracking algorithms.
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