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Underwater target enhanced localization using distributed estimation and search optimization
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AI
DOI：10.1088/1361-6501/ae10cb.png)
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Abstract
En 中文
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.
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