Passive target localisation in the undersea domain is traditionally constrained by observability limitations, often requiring ownship manoeuvres to resolve range ambiguity from bearing-only measurements. These manoeuvres can be tactically undesirable, increase platform detectability and delay actionable track formation. We propose a bearing-only tracking algorithm that enables reliable target localisation without the reliance on aggressive or pre-planned manoeuvres.

The approach leverages a probabilistic estimation framework operating on sequential bearing measurements, combined with robust data association and track management, to infer target state over time. Rather than requiring significant geometric diversity from ownship motion, the method exploits statistical consistency within the measurement stream and incorporates mechanisms to manage uncertainty, clutter, and false detections. The algorithm operates as a modular component within a deployable system and has been designed with real-time constraints and operational integration in mind, allowing seamless use within existing sonar processing chains.

The capability is demonstrated using real sensor data collected in "representative realistic maritime environments". Results show that the algorithm is able to achieve stable and convergent target localisation under limited manoeuvring conditions, maintaining track continuity and producing meaningful position estimates significantly earlier than conventional techniques. Performance is assessed in terms of convergence behaviour, robustness to measurement noise and clutter, and overall track quality. The results highlight the ability to extract actionable spatial information from bearing-only data in scenarios which would challenge traditional methods or require extended ownship manoeuvring periods.

The implications of this work are substantial for operational effectiveness. Reducing or removing the need for exaggerated manoeuvres enables more covert operation, lowers operator workload, and improves responsiveness in dynamic environments. This capability supports a shift towards more autonomous and tactically flexible systems, where reliable localisation can be achieved passively and with minimal disruption to mission profiles.