Underwater target enhanced localization using distributed estimation and search optimization

Abstract Considering the limited availability of usable signal sources and the instability of sensor measurements in underwater environments, this paper proposes a novel underwater target enhanced localization using distributed estimation and search optimization. First, a dual mapping from the signal domain to the position domain is established using time-of-arrival measurements between acoustic base stations and underwater beacons. Second, a singular value decomposition-enhanced unscented Kalman filter is introduced to improve the stability of the covariance matrix. Combined with a strong tracking mechanism, the filter gain is adaptively adjusted to perform a first-stage filtering estimation. Then, the underwater target localization optimization is transformed into an unconstrained problem, where a cascade quasi-Newton optimization model is constructed to mitigate the accumulation of errors and suppress abnormal positioning outputs. Compared with the improved two-step weighted least squares, the square-root unscented Kalman filter, and the successive constrained optimization modified polar representation, the proposed method demonstrates superior localization accuracy in both multivariable simulations and lake-based field experiments. The consistency between simulation and experimental results confirms the effectiveness of the proposed method in suppressing the impact of uncertain measurements.