Active-Sonar Navigation Enhanced by Delay-compensated Sliding Window Without In-Situ Sound Speed Measurement

High-precision underwater active-sonar navigation system faces an ill-posed positioning problem in request-response localization mode, compounded by prohibitive costs and computational demands associated with in-situ sound speed profile (SSP) measurements. This study presents a novel constant sound speed (CSS)-based acoustic navigation model that is enhanced through sliding-window processing that incorporates Zenith Acoustic Delay (ZAD) compensation and depth constraints. This model eliminates the need for in-situ SSP acquisition while improving positioning accuracy by reducing systematical error influence. The core innovation jointly estimates ZAD parameter to compensate spatiotemporal sound-speed variations within a sliding window and utilizes a B-spline trajectory model to map two-way travel-time (TWTT) observations onto vehicle trajectories. Experimental validation, including simulations and field data from stations at approximately 1700 m and 2200 m depths, demonstrates consistent meter-level 3D positioning accuracy (RMSE: 1.04-1.75 m) across diverse sound speed conditions (in-situ, historical, and proposed empirical CSS). Furthermore, the proposed CSS-based model achieves approximately 100 times faster computation than traditional ray-tracing-based model by avoiding ray-inversion calculations. Crucially, ZAD compensation significantly reduces positioning error induced by sound-speed variations, delivering accuracy comparable to that of in-situ SSP-based models. Notably, depth constraints from pressure sensors (PS) resolve inherent correlations between vertical position and the ZAD parameter, reducing vertical error by 78%. Acoustic positioning residuals are superior to those of ray-tracing-based positioning. This breakthrough eliminates the fundamental requirement for in-situ sound speed measurements, establishing a versatile and efficient solution for next-generation compact, unmanned, and intelligent autonomous underwater vehicle (AUV) navigation. The model enables real-time, low-cost operation in resource-limited scenarios, including deep-sea exploration, emergency response, and swarm applications.